Molecular Insights into AG73@Syndecan-4 Interactions: Implications for Cancer Cell Adhesion, Migration, and Therapeutic Potential

Molecular Insights into AG73@Syndecan-4 Interactions: Implications for Cancer Cell Adhesion, Migration, and Therapeutic Potential | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Molecular Insights into AG73@Syndecan-4 Interactions: Implications for Cancer Cell Adhesion, Migration, and Therapeutic Potential Francisco das Chagas Pereira de Andrade, Anderson Nogueira Mendes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6090672/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Syndecan-4 is widely expressed across tissues and is particularly relevant in cancer, where it modulates cell adhesion, invasion, and metastasis. The AG73 peptide is known to interact with syndecans, influencing cell adhesion and migration. In this study, we employed molecular docking and dynamics simulations to investigate the interaction between AG73 and Syndecan-4 cytoplasmic domain. Our findings suggest that AG73 binds to Syndecan-4 through hydrogen bonding and electrostatic interactions, stabilizing its structure and potentially enhancing PKCα activation. Pharmacokinetic and ADMET predictions indicate that AG73 has poor oral bioavailability but could be optimized through alternative delivery strategies. These insights contribute to a better understanding of Syndecan-4-mediated signaling in cancer and highlight AG73 as a potential modulator for therapeutic applications. Laminin-111 derivated AG73 peptide Syndecan 4 Cell Adhesion Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction There are four syndecans identified in mammals those are membrane proteoglycans that play major roles in regulating cell behavior, cell signaling, and cellmatrix interactions [ 1 ]. Syndecans and Integrins mediate cell adhesion to extracellular matrix and their synergistic cooperation is implicated in cell adhesion processes [ 2 – 4 ]. Syndecans structure prensents a heparan-sulfate-modified extracellular domain that regulates various biological processes, a single transmenbrane domain, apart from a short C-terminal cytoplasmic domain mediated by numerous interactions [ 5 ]. Syndecans help regulate cell proliferation and migration, angiogenesis, cell/cell and cell/ECM adhesion, and they may participate in several key tumorigenesis processes regulating tumor cell proliferation, adhesion, motility [ 6 ]. The cytoplasmic domain is likely to be involved in these events through recruitment of mediators to effect activation of a variety of intracellular signaling cascades [ 7 , 8 ]. Syndecan 1 is the major syndecan of epithelia and can function as a cell-matrix receptor binding various matrix protein like collagen, fibronectin, tenascin, and in addition can bind members of the FGF family [ 9 , 10 ]. While Syndecan 2 is expressed in mesenchymal cells and syndecan 3 is highly expressed in neuron cells and muscle stem cells [ 11 ]. Syndecan 4 is present widespread tissue distribution [ 10 , 12 ]; and mediates numerous cellular processes through signaling pathways that affect cellular proliferation, migration, mechanotransduction and endocytosis present a crucial role in cell adhesion [ 13 , 14 ]. The protein structure of syndecans enables it to play multiple roles, both as a component of the ECM and as a cell adhesion receptor that acts through interactions with numerous matrix components in several biological processes [ 15 ]. However, syndecans lack any intrinsic signaling capacity, the interactions of their cytoplasmic domains with various adaptor proteins including the postsynaptic density protein, disc large, and the zonula occludens (PDZ), [ 16 ]. Syndecan 4 cytodomain V region is also activate by 4,5-bisphosphate phosphatidylinositol (PIP2) and protein kinase Cα (PKCα) downstream signaling pathways and playing essential regulatory roles in cell adhesion process [ 6 , 17 , 18 ]. The V region of the cytoplasmic domain of Syndecan 4 binds to PIP2 to activaty PKCα forming a tetramer with 2 PIP2 molecules to the catalytic subunit of PKCα and resulting in the activation complex regulated by the phosphorylation of the cytoplasmic Ser179 (human Ser179, rat Ser183) of Syndecan 4 [ 19 ]. The AG73 peptide (RKRLQVQLSIRT) is mouse laminin-1 α-chain derivated (2719–2730), [ 20 ]. This peptide sequence binds to cell surface proteoglycans, including syndecan-1, -2 and − 4. These receptors play critical regulatory roles in a variety of physiological and pathophysiological functions, including wound healing, inflammation, neural patterning, tumor growth and angiogenesis [ 21 ]. AG73 plays an important role in cell adhesion and has previously been linked to migration, invasion, and metastasis. Puchalapalli et al demonstrated the intrinsic interaction between AG73 and Syndecans on breast cancer cells in supporting tumor cell adhesion and invasion through filopodia [ 22 ]. The syndecan-4-AG73 interaction can influence cell migration behavior and modulates cell adhesion, cells migration and invasion. In this study we employed Molecular Docking and Molecular Dynamics to investigate the probability of activating the PKCα activity by interaction with the syndecan 4 cytoplasmic domain by AG73 interaction with syndecan 4 V region in cytoplamic domain, looking for bindin modes, binding energy and conformational changes. 2 Methods 2.1 Evaluation of AG73 of in silico ADMET properties. The Administration, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties predictions were evaluated in silico analysis employing four online databases: pkCMS [ 23 ], SwissADME [ 24 ], ADMETlab 2.0[ 25 ] and ADMETSAR 2.0 [ 26 , 27 ]. The Physicochemical and pharmacokinetic properties predictors were used considering literature to validation [ 28 ]. The results obtained with each predictor were compared with the others. When necessary, the measurement units were transformed to gain greater confidence. The AG73 peptide chemical structure (Fig. 1 ) was extracted from the 2jd4 crystal structure ( https://www.rcsb.org/structure/2jd4 ). [Insert Fig. 1 here] 2.2 Molecular Docking of AG73 peptide with protein receptors. Protein–peptide interactions were predicted using molecular docking. Protein–peptide docking using HPEPDOCK a web hierarchical flexible-peptide docking protocol that integrates MODPEP [ 29 ] program for peptide conformational sampling [ 30 ], was used to analyze the protein-peptide interactions and binding interface with syndecan 2. Redocking by blind docking was performed in HPEPDOCK and the poses were compared to the validation of the initial Molecular Docking. The coordinate file of the protein structures was obtained using the experimental coordinates from the Protein Data Bank and UniProt. The missing residues in the protein structures were searched through sequence analysis tools framework EMBOSS Needle and fixed by software Modeller10.4. The files were saved in the PDB format (Table 1 ). Peptide PDB files were also prepared for the ligand initially prepared in Discovery Studio 2020, always maintaining the flexibility of the rotating bonds. For the submission of docking in HPEPDOCK, the structures were protonated through the server https://server.poissonboltzmann.org/apbs by the PARSE method. Table 1 AG73 PBD structure Macromolecule Code Method Resolution Syndecan 4 1EJP SOLUTION NMR 2.90 Å [Insert Table 1 here] To visualize the molecular docking results, identify interacting residues, elucidate extensive electrostatic interactions, and van der Waals interactions, and demonstrate the abundance of polar amino acid residues it was employed PyMol 2.1.1 and Discovery 2021 Studio software. The PyMol 2.1.1 software was also used to find the coordinates for each complex formed and to visualize the crystallographic location of the ligand to compare the conformation energies. To obtain the 2D and 3D images of the interactions between the ligand and the receptor, we used the softwares Discovery 2021 Studio and SAMSON software respectively. 2.3 Inhibition constant theoretical calculation The inhibition constant (Ki) was obtained from the binding energy (ΔG) using the expression Ki = exp (ΔG/RT) [ 31 ], where R is the universal gas constant (1.985 × 10 − 3 kcal.mol − 1 .K − 1 ) and T is the temperature (310.15 K). 2.4 Molecular Dynamics Molecular Dynamics simulations studies it was conducted to find and comprehend protein-peptide interactions [ 32 ], and investigat whether the AG73 peptide interacts with the Syndecan 4 intracellular domain in V region involved in PKCα activation, which are thought to be linked to the actin cytoskeleton via additional protein-protein interactions, mediating cell adhesion [ 13 , 33 , 34 ]. In this study, we suggest that AG73 adopts a β-sheet conformation like in 2jd4.pdb [ 35 ]. The PDB structure from which the peptide sequence was extracted. The better docking scores poses of the protein-peptide complexes was conducted after flexible docking for the MD simulations studies by GROMACS 2023 to analyze the relationship between structure and function and properties by the analysis of molecular conformation sampling (cluster analysis, dominant conformation identification), interaction analysis (hydrogen bonding network, Contact-Map, Binding free energy calculations (MM-PBSA), backbone fluctuation analysis (RMSD, RMSF), Conformational transition analysis (simple normal mode analysis, dominant conformation identification), and physicochemical property analysis (energy, volume, pressure, temperature, density monitoring) [ 36 , 37 ]. The simulation system was set up for the protein-peptide complex with solvent using the GPU-enabled GROMACS 2024.2 package [ 38 ]. The Syndecan-4@AG73 simulation was performed in a water box with GROMACS and the standard protocols were carried out in triplicate. All necessary topology files were generated using CHARMM-GUI [ 39 , 40 ]. CHARMM-GUI was used to build the simulation system [ 41 ]. And provided scripts compatible with GROMACS for this task [ 42 ]. CHARMM-GUI provided TIP3P water model to solvate the system. The dimensions of the box were defined by ensuring at least a 10 Å distance between the protein and the box edges to avoid boundary effects. Proper neutralization of the system was achieved by adding counterions (Na + and Cl − ) based on the protein’s charge automatically calculated by CHARMM-GUI. CHARMM36 force field was chosen, which provides parameters for proteins and peptides. Before the production MD simulation, energy minimization was performed to remove any unfavorable contacts or steric clashes in the system. CHARMM-GUI The system was equilibrated in two stages. First, the system was equilibrated under an NVT ensemble (constant number of particles, volume, and temperature) with restraints applied to the heavy atoms of the protein to allow the water and ions to relax around the solute. This phase runs for 100 ps. Second, the restraints were removed, and the system was equilibrated under an NPT ensemble (constant number of particles, pressure, and temperature) for an additional 1 ns to stabilize the density of the system [ 43 , 44 ]. The minimization, equilibration, and production steps were performed using the GROMACS 2024.2 The steepest-descent energy minimization was used, and the maximum force was set to 100 kJ/(mol∙nm) on any atom. The solvated system was equilibrated with two steps. First, the system was equilibrated for 1 ns under a constant volume ensemble (NVT) with restraints applied. Second, the system was equilibrated for another 1 ns under a constant pressure ensemble (NPT) with restraints. Production simulation was conducted for 100 ns under the NPT ensemble. All bonds containing hydrogen atoms were constrained using the default LINCS constraint algorithm. The coupling algorithm of Nose-Hoover was used to maintain temperature (310 K) and Parrinello-Rahman algorithm to maintain temperature pressure (1 atm, 101 325 Pa) with a constant of 1.0 ps. The electrostatic interactions were treated with the particle mesh-Ewald (PME) method. The integration time step was set to 2 fs and periodic boundary conditions were applied in all directions [ 45 , 46 ]. 2.4.1. MMPBSA analyses GROMACS modules gmx rms for root mean square deviation (RMSD), gmx rmsf for root mean square fluctuation (RMSF), gmx hbond for numbers of hydrogen-bond (Hbond), gmx gyrate for the radius of gyration (Rg), gmx sasa for solvent accessible surface area (SASA) were used to analyze each complex system. The xmgrace module was employed to generate plots and graphs to represent binding energies, interaction frequencies, and structural changes. The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) binding free energies like van der Waals and electrostatic interactions, potential energy, polar, and non-polar solvation energies were calculated by gmx_MMPBSA a tool based on AMBER's MMPBSA.py aiming to perform end-state free energy calculations with GROMACS files [ 47 ]. It has employed the visualization tools VMD and PyMOL to examine the trajectory and interaction details. 3. Results and Discussion 3.1 Pharmacokinetics and ADMET analysis of AG73 . In silico models based on rules of drug-likeness, phisicochemistry and pharmacokinetic [ 48 ] were employed to predict the key parameters of absorption, distribution, metabolism, excretion and toxicity (ADMET) [ 49 , 50 ]. The in silico ADMET analyses compare the physicochemical properties of the AG73 peptide, which are critical for understanding the behavior of the peptide in the body, particularly drug absorption, distribution, metabolism, and excretion (ADME) (Table 2 ) [ 51 – 53 ]. Table 2 Physicochemical property of AG73 peptide. Property ADMETSAR 3.0 ADMETlab 3.0 pkCMS Molecular Weight (MW) 1471.74 1470.85 1471.74 Num. heavy atoms up up up Fraction Csp3 up 0.781 up Volume up 1462.03 up Density up 1.006 up nHA (Acceptor ) 19 40 19 nHD (Donor ) 18 20 18 nRot 50 68 657 nRing up 0 up Molar Refractivity up up 391.25 MaxRing up 0 up nHet 40 40 up fChar up 8.0 up nRig up 14 up Flexibility up 4.857 up Stereo Centers up 14 up TPSA (Ų ) 678.4 678.3 608.68 Legend: up = uncorrelated parameter. Meaning & Preference In ADMETlab: LogS (Solubility) = Optimal: higher than − 4 log mol/L; 60 µg/mL: High solubility. LogD7.4 (Distribution Coefficient D) = < 1: Solubility high; Permeability low by passive transcellular diffusion; Permeability possible via paracellular if MW 5: Solubility low; Permeability high; Metabolism high. [Insert Table 2 here] The AG73 peptide has a molecular weight just above 1470 Daltons, which is relatively large, it is a critical factor in ADME [ 54 ]. The peptide also presents a significant number of hydrogen bond donors and acceptors suggesting strong interaction potential with biological molecules, and a high number of rotatable bonds indicates high molecular flexibility which may enhance binding to target, and polar surface area, potentially affecting its solubility and permeability [ 55 ] impacting on passive diffusion across cell membranes, a fundamental event during drug absorption and distribution [ 56 ]. [Insert Table 3 here] Table 3 Absorption of AG73 peptide. Property ADMETSAR 3.0 ADMETlab 3.0 pkCMS Skin_Permeability up up -2.735 Caco-2 Permeability (nm/s) 0.758 0.651 1.416 MDCK Permeability (nm/s) 0 0.678 0.198 Pgp-inhibitor No No Yes Pgp-substrate Yes Yes Yes HIA 0.57 < 0.3 0 F20% 0 1.0 up F30% 0 1.0 up Log S (mol/L) -1.27 -0.503 -1.29 Log P -2.74 -1.199 -8.735 Log D up -1.28 -6.492 Acid pKa 3.94 6.75 up Basic pKa 5.27 6.19 up Legend: up = uncorrelated parameter. Meaning & Preference In ADMETlab: Papp (Caco-2 Permeability) = Optimal: higher than − 5.15 Log unit or -4.70 or -4.80; Pgp-inhibitor = The Pgp-inhibitor & non-inhibitor classification criteria refers the reference.; Pgp-substrate = More likely to be a Pgp substrate: N + O ≥ 8; MW > 400; Acid with pKa > 4; More likely to be a Pgp non-substrate: N + O ≤ 4; MW < 400; Acid with pKa < 8; HIA (Human Intestinal Absorption) = ≥ 30%: HIA+; <30%: HIA-; F (20% Bioavailability) = ≥ 20%: F20+; <20%: F20-; F (30% Bioavailability) = ≥ 30%: F30+; <30%: F30-. LogP (Distribution Coefficient P) = Optimal: 0 < LogP < 3; LogP 3: poor aqueous solubility. The absorption values (Table 3 ) of Log S around − 1.27 suggest low water solubility (e.g., 0.054 mg/L); low Log P value of AG73 suggests a lipophilic compound with low membrane permeability, indicating potential challenges in bioavailability unless assisted by specific delivery mechanisms [ 57 , 58 ]. Based on the Log D prediction, we can conclude that at physiologic pH, the compound is likely to have low solubility in aqueous and low lipophilicity [ 59 ]. Absorption analyses also predict low skin permeability (-2.735), suggesting that the peptide is unlikely to be absorbed through the skin effectively [ 60 ]. Caco-2 and MDCK Permeability values indicate that the peptide presents a reasonable ability to permeate intestinal cells [ 61 ]. AG73 is classified as a Pgp-substrate, which means that it may interact with this transporter in complex ways, potentially reducing its cellular uptake due to efflux [ 62 ]. Whereas the HIA and bioavailability values are < 30 indicating poor intestinal absorption. The absorption results highlight that AG73 presents low permeability across various biological membranes indicating poor absorption in oral administration [ 63 ]. [Insert Table 4 here] Table 4 Distribution of AG73 peptide. Property ADMETSAR 3.0 ADMETlab 3.0 pkCMS PPB (Plasma Protein Binding) (%) 22 14.4 up Fu (Fraction unbound in plasms) up 75.9 0.39 VD (Volume Distribution) (L/kg) 1.862 0.513 0.616 BBB (Blood–Brain Barrier) (%) 0 0 0.004 Legend: up = uncorrelated parameter. PPB (Plasma Protein Binding) = Significant with drugs that are highly protein-bound and have a low therapeutic index.; VD (Volume Distribution) = Optimal: 0.04-20L/kg; Range: 0.7L/kg: Bound to tissue components (e.g., protein, lipid),highly lipophilic. BBB (Blood–Brain Barrier) = BB ratio > = 0.1: BBB+; BB ratio < 0.1: BBB - These features tend to improve BBB permeation: H-bonds (total) < 8–10; MW < 400–500; No acids. Distribution (Table 4 ) refers to how the peptide is transported in the body, especially its binding to plasma proteins, and its ability to cross the blood-brain barrier, and its distribution across tissues. The peptide presents a low Plasma Protein (around 14–22%), which could be a limiting factor in the amount of free peptide available for distribution [ 64 ]. The fraction unbound (Fu) of 75.9% suggests that 75,9% of the peptide is freely available in the bloodstream, while the rest is bound to proteins [ 65 ]. The volume of distribution (VD) range of 0.513 and 1.862 L/kg is considered optimal for drug distribution into tissues relative to the plasma [ 66 ]. The no permeability for BBB prediction[ 67 ] indicates that the peptide may have limited central nervous system access [ 68 ]. [Insert Table 5 here] Table 5 Metabolism of AG73 peptide. Enzyme Inhibitor Substrate CYP1A2 No No CYP2B6 No No CYP2C9 No No CYP2C19 No No CYP2D6 No No CYP3A4 No Yes UGT catalyzed Yes Yes Legend: up = uncorrelated parameter. Data from ADMETSAR 3.0, ADMETlab 3.0, PreADMET and pkCMS in silico web servers. Target receptors or enzymes that a compound binds and its effect on cellular pathways is crucial in drug design (Table 5 ). Among these, the interaction of the AG73 peptide with cytochrome P450 enzymes (CYPs), is crucial for drug metabolism [ 69 ]. The AG73 peptide doesn’t demonstrate potential for CYP inhibition. However, it may act as a substrate for d CYP3A4, suggesting these enzymes could play a role in AG73 oxidative metabolism [ 70 ]. [Insert Table 6 here] Table 6 Excretion of AG73 peptide. Property ADMETSAR 3.0 ADMETlab 3.0 pkCMS CL (Clearance Rate) 1 0.252 mL/min/kg 0.125 mL/min/kg T 1/2 (Half Life Time) 0.45 3.079 up Legend: up = uncorrelated parameter. The AG73 peptide exhibits a low clearance rate and a short half-life (Table 6 ), which implies that it is metabolized and excreted relatively quickly. This profile suggests the peptide might require frequent administration for sustained therapeutic effect, depending on its pharmacodynamic properties [ 71 ]. AG73 shows potential toxicity in some areas, particularly respiratory, and carcinogenicity in mouse and rat models (Table 7 ). In general, the peptide presents negative predictions of toxicity, like hERG, hepatotoxicity, or liver injury. Table 7 Toxicity of AG73 peptide. Property ADMETSAR 3.0 ADMETlab 3.0 pkCMS hERG Blockers 0 0.012 hERG II H-HT (Human Hepatotoxicity) up 0.999 up DILI (Drug Induced Liver Injury) 1 0.0 up AMES Toxicity Non-mutagen 0.574 No Rat Oral Acute Toxicity (mol/kg) -3.39 -1.44 2.48 Skin Sensitization Negative Positive Negative Eye Corrosion Negative Negative up Eye Irritation Negative Negative up Respiratory Toxicity Positive Positive up FDAMDD (Maximum Recommended Daily Dose) Negative 0.998 up Carcinogencity Negative up up Carcinogenicity (Mouse ) Negative up positive Carcinogenicity (Rat ) Negative up positive IGC50 (Tetrahymena pyriformis) up 3.413 positive LC50FM (Acute Fathead Minnow) up 4.43 positive LC50DM (Acute daphina toxicity ) up 5.587 2.483 Acute Oral Toxicity -3.39 -1.44 0.719 NR-AR Inactive Inactive up NR-AR-LBD Inactive Inactive up NR-AhR Inactive Inactive up NR-Aromatase Inactive Inactive up NR-ER Inactive Inactive up NR-ER-LBD Inactive Inactive up NR-PPAR-gamma Inactive Inactive up SR-ARE Inactive Active up SR-ATAD5 Inactive Inactive up SR-HSE Inactive Inactive up SR-MMP Inactive Inactive up SR-p53 Inactive Active up Acute Toxicity Rule No alerts No alerts up Genotoxic Carcinogenicity Rule up No alerts up NonGenotoxic Carcinogenicity Rule up No alerts up Skin Sensitization Rule up Three alerts up Aquatic Toxicity Rule up No alerts up NonBiodegradable Rule up No alerts up SureChEMBL Rule up Three alerts up FAF-Drugs4 Rule up Two alerts up Legend: up = uncorrelated parameter. [Insert Table 7 here] In terms of Druglikeness (Table 8 ), the AG73 peptide demonstrates poor drug similarity due to its high molecular weight, significant flexibility, and high hydrogen bonding capacity. These characteristics suggest strong interaction potential with biomacromolecules, and they also imply challenges in solubility and permeability, which could limit oral bioavailability like moderate intestinal permeability, low human intestinal absorption (HIA) and bioavailability, which suggests poor absorption when administered orally, with mixed predictions regarding its interaction with P-glycoprotein. That may imply alternative delivery methods, such as intravenous administration, or through drug carriers to enhance its effectiveness in therapeutic potential. Table 8 Medical Chemistry Druglikeness ADMETSAR 3.0 ADMETlab 3.0 SwissADMET QED 0.02 0.02 up Lipinski Rule Rejected Rejected Rejected Pfizer Rule Accepted Accepted up GSK Rule Rejected Rejected up [Insert Table 8 here] 3.2 Molecular Docking The interactions between AG73 and Syndecan 4 in V region of Syndecan 4 cytoplasmic domain, the poses, the binding affinity, and the molecular flexibility was investigated by Molecular Docking. The Molecular Docking shows that the interaction with the AG73 and Syndecan 4 binding site occur by hydrogen bonding, electrostatic interactions, van der Waals forces, and hydrophobic interactions. The AG73 peptide presents a high and divaricated number of interactions with the Syndecan 4 by hydrogen bonds, suggesting a stronger complex interaction since hydrogen bonds are key interactions determining protein-ligand binding affinity (Schiebel et al., 2018). While the electrostatic interactions, and hydrophobic interactions are essential for stabilizing the ligand at the enzyme binding site, increasing the complementarity between the target and the protein's binding site [ 72 – 74 ]. Syndecan 4 presents a highly charged binding site in cytoplasmic domain V region due to the presence of Aspartic acids Asp11 and Asp39, and Lysines Lys14, Lys15, Lys41, and Lys42 fragments. Aspartic acid (Asp) is a acid amino acid and carries a negative charge at physiological pH, and Lysine (Lys) is a basic amino acids that carry a positive charge at physiological pH [ 75 ] contributing to the interaction with the ligand. [Insert Fig. 2 here] The AG73 interacts with Syndecan 4 in V region moiety by hydrogen bonds between Tyr10 (A) and the functional carboxyl group (-COOH) of the first Arginine; Lys14 (B) and the functional carboxyl group of Lysine; Gly13 (B) and the functional amine group (-NH2), Asp11 (B) and Leu12 (B) and amine group at the N-terminal in the side chain of the second arginine; Pro16 (A) and Tyr18 (A) and functional amine and carboxyl groups, Lys15 (B) and amine group at the N-terminal in the side chain of the second Glutaminine; Tyr18 (B) and the functional carboxyl group of serine, functional amine group os third arginine; Pro16 (B) and amine group at the N-terminal in the side chain of the third arginine (Fig. 2 and Table 9 ). Table 9 Interaction Types and Amino Acids involved in the interaction of Syndecan 4 with AG73 docking result by HPEPDOCK. Name Hydrogen Bond (HB) interaction Van der Walls interaction Pi -Alkyl/ Pi -Aryl Interaction Charged Bond (Salt Bridge) Unfavorable Bond Syndecan 4 Chain A Tyr10 Leu12 Lys 15 Pro16 Tyr18 Syndecan 4 Chain B Asp11 Lys15 Lys19 Tyr18 Leu12 Ile 17 Lys20 Gly13 Lys14 Pro16 Tyr18 [Insert Table 9 here] The hydrophobic AG73-Syndecan 4 interactions are important to the recognition and accommodation of the peptide in the binding site and the stability of the complex. The complex also presents electronic and van der Waals interactions: the residues Tyr18 (B) interact with the third AG73 arginine by salt bridge; Lys19 (A) and Lys20 (A) bind leucine by alkyl interactions. Lys15 (B) and Ile17 (B) contribute to van der Waals interactions. The AG73 peptide is capable of binding to the V region of the cytoplasmic domain of Syndecan-4. Both, AG73 and Syndecan 4 present charged and polar that are capable of interacting by hydrogen bonds and electrostatic bonds in ligand-binding site. The key residues DLGKKPIYKKA coordinated with the AG73 in the Syndecan 4 binding site in the V region moiety of the cytoplasmic domain are residues involved in variable moiety thet binds with PIP2[ 76 , 77 ] and interact with PKCα, α-acitin, and syndesmos [ 78 , 79 ]. This suggests that AG73 could be able like a Syndecan competitive antagonists-agonists could facilitate the process of activation of the Syndecans in regulation of cell proliferation and cell migration by affecting cell-matrix adhesion and several signaling pathways. 3.3 Theorical inhibition constant (Ki) results The theoretical inhibition constant (Ki) results suggest that AG73 is active at concentrations of 0.16 µM for the Syndecan 4 cytoplasmic domain receptor (Table 10 ). The theoretical ΔG and Ki results suggest that AG73 can bind to the binding site of the Syndecan 4 cytoplasmic domain receptor at low concentrations and form stable complexes [ 80 , 81 ]. Table 10 AG73 docking pocket site binding energy in integrin Syndecan1. Complex ΔG T (kcal/mol) ΔG L (kcal/mol) K i (µM) pK i Syndecan 4 -54.17 -9.63 0.16 -0,796 [Insert Table 10 here] 3.4 Molecular Dynamics Molecular dynamics simulations were performed to predict accurately a wide variety of association phenomena, greatly improving our understanding of the interactions of AG73 peptide laminin derivatives with the Syndecan 4 cytoplasmic domain V region. Three independent simulations were performed for the complex with different initial velocities to examine the key interactions between AG73 and Syndecan 4, such as hydrogen bonds, hydrophobic interactions, and electrostatic interactions, analyze conformal changes in the structure of the Syndecan 4 upon AG73 binding, and analyze the stability of the complex by RMSD (root mean square deviation) and RMSF (root mean square fluctuation) of protein residues and potential energy [ 82 ]. 3.4.1. AG73 peptide complex The AG73@Syndecan-4 complex RMSD measures the average deviation of complex conformation of its original conformation over time and helps to evaluate whether the complex has reached a stable state [ 83 , 84 ]. Figure 3 shows the average RMSD time series of unbound Syndecan 4 and AG73@Syndecan-4 complex. The average RMSDs of unbound Syndecan 4 were found between 0.46 and 2.09 nm, and AG73@Syndecan-4 complex were found between 0.44 and 1.64 nm respectively (Fig. 3 A). [Insert Fig. 3 here] The RMSD value of the unbound Syndecan remained stable throughout the simulation after only 7.1 s, varying just 0.43 nm, indicating a stable equilibrium. The RMSD of AG73@Syndecan-4 complex increases until 1.9 ns and then it fluctuates between 1.6 to 1.64 nm until 100 ns keeping a stable equilibrium. The presence of AG73 causes conformational changes in the structure of Syndecan 4 in AG73@Syndecan-4 complex fluctuating less than unbonded Syndecan. The RMSD plot revealed that the binding of AG73 in the variable binding site of Syndecan 4 causes conformational changes in the protein structure that could be related to the more stable structure4 [ 85 , 86 ]. The AG73@Syndecan-4 complex got stable within only 1.9 ns, and also presented smaller RMSD fluctuation. The time-averaged RMSF values of Syndecan 4, and AG73@Syndecan-4 fragments in the absence and presence of AG73 were calculated and plotted against fragment numbers at the simulation trajectory (Fig. 3 B). The unbound Syndecan 4 exhibited higher RMSF 1.04, 1,43 and 1.01 nm respectively, and AG73@Syndecan-4 complex exhibited higher RMSF 1.14, 1.22, 1.27, and 1.12 nm respectively. It was observed that the C-alpha backbone atoms of amino acid fragments between 9–90, 410–530, and 840 − 94 exhibited fluctuations compared to other regions evidencing regions with more flexible fragments [ 87 ]. Different fragments exhibited fluctuations of the average RMSF of C-alpha fragments numbered 193–297, 625–793 in AG73@Syndecan-4 complex. The fluctuations between 941–1169 indicate the fluctuations of AG73 atoms. The radius of gyration of the systems was plotted to check the protein compactness to quantify the relationship between the Rg and the simulation time [ 88 ]. The average Rg values for unbound Syndecan, and AG73@Syndecan-4 complex were found between 0.86–2.29 and 0.86–2.12 nm respectively (Fig. 3 C). The Rg value of Syndecan 4 decreased at the first 50 ns, after upward keeping a tendency of stability trend during the last 50 ns of the simulation with a short range between 1.51 and 1.74 indicating that their structure became less compact. The Rg value of AG73@Syndecan-4 complex shows a downward trend during the first 8 ns of simulation stabilizing at 47 ns, after turning upward and stabilizing between 155 − 1.66 next at 81 ns; turning upward again and stabilizing between 155 − 1.66 next at and of the simulation conservating values in a range between 3.71 and 3.90 nm until the 100 ns. The lower Rg value also specifies stables and folded protein complexes. The SASA of unbound Syndecan 4 and AG73@Syndecan-4 complex fluctuated within the range of 69.88–55.42, and 59.55 − 84.56 nm respectively (Fig. 3 D). The SASA of AG73@Syndecan-4 complex showed a slightly oscillatory upward trend after 45 ns of MD simulation. This indicates that the binding of AG73 causes structural modifications in Syndecan 4 structure suggesting that the protein has become less compact and reveals the presence of the protein-peptide interactions [ 89 ]. We have also plotted the RMSD of AG73 and hydrogen bonds with the protein to study the molecular interactions and deviation of ligand positioning during molecular dynamic simulation. The RMSD of AG73 in AG73@Syndecan-4 (Fig. 3 E) shows low fluctuations indicating the stability of the bonds of the peptide in the binding site of the complex. Hydrogen bonds were formed between the protein and the peptide evidencing the stability, directionality, and specificity of the complexes [ 90 , 91 ]. The hydrogen bonds formed between AG73 and AG73@Syndecan-4 binding sites were found between 3–5 stable hydrogen bonds during the entire simulation (Fig. 3 F). MM-PBSA analysis provided the binding affinity energy of Syndecan 4, and AG73@Syndecan-4 complex, confirming the stability of these complexes. The free binding energy (Binding ΔG) was obtained after cumulative energies score taken from van der Waals, electrostatic, polar solvation, and solvent-accessible surface area [ 92 , 93 ]. [Insert Fig. 4 here] The complex AG73@Syndecan-4 showed the highest affinity with a total free energy of -54.17 kJ/mol (Fig. 4 ). The results also showed that electrostatic contribution was higher in total binding energy suggesting higher polar interactions in the Syndecan 4 binding site. MM-PBSA analysis also confirms that AG73 forms a stable complex with the Syndecan 4 (Table 10 ). This discovery could mark a turning point in the research and development of molecular targets for Syndecans in cell proliferation and cell migration by affecting cell-matrix adhesion and several signaling pathways. 4 Conclusion The Molecular Docking and Molecular Dyamics of Ag73 interactions with the Syndecan 4 provides new insights about molecular interactions between AG73 and the V region of the Syndecan-4 cytoplasmic domain and PKCα activation, highlighting their potential implications for cancer therapy. Molecular Docking and Molecular Dyamics of Syndecan-4@AG73 complex demonstrated the formation of a stable complex. In the V region of the Syndecan-4 cytoplasmic domain, AG73 binding the key residues associated with Syndecan 4 activity, inducing conformational changes and stabilizing the protein conformal structure and enhance binding affinity whith PKCα suggestin that AG73 may act as a competitive modulator, influencing Syndecan-4-mediated processes such as cell adhesion, migration, and proliferation. However, in terms of pharmacokinetics and phamacodynamics the peptide presents poor drug-likeness properties of AG73 highlight the need for alternative delivery strategies, such as intravenous administration or incorporation into drug carriers, to improve its therapeutic potential. This work highligth the potential of AG73 to contribute to novel therapeutic strategies of targeting cell-ECM interactions and signaling pathways in the tumor microenvironment. The computational analyses, including in silico Pharmacokinetics, pharmacodynamics, Molecular Docking and Dynamics, provide a foundation for further experimental validation and optimization of AG73 as a candidate for targeting Syndecan-4 in cancer progression. Future studies should explore the in vivo efficacy of AG73, its pharmacokinetics, and the broader implications of its role in modulating Syndecan-4 pathways Declarations Disclosure statement Authors declare no conflict of interest in this work with any potentially affected party by results of this study. The authors also declare no influence of any second party on designing of conducting the study. Consent to publish declaration Not applicable. Data Availability Not applicable. Ethics and Consent to Participate declarations Not applicable Acknowledgements Anderson Nogueira Mendes (#302704/2023-0) are grateful to the public Brazilian agency “Conselho Nacional de Desenvolvimento Científico e Tecnológico” (CNPq) for their personal scholarships. Funding declaration Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq): #302704/2023-0 Author Contribution Francisco das Chagas Pereira de Andrade: Conceptualization, Investigation Methodology, Writing - review & editing. Conceptualization, Investigation; Anderson Nogueira Mendes: Investigation, Supervision, Writing - review & editing, Project administration. All authors reviewed the manuscript. References Gondelaud F, Ricard-Blum S (2019) Structures and interactions of syndecans. FEBS Journal 286:2994–3007. https://doi.org/10.1111/febs.14828 Afratis NA, Nikitovic D, Multhaupt HAB, et al (2017) Syndecans – key regulators of cell signaling and biological functions. FEBS Journal 284:27–41. https://doi.org/10.1111/febs.13940 Morgan MR, Humphries MJ, Bass MD (2007) Synergistic control of cell adhesion by integrins and syndecans. Nat Rev Mol Cell Biol 8:957–969. https://doi.org/10.1038/nrm2289 Hozumi K, Kobayashi K, Katagiri F, et al (2010) Syndecan- and integrin-binding peptides synergistically accelerate cell adhesion. FEBS Lett 584:3381–3385. https://doi.org/10.1016/j.febslet.2010.06.032 Kim M, Kim Y (2023) NMR Structural Study of Syndecan-4 Transmembrane Domain with Cytoplasmic Region. Molecules 28:. https://doi.org/10.3390/molecules28237855 Cheng B, Montmasson M, Terradot L, Rousselle P (2016) Syndecans as cell surface receptors in cancer biology. A focus on their interaction with PDZ domain proteins. Front Pharmacol 7:. https://doi.org/10.3389/fphar.2016.00010 Brunet S, Sardon T, Zimmerman T, et al (2004) Characterization of the TPX2 Domains Involved in Microtubule Nucleation and Spindle Assembly in Xenopus nucleation around chromatin and functions in a network of other molecules, some of which also are regulated by. Mol Biol Cell 15:5318–5328. https://doi.org/10.1091/mbc.E04 Hilenski LL, Griendling KK (2013) Chap. 3 - Vascular Smooth Muscle. In: Creager MA, Beckman JA, Loscalzo J (eds) Vascular Medicine: A Companion to Braunwald’s Heart Disease (Second Edition), Second Edition. W.B. Saunders, Philadelphia, pp 25–42 Teng YH-F, Aquino RS, Park PW (2012) Molecular functions of syndecan-1 in disease. Matrix Biol 31:3–16. https://doi.org/10.1016/j.matbio.2011.10.001 Hetfrich MH, Horton MA (2006) CHAPTER 8 - Integrins and Other Adhesion Molecules. In: SEIBEL MJ, ROBINS SP, BILEZIKIAN JP (eds) Dynamics of Bone and Cartilage Metabolism (Second Edition), Second Edi. Academic Press, Burlington, pp 129–151 Chung H, Multhaupt HAB, Oh ES, Couchman JR (2016) Minireview: Syndecans and their crucial roles during tissue regeneration. FEBS Lett 590:2408–2417. https://doi.org/10.1002/1873-3468.12280 Woods A, Couchman JR (1994) Syndecan 4 heparan sulfate proteoglycan is a selectively enriched and widespread focal adhesion component. Mol Biol Cell 5:183–192. https://doi.org/10.1091/mbc.5.2.183 Elfenbein A, Simons M (2013) Syndecan-4 signaling at a glance. J Cell Sci 126:3799–3804. https://doi.org/10.1242/jcs.124636 Kim M, Kim Y (2023) NMR Structural Study of Syndecan-4 Transmembrane Domain with Cytoplasmic Region. Molecules 28:. https://doi.org/10.3390/molecules28237855 Hwang J, Park E, Choi YW, et al (2022) Emerging role of syndecans in maintaining homeostasis of colon epithelium during inflammation. Am J Physiol Cell Physiol 322:C960–C966. https://doi.org/10.1152/ajpcell.00048.2022 Choi Y, Yun JH, Yoo J, et al (2016) New structural insight of C-terminal region of Syntenin-1, enhancing the molecular dimerization and inhibitory function related on Syndecan-4 signaling. Sci Rep 6:1–16. https://doi.org/10.1038/srep36818 Horowitz A, Murakami M, Gao Y, Simons M (1999) Phosphatidylinositol-4,5-bisphosphate mediates the interaction of syndecan-4 with protein kinase C. Biochemistry 38:15871–15877. https://doi.org/10.1021/bi991363i Vuoriluoto K (2010) Anchor or Accelerate – a Study on Cancer Cell Adhesion Czarnowski D (2021) Syndecans in cancer: A review of function, expression, prognostic value, and therapeutic significance. Cancer Treat Res Commun 27:100312. https://doi.org/10.1016/j.ctarc.2021.100312 Mochizuki M, Philp D, Hozumi K, et al (2007) Angiogenic activitiy of syndecan-binding laminin peptide AG73 (RKRLQVQLSIRT). Arch Biochem Biophys 459:249–255. https://doi.org/https://doi.org/10.1016/j.abb.2006.12.026 Kikkawa Y, Hozumi K, Katagiri F, et al (2013) Laminin-111-derived peptides and cancer. Cell Adh Migr 7:150–159. https://doi.org/10.4161/cam.22827 Puchalapalli M, Mu L, Edwards C, et al (2019) The Laminin- α 1 Chain-Derived Peptide, AG73, Binds to Syndecans on MDA-231 Breast Cancer Cells and Alters Filopodium Formation. Analytical Cellular Pathology 2019:. https://doi.org/10.1155/2019/9192516 Pires, Douglas E. V.; Blundell, Tom L.; Ascher DB (2015) pkCSM: Predicting Small-Molecule Pharmacokinetic and Properties Using Graph-Based Signatures. Jornal or Medicinal Chemistry 58:4066–4072 Daina A, Zoete V (2016) A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem 1117–1121. https://doi.org/10.1002/cmdc.201600182 Xiong G, Wu Z, Yi J, et al (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49:W5–W14. https://doi.org/10.1093/nar/gkab255 Yang H, Lou C, Sun L, et al (2019) AdmetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 35:1067–1069. https://doi.org/10.1093/bioinformatics/bty707 Gu Y, Yu Z, Wang Y, et al (2024) admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties. Nucleic Acids Res 52:W432–W438. https://doi.org/10.1093/nar/gkae298 Andrade F das CP de, Mendes AN (2020) Computational analysis of eugenol inhibitory activity in lipoxygenase and cyclooxygenase pathways. Sci Rep 10:1–14. https://doi.org/10.1038/s41598-020-73203-z Yan Y, Zhang D, Huang SY (2017) Efficient conformational ensemble generation of protein-bound peptides. J Cheminform 9:1–13. https://doi.org/10.1186/s13321-017-0246-7 Zhou P, Jin B, Li H, Huang SY (2018) HPEPDOCK: A web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res 46:W443–W450. https://doi.org/10.1093/nar/gky357 Cheminform KJ, Kenny PW (2019) The nature of ligand efficiency. J Cheminform 1–19. https://doi.org/10.1186/s13321-019-0330-2 Lazim R, Suh D, Choi S (2020) Advances in molecular dynamics simulations and enhanced sampling methods for the study of protein systems. Int J Mol Sci 21:1–20. https://doi.org/10.3390/ijms21176339 Freissler E, Auf Der Heyde AM, David G, et al (2000) Syndecan-1 and syndecan-4 can mediate the invasion of Opa(HSPG)- expressing Neisseria gonorrhoeae into epithelial cells. Cell Microbiol 2:69–82. https://doi.org/10.1046/j.1462-5822.2000.00036.x Onyeisi JOS, Lopes CC, Götte M (2021) Syndecan-4 as a pathogenesis factor and therapeutic target in cancer. Biomolecules 11:1–16. https://doi.org/10.3390/biom11040503 Harrison D, Hussain S-A, Combs AC, et al (2007) Crystal structure and cell surface anchorage sites of laminin alpha1LG4-5. J Biol Chem 282:11573–11581. https://doi.org/10.1074/jbc.M610657200 Zhang Z, Shi Y, Liu H (2003) Molecular dynamics simulations of peptides and proteins with amplified collective motions. Biophys J 84:3583–3593. https://doi.org/10.1016/S0006-3495(03)75090-5 Luzik DA, Rogacheva ON, Izmailov SA, et al (2019) Molecular Dynamics model of peptide-protein conjugation: case study of covalent complex between Sos1 peptide and N-terminal SH3 domain from Grb2. Sci Rep 9:1–18. https://doi.org/10.1038/s41598-019-56078-7 Abraham M, Alekseenko A, Basov V, et al (2024) GROMACS 2024.2 Source code Park SJ, Kern N, Brown T, et al (2023) CHARMM-GUI PDB Manipulator: Various PDB Structural Modifications for Biomolecular Modeling and Simulation. J Mol Biol 435:167995. https://doi.org/10.1016/j.jmb.2023.167995 Lee J, Cheng X, Swails JM, et al (2016) CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput 12:405–413. https://doi.org/10.1021/acs.jctc.5b00935 Allouche A (2012) Software News and Updates Gabedit — A Graphical User Interface for Computational Chemistry Softwares. J Comput Chem 32:174–182. https://doi.org/10.1002/jcc Lee J, Cheng X, Swails JM, et al (2016) CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput 12:405–413. https://doi.org/10.1021/acs.jctc.5b00935 Galmozzi A, Dominguez E, Cravatt BF, Saez E (2014) CHARMM-GUI Ligand Binder for Absolute Binding Free Energy Calculations and Its Application. Methods Enzymol 538:151–169. https://doi.org/10.1021/ci300505n.CHARMM-GUI Wang KW, Lee J, Zhang H, et al (2022) CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs. Journal of Physical Chemistry B 126:7354–7364. https://doi.org/10.1021/acs.jpcb.2c05294 Bansal R, Mohagaonkar S, Sen A, et al (2021) In-silico study of peptide-protein interaction of antimicrobial peptides potentially targeting SARS and SARS-CoV-2 nucleocapsid protein. In Silico Pharmacol 9:1–14. https://doi.org/10.1007/s40203-021-00103-z Yu H, Wang M jun, Xuan N xia, et al (2015) Molecular dynamics simulation of the interactions between EHD1 EH domain and multiple peptides. J Zhejiang Univ Sci B 16:883–896. https://doi.org/10.1631/jzus.B1500106 Valdés-Tresanco MS, Valdés-Tresanco ME, Valiente PA, Moreno E (2021) gmx\_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput 17:6281–6291. https://doi.org/10.1021/acs.jctc.1c00645 Manhas D, Dhiman S, Kour H, et al (2024) ADME / PK Insights of Crocetin: A Molecule Having an Unusual Chemical Structure with Druglike Features. ACS Omega. https://doi.org/10.1021/acsomega.4c02116 Flores-holguín N, Frau J, Glossman-mitnik D (2021) Computational Pharmacokinetics Report, ADMET Study and Conceptual DFT-Based Estimation of the Chemical Reactivity Properties of Marine Cyclopeptides. ChemistryOpen 1142–1149. https://doi.org/10.1002/open.202100178 Clark DE (2005) Chap. 10 Computational Prediction of ADMET Properties: Recent Developments and Future Challenges. In: Annual Reports in Computational Chemistry. Elsevier, pp 133–151 Grogan S PC (2024) Pharmacokinetics. StatPearls Gianti E, Zauhar RJ (2021) Chap. 7 - Structure–activity relationships and drug design. In: Adejare A (ed) Remington (Twenty-third Edition), Twenty-thi. Academic Press, pp 129–153 Stielow M, Witczyńska A, Kubryń N, et al (2023) The Bioavailability of Drugs—The Current State of Knowledge. Molecules 28:. https://doi.org/10.3390/molecules28248038 Gharbi Y, Mercado R (2024) A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design. ArXiv 1–18 Barlow N, Chalmers DK, Williams-Noonan BJ, et al (2020) Improving Membrane Permeation in the Beyond Rule-of-Five Space by Using Prodrugs to Mask Hydrogen Bond Donors. ACS Chem Biol 15:2070–2078. https://doi.org/10.1021/acschembio.0c00218 Coimbra JTS, Feghali R, Ribeiro RP, et al (2020) The importance of intramolecular hydrogen bonds on the translocation of the small drug piracetam through a lipid bilayer. RSC Adv 11:899–908. https://doi.org/10.1039/d0ra09995c Preeti, Sambhakar S, Saharan R, et al (2023) Exploring LIPIDs for their potential to improves bioavailability of lipophilic drugs candidates: A review. Saudi Pharmaceutical Journal 31:101870. https://doi.org/10.1016/j.jsps.2023.101870 António L, Rocha F (2021) Development of vascularizable hydrogels to promote spinal cord injury repair: from RGD to mimetic ligands Waring MJ (2009) Defining optimum lipophilicity and molecular weight ranges for drug candidates-Molecular weight dependent lower log D limits based on permeability. Bioorg Med Chem Lett 19:2844–2851. https://doi.org/10.1016/j.bmcl.2009.03.109 Martínez-Campa C, González A, Mediavilla MD, et al (2009) Melatonin inhibits aromatase promoter expression by regulating cyclooxygenases expression and activity in breast cancer cells. Br J Cancer 101:1613–1619. https://doi.org/10.1038/sj.bjc.6605336 Pham The H, González-Álvarez I, Bermejo M, et al (2011) In Silico Prediction of Caco-2 Cell Permeability by a Classification QSAR Approach. Mol Inform 30:376–385. https://doi.org/https://doi.org/10.1002/minf.201000118 Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14:251–264. https://doi.org/10.1023/A:1008130001697 Silva, H. H. M., Alves, M. H. P., Gonçalves, R. J. S., Pereira, N. de A., & Affonso CRG (2023) Molecular modification proposal: analysis of physicochemical, pharmacokinetic and toxicological parameters “in silico” of barbituric drugs Li J, Yanagisawa K, Yoshikawa Y, et al (2022) Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning. Bioinformatics 38:1110–1117. https://doi.org/10.1093/bioinformatics/btab726 Bowman CM, Benet LZ (2018) An examination of protein binding and protein-facilitated uptake relating to in vitro-in vivo extrapolation. European Journal of Pharmaceutical Sciences 123:502–514. https://doi.org/10.1016/j.ejps.2018.08.008 Mahabadi. AMN (2024) Volume of Distribution. StatPearls Publishing Wang Z, Yang H, Wu Z, et al (2018) In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods. ChemMedChem 13:2189–2201. https://doi.org/https://doi.org/10.1002/cmdc.201800533 Vistoli G, Pedretti A, Testa B (2008) Assessing drug-likeness - what are we missing? Drug Discov Today 13:285–294. https://doi.org/10.1016/j.drudis.2007.11.007 Sekhar KC, Syed R, Golla M, et al (2014) Novel heteroaryl phosphonicdiamides PTPs inhibitors as anti-hyperglycemic agents. DARU, Journal of Pharmaceutical Sciences 22:. https://doi.org/10.1186/s40199-014-0076-3 Spina E, de Leon J (2016) What is the role of CYP genotyping in psychiatry ? Evidence-based Psychiatric Care 2:108–122 Tangboriboon N (2019) Chap. 15 - Carbon and Carbon Nanotube Drug Delivery and Its Characterization, Properties, and Applications. In: Mohapatra SS, Ranjan S, Dasgupta N, et al (eds) Nanocarriers for Drug Delivery. Elsevier, pp 451–467 Schiebel J, Gaspari R, Wulsdorf T, et al (2018) Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes. Nat Commun 9:. https://doi.org/10.1038/s41467-018-05769-2 Motiejunas D, Wade RC (2007) Structural, Energetic, and Dynamic Aspects of Ligand–Receptor Interactions. In: Taylor JB, Triggle DJ (eds) Comprehensive Medicinal Chemistry II. Elsevier, Oxford, pp 193–213 Patil R, Das S, Stanley A, et al (2010) Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. PLoS One 5:e12029. https://doi.org/10.1371/journal.pone.0012029 Pelley JW (2012) 2 - Structure and Properties of Biologic Molecules. In: Pelley JW (ed) Elsevier’s Integrated Review Biochemistry (Second Edition), Second Edi. W.B. Saunders, Philadelphia, pp 7–18 Multhaupt HA, Yoneda A, Whiteford JR, et al (2009) Syndecan signaling: when, where and why? J Physiol Pharmacol 60 Suppl 4:31–38 Koo B-K, Jung YS, Shin J, et al (2006) Structural Basis of Syndecan-4 Phosphorylation as a Molecular Switch to Regulate Signaling. J Mol Biol 355:651–663. https://doi.org/https://doi.org/10.1016/j.jmb.2005.09.087 Multhaupt HA, Yoneda A, Whiteford JR, et al (2009) Syndecan signaling: when, where and why? J Physiol Pharmacol 60 Suppl 4:31–38 Keller-Pinter A, Gyulai-Nagy S, Becsky D, et al (2021) Syndecan-4 in tumor cell motility. Cancers (Basel) 13:1–24. https://doi.org/10.3390/cancers13133322 Yoshino M, Murakami K (2009) A graphical method for determining inhibition constants. J Enzyme Inhib Med Chem 24:1288–1290. https://doi.org/10.3109/14756360902829766 De Brevern AG Recent Developments on Protein-Ligand Interactions From Structure, Function to Applications Filgueiras LA, de Andrade F das CP, Iwao Horita S, et al (2024) Analysis of SIKVAV’s receptor affinity, pharmacokinetics, and pharmacological characteristics: a matrikine with potent biological function. J Biomol Struct Dyn 1–23. https://doi.org/10.1080/07391102.2024.2313709 Aier I, Varadwaj PK, Raj U (2016) Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep 6:1–10. https://doi.org/10.1038/srep34984 Li X, Guo J, Lian J, et al (2021) Molecular Simulation Study on the Interaction between Tyrosinase and Flavonoids from Sea Buckthorn. ACS Omega 6:21579–21585. https://doi.org/10.1021/acsomega.1c02593 Rampogu S, Lee G, Park JS, et al (2022) Molecular Docking and Molecular Dynamics Simulations Discover Curcumin Analogue as a Plausible Dual Inhibitor for SARS-CoV-2. Int J Mol Sci 23:. https://doi.org/10.3390/ijms23031771 Sharma J, Kumar V, Singh R, Rajendran V (2020) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19. The COVID-19 resource centre is hosted on Elsevier Connect, the company ’ s public news and information. Food Chem Novak J, Tseilikman VE, Tseilikman OB, et al (2023) Can Resveratrol Influence the Activity of 11β-Hydroxysteroid Dehydrogenase Type 1? A Combined In Silico and In Vivo Study. Pharmaceuticals 16:1–18. https://doi.org/10.3390/ph16020251 Pereira GRC, De Azevedo Abrahim Vieira B, De Mesquita JF (2021) Comprehensive in silico analysis and molecular dynamics of the superoxide dismutase 1 (SOD1) variants related to amyotrophic lateral sclerosis. PLoS One 16:1–27. https://doi.org/10.1371/journal.pone.0247841 Bagewadi ZK, Yunus Khan TM, Gangadharappa B, et al (2023) Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach. Saudi J Biol Sci 30:103753. https://doi.org/https://doi.org/10.1016/j.sjbs.2023.103753 Madushanka A, Moura RT, Verma N, Kraka E (2023) Quantum Mechanical Assessment of Protein–Ligand Hydrogen Bond Strength Patterns: Insights from Semiempirical Tight-Binding and Local Vibrational Mode Theory. Int J Mol Sci 24:. https://doi.org/10.3390/ijms24076311 Adhav VA, Saikrishnan K (2023) The Realm of Unconventional Noncovalent Interactions in Proteins: Their Significance in Structure and Function. ACS Omega 8:22268–22284. https://doi.org/10.1021/acsomega.3c00205 Wang C, Greene D, Xiao L, et al (2018) Recent developments and applications of the MMPBSA method. Front Mol Biosci 4:1–18. https://doi.org/10.3389/fmolb.2017.00087 Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461. https://doi.org/10.1517/17460441.2015.1032936 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6090672","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446466520,"identity":"96ccb874-9e2c-43a9-837b-1702401d6436","order_by":0,"name":"Francisco das Chagas Pereira de Andrade","email":"","orcid":"","institution":"Federal University of Piauí","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"das Chagas Pereira","lastName":"de Andrade","suffix":""},{"id":446466521,"identity":"47a6e353-08f1-4d02-8c05-0fdfc71bcc75","order_by":1,"name":"Anderson Nogueira Mendes","email":"data:image/png;base64,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","orcid":"","institution":"Federal University of Piauí","correspondingAuthor":true,"prefix":"","firstName":"Anderson","middleName":"Nogueira","lastName":"Mendes","suffix":""}],"badges":[],"createdAt":"2025-02-23 14:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6090672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6090672/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81318046,"identity":"4bbf8d0e-a5bf-4643-9bcb-d9785037f8dd","added_by":"auto","created_at":"2025-04-24 16:42:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103989,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular Structure of peptide AG73. (A) 2D Molecular Structure. (B) 3D Molecular Structure view along the main plane. (C) 3D view beta-sheet structure. 2D structures were built in the tool draw peptide primary structure PepDraw (https://pepdraw.com/). The 3D structure was obtained in Pymol and saved by SAMSON 22 R. Beta-sheet structure obtained by Pymol.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6090672/v1/757c65659277d7a75400d568.png"},{"id":81318049,"identity":"07f5bc4c-8f88-4c8d-8e1e-1cef57cdb357","added_by":"auto","created_at":"2025-04-24 16:42:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAG73\u003c/strong\u003e \u003cstrong\u003emolecular docking and molecular interaction with Syndecan 4.\u003c/strong\u003e (A) Prediction of ligand in Syndecan 4 binding sites. (B) AG73 alignment with Syndecan 4 binding pocket; (C) molecular interaction of AG73 with integrin Syndecan 4\u003cstrong\u003e \u003c/strong\u003ebinding pocket S1; (D) AG73alignment with Syndecan 4\u003cstrong\u003e \u003c/strong\u003ebinding pocket with coordinated active fragments Tyr10, Asp 11, Leu12, Lys15, Pro16, Tyr18 in the chain A, and Asp11, Leu12, Gly13, Lys14, Lys15, Pro16, Ile17, Tyr18, Lys 20 in the chain B; The dashed lines in green evidence the hydrogen bonds. Structure A was generated in PrankWeb, a web-based application; Figure B was generated in SAMSON and Figure C was generated in Discovery Studio software version 2020. LIGPLOT+ generated pocket 2D structure (D). The authors created the figures.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6090672/v1/fc879ac5a6c69d0cb22f47ca.png"},{"id":81318047,"identity":"9196282a-2aec-489a-859f-fa9f1c6a6252","added_by":"auto","created_at":"2025-04-24 16:42:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation analysis of unbound Syndecan 4 \u0026nbsp;and protein complex Syndecan 4 with AG73\u003c/strong\u003e. (A) Backbone RMSD (B) RMSF. (C) The radius of gyration (Rg). (D) SASA. Unbound Syndecan 4\u003cstrong\u003e \u003c/strong\u003eis shown in black, the Syndecan_4@AG73 complex is shown in red. (E) RMSD of AG73. (F) Total number of hydrogen bonds of AG73 in Syndecan 4\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6090672/v1/603d60d86f4ebd9c52d9cc25.png"},{"id":81318048,"identity":"1eafd811-29be-4a2a-99b3-0c6a02522585","added_by":"auto","created_at":"2025-04-24 16:42:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSystems enegetics componetes\u003c/strong\u003e: (A) Syndecan 4; (B) Syndecan_4@AG73 complex; (C) AG73. The binding free energy is separated into gas-phase energy (ΔGGAS), eletrônico entropic contribution (EEL) and, polar solvation energy (EEG), non-polar solvation energy (ESURF).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6090672/v1/0032de4d06b3460f2db162af.png"},{"id":89895718,"identity":"42a67d16-4e35-476e-b339-6819f5fdf935","added_by":"auto","created_at":"2025-08-26 08:24:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1767123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6090672/v1/383e8baa-d759-4a66-856f-55184acbedd9.pdf"},{"id":81318067,"identity":"814dd0a9-0076-4610-8a58-6daaf1cba9b3","added_by":"auto","created_at":"2025-04-24 16:42:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3215810,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6090672/v1/9e9d6de37ad96063377b83f9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular Insights into AG73@Syndecan-4 Interactions: Implications for Cancer Cell Adhesion, Migration, and Therapeutic Potential","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThere are four syndecans identified in mammals those are membrane proteoglycans that play major roles in regulating cell behavior, cell signaling, and cellmatrix interactions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Syndecans and Integrins mediate cell adhesion to extracellular matrix and their synergistic cooperation is implicated in cell adhesion processes [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Syndecans structure prensents a heparan-sulfate-modified extracellular domain that regulates various biological processes, a single transmenbrane domain, apart from a short C-terminal cytoplasmic domain mediated by numerous interactions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSyndecans help regulate cell proliferation and migration, angiogenesis, cell/cell and cell/ECM adhesion, and they may participate in several key tumorigenesis processes regulating tumor cell proliferation, adhesion, motility [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The cytoplasmic domain is likely to be involved in these events through recruitment of mediators to effect activation of a variety of intracellular signaling cascades [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSyndecan 1 is the major syndecan of epithelia and can function as a cell-matrix receptor binding various matrix protein like collagen, fibronectin, tenascin, and in addition can bind members of the FGF family [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While Syndecan 2 is expressed in mesenchymal cells and syndecan 3 is highly expressed in neuron cells and muscle stem cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Syndecan 4 is present widespread tissue distribution [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; and mediates numerous cellular processes through signaling pathways that affect cellular proliferation, migration, mechanotransduction and endocytosis present a crucial role in cell adhesion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe protein structure of syndecans enables it to play multiple roles, both as a component of the ECM and as a cell adhesion receptor that acts through interactions with numerous matrix components in several biological processes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, syndecans lack any intrinsic signaling capacity, the interactions of their cytoplasmic domains with various adaptor proteins including the postsynaptic density protein, disc large, and the zonula occludens (PDZ), [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSyndecan 4 cytodomain V region is also activate by 4,5-bisphosphate phosphatidylinositol (PIP2) and protein kinase Cα (PKCα) downstream signaling pathways and playing essential regulatory roles in cell adhesion process [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The V region of the cytoplasmic domain of Syndecan 4 binds to PIP2 to activaty PKCα forming a tetramer with 2 PIP2 molecules to the catalytic subunit of PKCα and resulting in the activation complex regulated by the phosphorylation of the cytoplasmic Ser179 (human Ser179, rat Ser183) of Syndecan 4 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe AG73 peptide (RKRLQVQLSIRT) is mouse laminin-1 α-chain derivated (2719\u0026ndash;2730), [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This peptide sequence binds to cell surface proteoglycans, including syndecan-1, -2 and \u0026minus;\u0026thinsp;4. These receptors play critical regulatory roles in a variety of physiological and pathophysiological functions, including wound healing, inflammation, neural patterning, tumor growth and angiogenesis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. AG73 plays an important role in cell adhesion and has previously been linked to migration, invasion, and metastasis. Puchalapalli et al demonstrated the intrinsic interaction between AG73 and Syndecans on breast cancer cells in supporting tumor cell adhesion and invasion through filopodia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe syndecan-4-AG73 interaction can influence cell migration behavior and modulates cell adhesion, cells migration and invasion. In this study we employed Molecular Docking and Molecular Dynamics to investigate the probability of activating the PKCα activity by interaction with the syndecan 4 cytoplasmic domain by AG73 interaction with syndecan 4 V region in cytoplamic domain, looking for bindin modes, binding energy and conformational changes.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Evaluation of AG73 of \u003cem\u003ein silico\u003c/em\u003e ADMET properties.\u003c/h2\u003e \u003cp\u003eThe Administration, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties predictions were evaluated \u003cem\u003ein silico\u003c/em\u003e analysis employing four \u003cem\u003eonline\u003c/em\u003e databases: pkCMS [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], SwissADME [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], ADMETlab 2.0[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and ADMETSAR 2.0 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The Physicochemical and pharmacokinetic properties predictors were used considering literature to validation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The results obtained with each predictor were compared with the others. When necessary, the measurement units were transformed to gain greater confidence. The AG73 peptide chemical structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was extracted from the 2jd4 crystal structure (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/structure/2jd4\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/structure/2jd4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Molecular Docking of AG73 peptide with protein receptors.\u003c/h2\u003e \u003cp\u003eProtein\u0026ndash;peptide interactions were predicted using molecular docking. Protein\u0026ndash;peptide docking using HPEPDOCK a \u003cem\u003eweb\u003c/em\u003e hierarchical flexible-peptide docking protocol that integrates MODPEP [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] program for peptide conformational sampling [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], was used to analyze the protein-peptide interactions and binding interface with syndecan 2. Redocking by blind docking was performed in HPEPDOCK and the poses were compared to the validation of the initial Molecular Docking.\u003c/p\u003e \u003cp\u003eThe coordinate file of the protein structures was obtained using the experimental coordinates from the Protein Data Bank and UniProt. The missing residues in the protein structures were searched through sequence analysis tools framework EMBOSS Needle and fixed by software Modeller10.4. The files were saved in the PDB format (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Peptide PDB files were also prepared for the ligand initially prepared in Discovery Studio 2020, always maintaining the flexibility of the rotating bonds. For the submission of docking in HPEPDOCK, the structures were protonated through the server \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://server.poissonboltzmann.org/apbs\u003c/span\u003e\u003cspan address=\"https://server.poissonboltzmann.org/apbs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e by the PARSE method.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAG73 PBD structure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacromolecule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyndecan 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1EJP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOLUTION NMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.90 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eTo visualize the molecular docking results, identify interacting residues, elucidate extensive electrostatic interactions, and van der Waals interactions, and demonstrate the abundance of polar amino acid residues it was employed PyMol 2.1.1 and Discovery 2021 Studio software. The PyMol 2.1.1 software was also used to find the coordinates for each complex formed and to visualize the crystallographic location of the ligand to compare the conformation energies. To obtain the 2D and 3D images of the interactions between the ligand and the receptor, we used the softwares Discovery 2021 Studio and SAMSON software respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Inhibition constant theoretical calculation\u003c/h2\u003e \u003cp\u003eThe inhibition constant (Ki) was obtained from the binding energy (ΔG) using the expression Ki\u0026thinsp;=\u0026thinsp;exp (ΔG/RT) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], where R is the universal gas constant (1.985 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e kcal.mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.K\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and T is the temperature (310.15 K).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular Dynamics\u003c/h2\u003e \u003cp\u003eMolecular Dynamics simulations studies it was conducted to find and comprehend protein-peptide interactions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and investigat whether the AG73 peptide interacts with the Syndecan 4 intracellular domain in V region involved in PKCα activation, which are thought to be linked to the actin cytoskeleton via additional protein-protein interactions, mediating cell adhesion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, we suggest that AG73 adopts a β-sheet conformation like in 2jd4.pdb [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The PDB structure from which the peptide sequence was extracted.\u003c/p\u003e \u003cp\u003eThe better docking scores poses of the protein-peptide complexes was conducted after flexible docking for the MD simulations studies by GROMACS 2023 to analyze the relationship between structure and function and properties by the analysis of molecular conformation sampling (cluster analysis, dominant conformation identification), interaction analysis (hydrogen bonding network, Contact-Map, Binding free energy calculations (MM-PBSA), backbone fluctuation analysis (RMSD, RMSF), Conformational transition analysis (simple normal mode analysis, dominant conformation identification), and physicochemical property analysis (energy, volume, pressure, temperature, density monitoring) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe simulation system was set up for the protein-peptide complex with solvent using the GPU-enabled GROMACS 2024.2 package [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The Syndecan-4@AG73 simulation was performed in a water box with GROMACS and the standard protocols were carried out in triplicate. All necessary topology files were generated using CHARMM-GUI [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCHARMM-GUI was used to build the simulation system [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. And provided scripts compatible with GROMACS for this task [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. CHARMM-GUI provided TIP3P water model to solvate the system. The dimensions of the box were defined by ensuring at least a 10 \u0026Aring; distance between the protein and the box edges to avoid boundary effects. Proper neutralization of the system was achieved by adding counterions (Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) based on the protein\u0026rsquo;s charge automatically calculated by CHARMM-GUI. CHARMM36 force field was chosen, which provides parameters for proteins and peptides. Before the production MD simulation, energy minimization was performed to remove any unfavorable contacts or steric clashes in the system. CHARMM-GUI The system was equilibrated in two stages. First, the system was equilibrated under an NVT ensemble (constant number of particles, volume, and temperature) with restraints applied to the heavy atoms of the protein to allow the water and ions to relax around the solute. This phase runs for 100 ps. Second, the restraints were removed, and the system was equilibrated under an NPT ensemble (constant number of particles, pressure, and temperature) for an additional 1 ns to stabilize the density of the system [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe minimization, equilibration, and production steps were performed using the GROMACS 2024.2 The steepest-descent energy minimization was used, and the maximum force was set to 100 kJ/(mol∙nm) on any atom. The solvated system was equilibrated with two steps. First, the system was equilibrated for 1 ns under a constant volume ensemble (NVT) with restraints applied. Second, the system was equilibrated for another 1 ns under a constant pressure ensemble (NPT) with restraints. Production simulation was conducted for 100 ns under the NPT ensemble. All bonds containing hydrogen atoms were constrained using the default LINCS constraint algorithm. The coupling algorithm of Nose-Hoover was used to maintain temperature (310 K) and Parrinello-Rahman algorithm to maintain temperature pressure (1 atm, 101 325 Pa) with a constant of 1.0 ps. The electrostatic interactions were treated with the particle mesh-Ewald (PME) method. The integration time step was set to 2 fs and periodic boundary conditions were applied in all directions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. MMPBSA analyses\u003c/h2\u003e \u003cp\u003eGROMACS modules gmx rms for root mean square deviation (RMSD), gmx rmsf for root mean square fluctuation (RMSF), gmx hbond for numbers of hydrogen-bond (Hbond), gmx gyrate for the radius of gyration (Rg), gmx sasa for solvent accessible surface area (SASA) were used to analyze each complex system. The xmgrace module was employed to generate plots and graphs to represent binding energies, interaction frequencies, and structural changes. The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) binding free energies like van der Waals and electrostatic interactions, potential energy, polar, and non-polar solvation energies were calculated by gmx_MMPBSA a tool based on AMBER's MMPBSA.py aiming to perform end-state free energy calculations with GROMACS files [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. It has employed the visualization tools VMD and PyMOL to examine the trajectory and interaction details.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Pharmacokinetics and ADMET analysis of AG73 .\u003c/h2\u003e \u003cp\u003e \u003cem\u003eIn silico\u003c/em\u003e models based on rules of drug-likeness, phisicochemistry and pharmacokinetic [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] were employed to predict the key parameters of absorption, distribution, metabolism, excretion and toxicity (ADMET) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The \u003cem\u003ein silico\u003c/em\u003e ADMET analyses compare the physicochemical properties of the AG73 peptide, which are critical for understanding the behavior of the peptide in the body, particularly drug absorption, distribution, metabolism, and excretion (ADME) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical property of AG73 peptide.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADMETSAR 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADMETlab 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epkCMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular Weight (MW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1471.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1470.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1471.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNum. heavy atoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFraction Csp3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1462.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enHA (Acceptor )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enHD (Donor )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enRot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enRing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolar Refractivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e391.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaxRing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enHet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efChar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enRig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlexibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStereo Centers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPSA (\u0026Aring;\u0026sup2; )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e678.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e608.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLegend: up =\u0026thinsp;uncorrelated parameter.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMeaning \u0026amp; Preference In ADMETlab: LogS (Solubility)\u0026thinsp;=\u0026thinsp;Optimal: higher than \u0026minus;\u0026thinsp;4 log mol/L; \u0026lt;10 \u0026micro;g/mL: Low solubility; 10\u0026ndash;60 \u0026micro;g/mL: Moderate solubility; \u0026gt;60 \u0026micro;g/mL: High solubility. LogD7.4 (Distribution Coefficient D)\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;1: Solubility high; Permeability low by passive transcellular diffusion; Permeability possible via paracellular if MW\u0026thinsp;\u0026lt;\u0026thinsp;200; Metabolism low. 1 to 3: Solubility moderate; Permeability moderate; Metabolism low. 3 to 5: Solubility low; Permeability high; Metabolism moderate to high. \u0026gt; 5: Solubility low; Permeability high; Metabolism high.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe AG73 peptide has a molecular weight just above 1470 Daltons, which is relatively large, it is a critical factor in ADME [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The peptide also presents a significant number of hydrogen bond donors and acceptors suggesting strong interaction potential with biological molecules, and a high number of rotatable bonds indicates high molecular flexibility which may enhance binding to target, and polar surface area, potentially affecting its solubility and permeability [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] impacting on passive diffusion across cell membranes, a fundamental event during drug absorption and distribution [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbsorption of AG73 peptide.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADMETSAR 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADMETlab 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epkCMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin_Permeability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaco-2 Permeability (nm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDCK Permeability (nm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePgp-inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePgp-substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog S (mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcid pKa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic pKa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLegend: up =\u0026thinsp;uncorrelated parameter.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMeaning \u0026amp; Preference In ADMETlab: Papp (Caco-2 Permeability)\u0026thinsp;=\u0026thinsp;Optimal: higher than \u0026minus;\u0026thinsp;5.15 Log unit or -4.70 or -4.80; Pgp-inhibitor\u0026thinsp;=\u0026thinsp;The Pgp-inhibitor \u0026amp; non-inhibitor classification criteria refers the reference.; Pgp-substrate\u0026thinsp;=\u0026thinsp;More likely to be a Pgp substrate: N\u0026thinsp;+\u0026thinsp;O\u0026thinsp;\u0026ge;\u0026thinsp;8; MW\u0026thinsp;\u0026gt;\u0026thinsp;400; Acid with pKa\u0026thinsp;\u0026gt;\u0026thinsp;4; More likely to be a Pgp non-substrate: N\u0026thinsp;+\u0026thinsp;O\u0026thinsp;\u0026le;\u0026thinsp;4; MW\u0026thinsp;\u0026lt;\u0026thinsp;400; Acid with pKa\u0026thinsp;\u0026lt;\u0026thinsp;8; HIA (Human Intestinal Absorption)\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;30%: HIA+; \u0026lt;30%: HIA-; F (20% Bioavailability)\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;20%: F20+; \u0026lt;20%: F20-; F (30% Bioavailability)\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;30%: F30+; \u0026lt;30%: F30-. LogP (Distribution Coefficient P)\u0026thinsp;=\u0026thinsp;Optimal: 0\u0026thinsp;\u0026lt;\u0026thinsp;LogP\u0026thinsp;\u0026lt;\u0026thinsp;3; LogP\u0026thinsp;\u0026lt;\u0026thinsp;0: poor lipid bilayer permeability.; LogP\u0026thinsp;\u0026gt;\u0026thinsp;3: poor aqueous solubility.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe absorption values (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) of Log S around \u0026minus;\u0026thinsp;1.27 suggest low water solubility (e.g., 0.054 mg/L); low Log P value of AG73 suggests a lipophilic compound with low membrane permeability, indicating potential challenges in bioavailability unless assisted by specific delivery mechanisms [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Based on the Log D prediction, we can conclude that at physiologic pH, the compound is likely to have low solubility in aqueous and low lipophilicity [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Absorption analyses also predict low skin permeability (-2.735), suggesting that the peptide is unlikely to be absorbed through the skin effectively [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Caco-2 and MDCK Permeability values indicate that the peptide presents a reasonable ability to permeate intestinal cells [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAG73 is classified as a Pgp-substrate, which means that it may interact with this transporter in complex ways, potentially reducing its cellular uptake due to efflux [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Whereas the HIA and bioavailability values are \u0026lt;\u0026thinsp;30 indicating poor intestinal absorption. The absorption results highlight that AG73 presents low permeability across various biological membranes indicating poor absorption in oral administration [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of AG73 peptide.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADMETSAR 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADMETlab\u003c/p\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epkCMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPB (Plasma Protein Binding) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFu (Fraction unbound in plasms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVD (Volume Distribution) (L/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBB (Blood\u0026ndash;Brain Barrier) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLegend: up =\u0026thinsp;uncorrelated parameter.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePPB (Plasma Protein Binding)\u0026thinsp;=\u0026thinsp;Significant with drugs that are highly protein-bound and have a low therapeutic index.; VD (Volume Distribution)\u0026thinsp;=\u0026thinsp;Optimal: 0.04-20L/kg; Range: \u0026lt;0.07L/kg: Confined to blood, Bound to plasma protein or highly hydrophilic; 0.07-0.7L/kg: Evenly distributed; \u0026gt;0.7L/kg: Bound to tissue components (e.g., protein, lipid),highly lipophilic. BBB (Blood\u0026ndash;Brain Barrier)\u0026thinsp;=\u0026thinsp;BB ratio\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.1: BBB+; BB ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.1: BBB - These features tend to improve BBB permeation: H-bonds (total)\u0026thinsp;\u0026lt;\u0026thinsp;8\u0026ndash;10; MW\u0026thinsp;\u0026lt;\u0026thinsp;400\u0026ndash;500; No acids.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDistribution (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) refers to how the peptide is transported in the body, especially its binding to plasma proteins, and its ability to cross the blood-brain barrier, and its distribution across tissues. The peptide presents a low Plasma Protein (around 14\u0026ndash;22%), which could be a limiting factor in the amount of free peptide available for distribution [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The fraction unbound (Fu) of 75.9% suggests that 75,9% of the peptide is freely available in the bloodstream, while the rest is bound to proteins [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The volume of distribution (VD) range of 0.513 and 1.862 L/kg is considered optimal for drug distribution into tissues relative to the plasma [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The no permeability for BBB prediction[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] indicates that the peptide may have limited central nervous system access [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetabolism of AG73 peptide.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnzyme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInhibitor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubstrate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP1A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2B6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2D6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP3A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUGT catalyzed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eLegend: up =\u0026thinsp;uncorrelated parameter.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eData from ADMETSAR 3.0, ADMETlab 3.0, PreADMET and pkCMS \u003cem\u003ein silico\u003c/em\u003e web servers.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTarget receptors or enzymes that a compound binds and its effect on cellular pathways is crucial in drug design (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among these, the interaction of the AG73 peptide with cytochrome P450 enzymes (CYPs), is crucial for drug metabolism [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The AG73 peptide doesn\u0026rsquo;t demonstrate potential for CYP inhibition. However, it may act as a substrate for d CYP3A4, suggesting these enzymes could play a role in AG73 oxidative metabolism [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExcretion of AG73 peptide.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADMETSAR 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADMETlab 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epkCMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003cp\u003e(Clearance Rate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003cp\u003emL/min/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003cp\u003emL/min/kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e1/2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(Half Life Time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLegend: up =\u0026thinsp;uncorrelated parameter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe AG73 peptide exhibits a low clearance rate and a short half-life (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which implies that it is metabolized and excreted relatively quickly. This profile suggests the peptide might require frequent administration for sustained therapeutic effect, depending on its pharmacodynamic properties [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. AG73 shows potential toxicity in some areas, particularly respiratory, and carcinogenicity in mouse and rat models (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In general, the peptide presents negative predictions of toxicity, like hERG, hepatotoxicity, or liver injury.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eToxicity of AG73 peptide.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADMETSAR 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADMETlab\u003c/p\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epkCMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehERG Blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehERG II\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-HT (Human Hepatotoxicity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDILI (Drug Induced Liver Injury)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMES Toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-mutagen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRat Oral Acute Toxicity (mol/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin Sensitization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye Corrosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye Irritation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory Toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDAMDD (Maximum Recommended Daily Dose)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarcinogencity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarcinogenicity (Mouse )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarcinogenicity (Rat )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGC50 (Tetrahymena pyriformis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC50FM (Acute Fathead Minnow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC50DM (Acute daphina toxicity )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Oral Toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR-AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR-AR-LBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR-AhR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR-Aromatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR-ER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR-ER-LBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR-PPAR-gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR-ARE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR-ATAD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR-HSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR-MMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR-p53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Toxicity Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotoxic Carcinogenicity Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonGenotoxic Carcinogenicity Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin Sensitization Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThree alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquatic Toxicity Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonBiodegradable Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSureChEMBL Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThree alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAF-Drugs4 Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLegend: up =\u0026thinsp;uncorrelated parameter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eIn terms of Druglikeness (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), the AG73 peptide demonstrates poor drug similarity due to its high molecular weight, significant flexibility, and high hydrogen bonding capacity. These characteristics suggest strong interaction potential with biomacromolecules, and they also imply challenges in solubility and permeability, which could limit oral bioavailability like moderate intestinal permeability, low human intestinal absorption (HIA) and bioavailability, which suggests poor absorption when administered orally, with mixed predictions regarding its interaction with P-glycoprotein. That may imply alternative delivery methods, such as intravenous administration, or through drug carriers to enhance its effectiveness in therapeutic potential.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMedical Chemistry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDruglikeness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADMETSAR 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADMETlab 3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSwissADMET\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipinski Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePfizer Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSK Rule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Molecular Docking\u003c/h2\u003e \u003cp\u003eThe interactions between AG73 and Syndecan 4 in V region of Syndecan 4 cytoplasmic domain, the poses, the binding affinity, and the molecular flexibility was investigated by Molecular Docking. The Molecular Docking shows that the interaction with the AG73 and Syndecan 4 binding site occur by hydrogen bonding, electrostatic interactions, van der Waals forces, and hydrophobic interactions. The AG73 peptide presents a high and divaricated number of interactions with the Syndecan 4 by hydrogen bonds, suggesting a stronger complex interaction since hydrogen bonds are key interactions determining protein-ligand binding affinity (Schiebel et al., 2018). While the electrostatic interactions, and hydrophobic interactions are essential for stabilizing the ligand at the enzyme binding site, increasing the complementarity between the target and the protein's binding site [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSyndecan 4 presents a highly charged binding site in cytoplasmic domain V region due to the presence of Aspartic acids Asp11 and Asp39, and Lysines Lys14, Lys15, Lys41, and Lys42 fragments. Aspartic acid (Asp) is a acid amino acid and carries a negative charge at physiological pH, and Lysine (Lys) is a basic amino acids that carry a positive charge at physiological pH [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] contributing to the interaction with the ligand.\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe AG73 interacts with Syndecan 4 in V region moiety by hydrogen bonds between Tyr10 (A) and the functional carboxyl group (-COOH) of the first Arginine; Lys14 (B) and the functional carboxyl group of Lysine; Gly13 (B) and the functional amine group (-NH2), Asp11 (B) and Leu12 (B) and amine group at the N-terminal in the side chain of the second arginine; Pro16 (A) and Tyr18 (A) and functional amine and carboxyl groups, Lys15 (B) and amine group at the N-terminal in the side chain of the second Glutaminine; Tyr18 (B) and the functional carboxyl group of serine, functional amine group os third arginine; Pro16 (B) and amine group at the N-terminal in the side chain of the third arginine (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction Types and Amino Acids involved in the interaction of Syndecan 4 with AG73 docking result by HPEPDOCK.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrogen Bond (HB) interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVan der Walls interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePi -Alkyl/\u003c/p\u003e \u003cp\u003ePi -Aryl\u003c/p\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCharged Bond (Salt Bridge)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnfavorable Bond\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSyndecan 4\u003c/p\u003e \u003cp\u003eChain A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeu12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLys 15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePro16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTyr18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSyndecan 4\u003c/p\u003e \u003cp\u003eChain B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLys15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTyr18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeu12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIle 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGly13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePro16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyr18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe hydrophobic AG73-Syndecan 4 interactions are important to the recognition and accommodation of the peptide in the binding site and the stability of the complex. The complex also presents electronic and van der Waals interactions: the residues Tyr18 (B) interact with the third AG73 arginine by salt bridge; Lys19 (A) and Lys20 (A) bind leucine by alkyl interactions. Lys15 (B) and Ile17 (B) contribute to van der Waals interactions.\u003c/p\u003e \u003cp\u003eThe AG73 peptide is capable of binding to the V region of the cytoplasmic domain of Syndecan-4. Both, AG73 and Syndecan 4 present charged and polar that are capable of interacting by hydrogen bonds and electrostatic bonds in ligand-binding site. The key residues DLGKKPIYKKA coordinated with the AG73 in the Syndecan 4 binding site in the V region moiety of the cytoplasmic domain are residues involved in variable moiety thet binds with PIP2[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and interact with PKCα, α-acitin, and syndesmos [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. This suggests that AG73 could be able like a Syndecan competitive antagonists-agonists could facilitate the process of activation of the Syndecans in regulation of cell proliferation and cell migration by affecting cell-matrix adhesion and several signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Theorical inhibition constant (Ki) results\u003c/h2\u003e \u003cp\u003eThe theoretical inhibition constant (Ki) results suggest that AG73 is active at concentrations of 0.16 \u0026micro;M for the Syndecan 4 cytoplasmic domain receptor (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The theoretical ΔG and Ki results suggest that AG73 can bind to the binding site of the Syndecan 4 cytoplasmic domain receptor at low concentrations and form stable complexes [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAG73 docking pocket site binding energy in integrin Syndecan1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔG\u003csub\u003eT\u003c/sub\u003e (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔG\u003csub\u003eL\u003c/sub\u003e (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e (\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epK\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyndecan 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-54.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Molecular Dynamics\u003c/h2\u003e \u003cp\u003eMolecular dynamics simulations were performed to predict accurately a wide variety of association phenomena, greatly improving our understanding of the interactions of AG73 peptide laminin derivatives with the Syndecan 4 cytoplasmic domain V region. Three independent simulations were performed for the complex with different initial velocities to examine the key interactions between AG73 and Syndecan 4, such as hydrogen bonds, hydrophobic interactions, and electrostatic interactions, analyze conformal changes in the structure of the Syndecan 4 upon AG73 binding, and analyze the stability of the complex by RMSD (root mean square deviation) and RMSF (root mean square fluctuation) of protein residues and potential energy [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. AG73 peptide complex\u003c/h2\u003e \u003cp\u003eThe AG73@Syndecan-4 complex RMSD measures the average deviation of complex conformation of its original conformation over time and helps to evaluate whether the complex has reached a stable state [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the average RMSD time series of unbound Syndecan 4 and AG73@Syndecan-4 complex. The average RMSDs of unbound Syndecan 4 were found between 0.46 and 2.09 nm, and AG73@Syndecan-4 complex were found between 0.44 and 1.64 nm respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe RMSD value of the unbound Syndecan remained stable throughout the simulation after only 7.1 s, varying just 0.43 nm, indicating a stable equilibrium. The RMSD of AG73@Syndecan-4 complex increases until 1.9 ns and then it fluctuates between 1.6 to 1.64 nm until 100 ns keeping a stable equilibrium. The presence of AG73 causes conformational changes in the structure of Syndecan 4 in AG73@Syndecan-4 complex fluctuating less than unbonded Syndecan. The RMSD plot revealed that the binding of AG73 in the variable binding site of Syndecan 4 causes conformational changes in the protein structure that could be related to the more stable structure4 [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The AG73@Syndecan-4 complex got stable within only 1.9 ns, and also presented smaller RMSD fluctuation.\u003c/p\u003e \u003cp\u003eThe time-averaged RMSF values of Syndecan 4, and AG73@Syndecan-4 fragments in the absence and presence of AG73 were calculated and plotted against fragment numbers at the simulation trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The unbound Syndecan 4 exhibited higher RMSF 1.04, 1,43 and 1.01 nm respectively, and AG73@Syndecan-4 complex exhibited higher RMSF 1.14, 1.22, 1.27, and 1.12 nm respectively. It was observed that the C-alpha backbone atoms of amino acid fragments between 9\u0026ndash;90, 410\u0026ndash;530, and 840\u0026thinsp;\u0026minus;\u0026thinsp;94 exhibited fluctuations compared to other regions evidencing regions with more flexible fragments [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Different fragments exhibited fluctuations of the average RMSF of C-alpha fragments numbered 193\u0026ndash;297, 625\u0026ndash;793 in AG73@Syndecan-4 complex. The fluctuations between 941\u0026ndash;1169 indicate the fluctuations of AG73 atoms.\u003c/p\u003e \u003cp\u003eThe radius of gyration of the systems was plotted to check the protein compactness to quantify the relationship between the Rg and the simulation time [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. The average Rg values for unbound Syndecan, and AG73@Syndecan-4 complex were found between 0.86\u0026ndash;2.29 and 0.86\u0026ndash;2.12 nm respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The Rg value of Syndecan 4 decreased at the first 50 ns, after upward keeping a tendency of stability trend during the last 50 ns of the simulation with a short range between 1.51 and 1.74 indicating that their structure became less compact. The Rg value of AG73@Syndecan-4 complex shows a downward trend during the first 8 ns of simulation stabilizing at 47 ns, after turning upward and stabilizing between 155\u0026thinsp;\u0026minus;\u0026thinsp;1.66 next at 81 ns; turning upward again and stabilizing between 155\u0026thinsp;\u0026minus;\u0026thinsp;1.66 next at and of the simulation conservating values in a range between 3.71 and 3.90 nm until the 100 ns. The lower Rg value also specifies stables and folded protein complexes.\u003c/p\u003e \u003cp\u003eThe SASA of unbound Syndecan 4 and AG73@Syndecan-4 complex fluctuated within the range of 69.88\u0026ndash;55.42, and 59.55\u0026thinsp;\u0026minus;\u0026thinsp;84.56 nm respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The SASA of AG73@Syndecan-4 complex showed a slightly oscillatory upward trend after 45 ns of MD simulation. This indicates that the binding of AG73 causes structural modifications in Syndecan 4 structure suggesting that the protein has become less compact and reveals the presence of the protein-peptide interactions [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe have also plotted the RMSD of AG73 and hydrogen bonds with the protein to study the molecular interactions and deviation of ligand positioning during molecular dynamic simulation. The RMSD of AG73 in AG73@Syndecan-4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) shows low fluctuations indicating the stability of the bonds of the peptide in the binding site of the complex. Hydrogen bonds were formed between the protein and the peptide evidencing the stability, directionality, and specificity of the complexes [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. The hydrogen bonds formed between AG73 and AG73@Syndecan-4 binding sites were found between 3\u0026ndash;5 stable hydrogen bonds during the entire simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eMM-PBSA analysis provided the binding affinity energy of Syndecan 4, and AG73@Syndecan-4 complex, confirming the stability of these complexes. The free binding energy (Binding ΔG) was obtained after cumulative energies score taken from van der Waals, electrostatic, polar solvation, and solvent-accessible surface area [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe complex AG73@Syndecan-4 showed the highest affinity with a total free energy of -54.17 kJ/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results also showed that electrostatic contribution was higher in total binding energy suggesting higher polar interactions in the Syndecan 4 binding site. MM-PBSA analysis also confirms that AG73 forms a stable complex with the Syndecan 4 (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). This discovery could mark a turning point in the research and development of molecular targets for Syndecans in cell proliferation and cell migration by affecting cell-matrix adhesion and several signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThe Molecular Docking and Molecular Dyamics of Ag73 interactions with the Syndecan 4 provides new insights about molecular interactions between AG73 and the V region of the Syndecan-4 cytoplasmic domain and PKCα activation, highlighting their potential implications for cancer therapy.\u003c/p\u003e \u003cp\u003eMolecular Docking and Molecular Dyamics of Syndecan-4@AG73 complex demonstrated the formation of a stable complex. In the V region of the Syndecan-4 cytoplasmic domain, AG73 binding the key residues associated with Syndecan 4 activity, inducing conformational changes and stabilizing the protein conformal structure and enhance binding affinity whith PKCα suggestin that AG73 may act as a competitive modulator, influencing Syndecan-4-mediated processes such as cell adhesion, migration, and proliferation.\u003c/p\u003e \u003cp\u003eHowever, in terms of pharmacokinetics and phamacodynamics the peptide presents poor drug-likeness properties of AG73 highlight the need for alternative delivery strategies, such as intravenous administration or incorporation into drug carriers, to improve its therapeutic potential.\u003c/p\u003e \u003cp\u003eThis work highligth the potential of AG73 to contribute to novel therapeutic strategies of targeting cell-ECM interactions and signaling pathways in the tumor microenvironment. The computational analyses, including in silico Pharmacokinetics, pharmacodynamics, Molecular Docking and Dynamics, provide a foundation for further experimental validation and optimization of AG73 as a candidate for targeting Syndecan-4 in cancer progression.\u003c/p\u003e \u003cp\u003eFuture studies should explore the in vivo efficacy of AG73, its pharmacokinetics, and the broader implications of its role in modulating Syndecan-4 pathways\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflict of interest in this work with any potentially affected party by results of this study. The authors also declare no influence of any second party on designing of conducting the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnderson Nogueira Mendes (#302704/2023-0) are grateful to the public Brazilian agency “Conselho Nacional de Desenvolvimento Científico e Tecnológico” (CNPq) for their personal scholarships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq): #302704/2023-0\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrancisco das Chagas Pereira de Andrade: Conceptualization, Investigation Methodology, Writing - review \u0026amp; editing. Conceptualization, Investigation; Anderson Nogueira Mendes: Investigation, Supervision, Writing - review \u0026amp; editing, Project administration. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGondelaud F, Ricard-Blum S (2019) Structures and interactions of syndecans. FEBS Journal 286:2994\u0026ndash;3007. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/febs.14828\u003c/span\u003e\u003cspan address=\"10.1111/febs.14828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfratis NA, Nikitovic D, Multhaupt HAB, et al (2017) Syndecans \u0026ndash; key regulators of cell signaling and biological functions. FEBS Journal 284:27\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/febs.13940\u003c/span\u003e\u003cspan address=\"10.1111/febs.13940\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgan MR, Humphries MJ, Bass MD (2007) Synergistic control of cell adhesion by integrins and syndecans. Nat Rev Mol Cell Biol 8:957\u0026ndash;969. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrm2289\u003c/span\u003e\u003cspan address=\"10.1038/nrm2289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHozumi K, Kobayashi K, Katagiri F, et al (2010) Syndecan- and integrin-binding peptides synergistically accelerate cell adhesion. FEBS Lett 584:3381\u0026ndash;3385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.febslet.2010.06.032\u003c/span\u003e\u003cspan address=\"10.1016/j.febslet.2010.06.032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim M, Kim Y (2023) NMR Structural Study of Syndecan-4 Transmembrane Domain with Cytoplasmic Region. Molecules 28:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules28237855\u003c/span\u003e\u003cspan address=\"10.3390/molecules28237855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng B, Montmasson M, Terradot L, Rousselle P (2016) Syndecans as cell surface receptors in cancer biology. A focus on their interaction with PDZ domain proteins. Front Pharmacol 7:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2016.00010\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2016.00010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunet S, Sardon T, Zimmerman T, et al (2004) Characterization of the TPX2 Domains Involved in Microtubule Nucleation and Spindle Assembly in Xenopus nucleation around chromatin and functions in a network of other molecules, some of which also are regulated by. Mol Biol Cell 15:5318\u0026ndash;5328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1091/mbc.E04\u003c/span\u003e\u003cspan address=\"10.1091/mbc.E04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHilenski LL, Griendling KK (2013) Chap. 3 - Vascular Smooth Muscle. In: Creager MA, Beckman JA, Loscalzo J (eds) Vascular Medicine: A Companion to Braunwald\u0026rsquo;s Heart Disease (Second Edition), Second Edition. W.B. Saunders, Philadelphia, pp 25\u0026ndash;42\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeng YH-F, Aquino RS, Park PW (2012) Molecular functions of syndecan-1 in disease. Matrix Biol 31:3\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.matbio.2011.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.matbio.2011.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHetfrich MH, Horton MA (2006) CHAPTER 8 - Integrins and Other Adhesion Molecules. In: SEIBEL MJ, ROBINS SP, BILEZIKIAN JP (eds) Dynamics of Bone and Cartilage Metabolism (Second Edition), Second Edi. Academic Press, Burlington, pp 129\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung H, Multhaupt HAB, Oh ES, Couchman JR (2016) Minireview: Syndecans and their crucial roles during tissue regeneration. FEBS Lett 590:2408\u0026ndash;2417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/1873-3468.12280\u003c/span\u003e\u003cspan address=\"10.1002/1873-3468.12280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoods A, Couchman JR (1994) Syndecan 4 heparan sulfate proteoglycan is a selectively enriched and widespread focal adhesion component. Mol Biol Cell 5:183\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1091/mbc.5.2.183\u003c/span\u003e\u003cspan address=\"10.1091/mbc.5.2.183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElfenbein A, Simons M (2013) Syndecan-4 signaling at a glance. J Cell Sci 126:3799\u0026ndash;3804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1242/jcs.124636\u003c/span\u003e\u003cspan address=\"10.1242/jcs.124636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim M, Kim Y (2023) NMR Structural Study of Syndecan-4 Transmembrane Domain with Cytoplasmic Region. Molecules 28:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules28237855\u003c/span\u003e\u003cspan address=\"10.3390/molecules28237855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang J, Park E, Choi YW, et al (2022) Emerging role of syndecans in maintaining homeostasis of colon epithelium during inflammation. Am J Physiol Cell Physiol 322:C960\u0026ndash;C966. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/ajpcell.00048.2022\u003c/span\u003e\u003cspan address=\"10.1152/ajpcell.00048.2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi Y, Yun JH, Yoo J, et al (2016) New structural insight of C-terminal region of Syntenin-1, enhancing the molecular dimerization and inhibitory function related on Syndecan-4 signaling. Sci Rep 6:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep36818\u003c/span\u003e\u003cspan address=\"10.1038/srep36818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorowitz A, Murakami M, Gao Y, Simons M (1999) Phosphatidylinositol-4,5-bisphosphate mediates the interaction of syndecan-4 with protein kinase C. Biochemistry 38:15871\u0026ndash;15877. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/bi991363i\u003c/span\u003e\u003cspan address=\"10.1021/bi991363i\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVuoriluoto K (2010) Anchor or Accelerate \u0026ndash; a Study on Cancer Cell Adhesion\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzarnowski D (2021) Syndecans in cancer: A review of function, expression, prognostic value, and therapeutic significance. Cancer Treat Res Commun 27:100312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ctarc.2021.100312\u003c/span\u003e\u003cspan address=\"10.1016/j.ctarc.2021.100312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMochizuki M, Philp D, Hozumi K, et al (2007) Angiogenic activitiy of syndecan-binding laminin peptide AG73 (RKRLQVQLSIRT). Arch Biochem Biophys 459:249\u0026ndash;255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.abb.2006.12.026\u003c/span\u003e\u003cspan address=\"10.1016/j.abb.2006.12.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKikkawa Y, Hozumi K, Katagiri F, et al (2013) Laminin-111-derived peptides and cancer. Cell Adh Migr 7:150\u0026ndash;159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4161/cam.22827\u003c/span\u003e\u003cspan address=\"10.4161/cam.22827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuchalapalli M, Mu L, Edwards C, et al (2019) The Laminin- α 1 Chain-Derived Peptide, AG73, Binds to Syndecans on MDA-231 Breast Cancer Cells and Alters Filopodium Formation. Analytical Cellular Pathology 2019:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2019/9192516\u003c/span\u003e\u003cspan address=\"10.1155/2019/9192516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePires, Douglas E. V.; Blundell, Tom L.; Ascher DB (2015) pkCSM: Predicting Small-Molecule Pharmacokinetic and Properties Using Graph-Based Signatures. Jornal or Medicinal Chemistry 58:4066\u0026ndash;4072\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina A, Zoete V (2016) A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem 1117\u0026ndash;1121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cmdc.201600182\u003c/span\u003e\u003cspan address=\"10.1002/cmdc.201600182\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong G, Wu Z, Yi J, et al (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49:W5\u0026ndash;W14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkab255\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkab255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Lou C, Sun L, et al (2019) AdmetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 35:1067\u0026ndash;1069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/bty707\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bty707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Y, Yu Z, Wang Y, et al (2024) admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties. Nucleic Acids Res 52:W432\u0026ndash;W438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae298\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade F das CP de, Mendes AN (2020) Computational analysis of eugenol inhibitory activity in lipoxygenase and cyclooxygenase pathways. Sci Rep 10:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-73203-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-73203-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Zhang D, Huang SY (2017) Efficient conformational ensemble generation of protein-bound peptides. J Cheminform 9:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13321-017-0246-7\u003c/span\u003e\u003cspan address=\"10.1186/s13321-017-0246-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou P, Jin B, Li H, Huang SY (2018) HPEPDOCK: A web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res 46:W443\u0026ndash;W450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gky357\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheminform KJ, Kenny PW (2019) The nature of ligand efficiency. J Cheminform 1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13321-019-0330-2\u003c/span\u003e\u003cspan address=\"10.1186/s13321-019-0330-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazim R, Suh D, Choi S (2020) Advances in molecular dynamics simulations and enhanced sampling methods for the study of protein systems. Int J Mol Sci 21:1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21176339\u003c/span\u003e\u003cspan address=\"10.3390/ijms21176339\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreissler E, Auf Der Heyde AM, David G, et al (2000) Syndecan-1 and syndecan-4 can mediate the invasion of Opa(HSPG)- expressing Neisseria gonorrhoeae into epithelial cells. Cell Microbiol 2:69\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1462-5822.2000.00036.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1462-5822.2000.00036.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnyeisi JOS, Lopes CC, G\u0026ouml;tte M (2021) Syndecan-4 as a pathogenesis factor and therapeutic target in cancer. Biomolecules 11:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom11040503\u003c/span\u003e\u003cspan address=\"10.3390/biom11040503\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison D, Hussain S-A, Combs AC, et al (2007) Crystal structure and cell surface anchorage sites of laminin alpha1LG4-5. J Biol Chem 282:11573\u0026ndash;11581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M610657200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M610657200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Shi Y, Liu H (2003) Molecular dynamics simulations of peptides and proteins with amplified collective motions. Biophys J 84:3583\u0026ndash;3593. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0006-3495(03)75090-5\u003c/span\u003e\u003cspan address=\"10.1016/S0006-3495(03)75090-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuzik DA, Rogacheva ON, Izmailov SA, et al (2019) Molecular Dynamics model of peptide-protein conjugation: case study of covalent complex between Sos1 peptide and N-terminal SH3 domain from Grb2. Sci Rep 9:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-56078-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-56078-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraham M, Alekseenko A, Basov V, et al (2024) GROMACS 2024.2 Source code\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark SJ, Kern N, Brown T, et al (2023) CHARMM-GUI PDB Manipulator: Various PDB Structural Modifications for Biomolecular Modeling and Simulation. J Mol Biol 435:167995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmb.2023.167995\u003c/span\u003e\u003cspan address=\"10.1016/j.jmb.2023.167995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Cheng X, Swails JM, et al (2016) CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput 12:405\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jctc.5b00935\u003c/span\u003e\u003cspan address=\"10.1021/acs.jctc.5b00935\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllouche A (2012) Software News and Updates Gabedit \u0026mdash; A Graphical User Interface for Computational Chemistry Softwares. J Comput Chem 32:174\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcc\u003c/span\u003e\u003cspan address=\"10.1002/jcc\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Cheng X, Swails JM, et al (2016) CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput 12:405\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jctc.5b00935\u003c/span\u003e\u003cspan address=\"10.1021/acs.jctc.5b00935\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalmozzi A, Dominguez E, Cravatt BF, Saez E (2014) CHARMM-GUI Ligand Binder for Absolute Binding Free Energy Calculations and Its Application. Methods Enzymol 538:151\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/ci300505n.CHARMM-GUI\u003c/span\u003e\u003cspan address=\"10.1021/ci300505n.CHARMM-GUI\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang KW, Lee J, Zhang H, et al (2022) CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs. Journal of Physical Chemistry B 126:7354\u0026ndash;7364. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jpcb.2c05294\u003c/span\u003e\u003cspan address=\"10.1021/acs.jpcb.2c05294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal R, Mohagaonkar S, Sen A, et al (2021) In-silico study of peptide-protein interaction of antimicrobial peptides potentially targeting SARS and SARS-CoV-2 nucleocapsid protein. In Silico Pharmacol 9:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40203-021-00103-z\u003c/span\u003e\u003cspan address=\"10.1007/s40203-021-00103-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu H, Wang M jun, Xuan N xia, et al (2015) Molecular dynamics simulation of the interactions between EHD1 EH domain and multiple peptides. J Zhejiang Univ Sci B 16:883\u0026ndash;896. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1631/jzus.B1500106\u003c/span\u003e\u003cspan address=\"10.1631/jzus.B1500106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVald\u0026eacute;s-Tresanco MS, Vald\u0026eacute;s-Tresanco ME, Valiente PA, Moreno E (2021) gmx\\_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput 17:6281\u0026ndash;6291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jctc.1c00645\u003c/span\u003e\u003cspan address=\"10.1021/acs.jctc.1c00645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManhas D, Dhiman S, Kour H, et al (2024) ADME / PK Insights of Crocetin: A Molecule Having an Unusual Chemical Structure with Druglike Features. ACS Omega. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsomega.4c02116\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.4c02116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores-holgu\u0026iacute;n N, Frau J, Glossman-mitnik D (2021) Computational Pharmacokinetics Report, ADMET Study and Conceptual DFT-Based Estimation of the Chemical Reactivity Properties of Marine Cyclopeptides. ChemistryOpen 1142\u0026ndash;1149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/open.202100178\u003c/span\u003e\u003cspan address=\"10.1002/open.202100178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark DE (2005) Chap. 10 Computational Prediction of ADMET Properties: Recent Developments and Future Challenges. In: Annual Reports in Computational Chemistry. Elsevier, pp 133\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrogan S PC (2024) Pharmacokinetics. StatPearls\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGianti E, Zauhar RJ (2021) Chap. 7 - Structure\u0026ndash;activity relationships and drug design. In: Adejare A (ed) Remington (Twenty-third Edition), Twenty-thi. Academic Press, pp 129\u0026ndash;153\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStielow M, Witczyńska A, Kubryń N, et al (2023) The Bioavailability of Drugs\u0026mdash;The Current State of Knowledge. Molecules 28:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules28248038\u003c/span\u003e\u003cspan address=\"10.3390/molecules28248038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGharbi Y, Mercado R (2024) A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design. ArXiv 1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarlow N, Chalmers DK, Williams-Noonan BJ, et al (2020) Improving Membrane Permeation in the Beyond Rule-of-Five Space by Using Prodrugs to Mask Hydrogen Bond Donors. ACS Chem Biol 15:2070\u0026ndash;2078. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acschembio.0c00218\u003c/span\u003e\u003cspan address=\"10.1021/acschembio.0c00218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoimbra JTS, Feghali R, Ribeiro RP, et al (2020) The importance of intramolecular hydrogen bonds on the translocation of the small drug piracetam through a lipid bilayer. RSC Adv 11:899\u0026ndash;908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/d0ra09995c\u003c/span\u003e\u003cspan address=\"10.1039/d0ra09995c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreeti, Sambhakar S, Saharan R, et al (2023) Exploring LIPIDs for their potential to improves bioavailability of lipophilic drugs candidates: A review. Saudi Pharmaceutical Journal 31:101870. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jsps.2023.101870\u003c/span\u003e\u003cspan address=\"10.1016/j.jsps.2023.101870\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnt\u0026oacute;nio L, Rocha F (2021) Development of vascularizable hydrogels to promote spinal cord injury repair: from RGD to mimetic ligands\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaring MJ (2009) Defining optimum lipophilicity and molecular weight ranges for drug candidates-Molecular weight dependent lower log D limits based on permeability. Bioorg Med Chem Lett 19:2844\u0026ndash;2851. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bmcl.2009.03.109\u003c/span\u003e\u003cspan address=\"10.1016/j.bmcl.2009.03.109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Campa C, Gonz\u0026aacute;lez A, Mediavilla MD, et al (2009) Melatonin inhibits aromatase promoter expression by regulating cyclooxygenases expression and activity in breast cancer cells. Br J Cancer 101:1613\u0026ndash;1619. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sj.bjc.6605336\u003c/span\u003e\u003cspan address=\"10.1038/sj.bjc.6605336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham The H, Gonz\u0026aacute;lez-\u0026Aacute;lvarez I, Bermejo M, et al (2011) In Silico Prediction of Caco-2 Cell Permeability by a Classification QSAR Approach. Mol Inform 30:376\u0026ndash;385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1002/minf.201000118\u003c/span\u003e\u003cspan address=\"10.1002/minf.201000118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14:251\u0026ndash;264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1008130001697\u003c/span\u003e\u003cspan address=\"10.1023/A:1008130001697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva, H. H. M., Alves, M. H. P., Gon\u0026ccedil;alves, R. J. S., Pereira, N. de A., \u0026amp; Affonso CRG (2023) Molecular modification proposal: analysis of physicochemical, pharmacokinetic and toxicological parameters \u0026ldquo;in silico\u0026rdquo; of barbituric drugs\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Yanagisawa K, Yoshikawa Y, et al (2022) Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning. Bioinformatics 38:1110\u0026ndash;1117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btab726\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btab726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowman CM, Benet LZ (2018) An examination of protein binding and protein-facilitated uptake relating to in vitro-in vivo extrapolation. European Journal of Pharmaceutical Sciences 123:502\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejps.2018.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ejps.2018.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahabadi. AMN (2024) Volume of Distribution. StatPearls Publishing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Yang H, Wu Z, et al (2018) In Silico Prediction of Blood\u0026ndash;Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods. ChemMedChem 13:2189\u0026ndash;2201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1002/cmdc.201800533\u003c/span\u003e\u003cspan address=\"10.1002/cmdc.201800533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVistoli G, Pedretti A, Testa B (2008) Assessing drug-likeness - what are we missing? Drug Discov Today 13:285\u0026ndash;294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drudis.2007.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.drudis.2007.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekhar KC, Syed R, Golla M, et al (2014) Novel heteroaryl phosphonicdiamides PTPs inhibitors as anti-hyperglycemic agents. DARU, Journal of Pharmaceutical Sciences 22:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40199-014-0076-3\u003c/span\u003e\u003cspan address=\"10.1186/s40199-014-0076-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpina E, de Leon J (2016) What is the role of CYP genotyping in psychiatry ? Evidence-based Psychiatric Care 2:108\u0026ndash;122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTangboriboon N (2019) Chap. 15 - Carbon and Carbon Nanotube Drug Delivery and Its Characterization, Properties, and Applications. In: Mohapatra SS, Ranjan S, Dasgupta N, et al (eds) Nanocarriers for Drug Delivery. Elsevier, pp 451\u0026ndash;467\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchiebel J, Gaspari R, Wulsdorf T, et al (2018) Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes. Nat Commun 9:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-018-05769-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-018-05769-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotiejunas D, Wade RC (2007) Structural, Energetic, and Dynamic Aspects of Ligand\u0026ndash;Receptor Interactions. In: Taylor JB, Triggle DJ (eds) Comprehensive Medicinal Chemistry II. Elsevier, Oxford, pp 193\u0026ndash;213\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatil R, Das S, Stanley A, et al (2010) Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. PLoS One 5:e12029. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0012029\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0012029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelley JW (2012) 2 - Structure and Properties of Biologic Molecules. In: Pelley JW (ed) Elsevier\u0026rsquo;s Integrated Review Biochemistry (Second Edition), Second Edi. W.B. Saunders, Philadelphia, pp 7\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulthaupt HA, Yoneda A, Whiteford JR, et al (2009) Syndecan signaling: when, where and why? J Physiol Pharmacol 60 Suppl 4:31\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoo B-K, Jung YS, Shin J, et al (2006) Structural Basis of Syndecan-4 Phosphorylation as a Molecular Switch to Regulate Signaling. J Mol Biol 355:651\u0026ndash;663. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.jmb.2005.09.087\u003c/span\u003e\u003cspan address=\"10.1016/j.jmb.2005.09.087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulthaupt HA, Yoneda A, Whiteford JR, et al (2009) Syndecan signaling: when, where and why? J Physiol Pharmacol 60 Suppl 4:31\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller-Pinter A, Gyulai-Nagy S, Becsky D, et al (2021) Syndecan-4 in tumor cell motility. Cancers (Basel) 13:1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers13133322\u003c/span\u003e\u003cspan address=\"10.3390/cancers13133322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshino M, Murakami K (2009) A graphical method for determining inhibition constants. J Enzyme Inhib Med Chem 24:1288\u0026ndash;1290. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/14756360902829766\u003c/span\u003e\u003cspan address=\"10.3109/14756360902829766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Brevern AG Recent Developments on Protein-Ligand Interactions From Structure, Function to Applications\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilgueiras LA, de Andrade F das CP, Iwao Horita S, et al (2024) Analysis of SIKVAV\u0026rsquo;s receptor affinity, pharmacokinetics, and pharmacological characteristics: a matrikine with potent biological function. J Biomol Struct Dyn 1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07391102.2024.2313709\u003c/span\u003e\u003cspan address=\"10.1080/07391102.2024.2313709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAier I, Varadwaj PK, Raj U (2016) Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep 6:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep34984\u003c/span\u003e\u003cspan address=\"10.1038/srep34984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Guo J, Lian J, et al (2021) Molecular Simulation Study on the Interaction between Tyrosinase and Flavonoids from Sea Buckthorn. ACS Omega 6:21579\u0026ndash;21585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsomega.1c02593\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.1c02593\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRampogu S, Lee G, Park JS, et al (2022) Molecular Docking and Molecular Dynamics Simulations Discover Curcumin Analogue as a Plausible Dual Inhibitor for SARS-CoV-2. Int J Mol Sci 23:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23031771\u003c/span\u003e\u003cspan address=\"10.3390/ijms23031771\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma J, Kumar V, Singh R, Rajendran V (2020) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19. The COVID-19 resource centre is hosted on Elsevier Connect, the company \u0026rsquo; s public news and information. Food Chem\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNovak J, Tseilikman VE, Tseilikman OB, et al (2023) Can Resveratrol Influence the Activity of 11β-Hydroxysteroid Dehydrogenase Type 1? A Combined In Silico and In Vivo Study. Pharmaceuticals 16:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ph16020251\u003c/span\u003e\u003cspan address=\"10.3390/ph16020251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira GRC, De Azevedo Abrahim Vieira B, De Mesquita JF (2021) Comprehensive in silico analysis and molecular dynamics of the superoxide dismutase 1 (SOD1) variants related to amyotrophic lateral sclerosis. PLoS One 16:1\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0247841\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0247841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagewadi ZK, Yunus Khan TM, Gangadharappa B, et al (2023) Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach. Saudi J Biol Sci 30:103753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.sjbs.2023.103753\u003c/span\u003e\u003cspan address=\"10.1016/j.sjbs.2023.103753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadushanka A, Moura RT, Verma N, Kraka E (2023) Quantum Mechanical Assessment of Protein\u0026ndash;Ligand Hydrogen Bond Strength Patterns: Insights from Semiempirical Tight-Binding and Local Vibrational Mode Theory. Int J Mol Sci 24:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms24076311\u003c/span\u003e\u003cspan address=\"10.3390/ijms24076311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdhav VA, Saikrishnan K (2023) The Realm of Unconventional Noncovalent Interactions in Proteins: Their Significance in Structure and Function. ACS Omega 8:22268\u0026ndash;22284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsomega.3c00205\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.3c00205\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Greene D, Xiao L, et al (2018) Recent developments and applications of the MMPBSA method. Front Mol Biosci 4:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmolb.2017.00087\u003c/span\u003e\u003cspan address=\"10.3389/fmolb.2017.00087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449\u0026ndash;461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1517/17460441.2015.1032936\u003c/span\u003e\u003cspan address=\"10.1517/17460441.2015.1032936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Laminin-111 derivated, AG73 peptide, Syndecan 4, Cell Adhesion","lastPublishedDoi":"10.21203/rs.3.rs-6090672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6090672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSyndecan-4 is widely expressed across tissues and is particularly relevant in cancer, where it modulates cell adhesion, invasion, and metastasis. The AG73 peptide is known to interact with syndecans, influencing cell adhesion and migration. In this study, we employed molecular docking and dynamics simulations to investigate the interaction between AG73 and Syndecan-4 cytoplasmic domain. Our findings suggest that AG73 binds to Syndecan-4 through hydrogen bonding and electrostatic interactions, stabilizing its structure and potentially enhancing PKCα activation. Pharmacokinetic and ADMET predictions indicate that AG73 has poor oral bioavailability but could be optimized through alternative delivery strategies. These insights contribute to a better understanding of Syndecan-4-mediated signaling in cancer and highlight AG73 as a potential modulator for therapeutic applications.\u003c/p\u003e","manuscriptTitle":"Molecular Insights into AG73@Syndecan-4 Interactions: Implications for Cancer Cell Adhesion, Migration, and Therapeutic Potential","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 16:42:48","doi":"10.21203/rs.3.rs-6090672/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"034401de-4bec-4900-b4be-936b1befa2e1","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T08:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 16:42:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6090672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6090672","identity":"rs-6090672","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}