Revolutionizing Diabetes Treatment: Computational Insights into 4- Hydroxy isoleucine Derivatives and Advanced Molecular Screening for Anti-Diabetic Compounds

Revolutionizing Diabetes Treatment: Computational Insights into 4- Hydroxy isoleucine Derivatives and Advanced Molecular Screening for Anti-Diabetic Compounds | 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 Revolutionizing Diabetes Treatment: Computational Insights into 4- Hydroxy isoleucine Derivatives and Advanced Molecular Screening for Anti-Diabetic Compounds Lakshmi Mounika Kelam, Manjinder Singh Gill, M. Elizabeth Sobhia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4575416/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background: The present study focuses on the importance of, a peculiar nonprotein amino acid 4-hydroxy isoleucine (4OHIle) as a constituent isolated from fenugreek (Trigonella foenum-graecum), which plays a vital role in the treatment of Diabetes mellitus. Here, we represent 4-hydroxy isoleucine derivatives has the ability to simulate insulin production and increase insulin sensitivity in diabetes mellitus targets. Objectives In this study, using computational methods we search for suitable drug like compounds that have similar ligand binding kinetics to target diabetes mellitus. Methods The top drug like compounds are selected based on computational methods such as Molecular Docking, Molecular Dynamic Simulation, Gibbs Free Energy calculations and Free Energy Landscape (FEL), shape based generative modelling for de novo drug design. Results Docking-based simulation screened out best 2 compounds against each targeted enzyme implicated in diabetes. Further, their dynamics studies reveal that the compounds 4-OHIL, 4-OHIL-4, 2R-3S-4R-4OHIL and 4-OHIL-Amide-2 were affirmed as the best inhibitors of respective enzyme targets. The best inhibitors are further optimised using generative model (Ligdream) Conclusion Anticipating the competitive inhibition of target protein expression in diabetes mellitus, we envision that the best inhibitors of respective enzyme targets. The findings from this current investigation carry significant modifications for the advancement in order to improve their potential to treat type 2 diabetes Diabetes Alpha Glucosidase Alpha amylase Aldose reductase inhibitors docking Molecular dynamics Binding free energy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Diabetes Mellitus is marked by an elevation in glucose levels in the blood, a chronic metabolic disease in which uncontrolled levels are associated with the many chronic complications that cause damage to the eyes, heart, and renal system [ 1 ]. However, there are four distinct types of diabetes; the two most common are insulin-dependent type 1 diabetes mellitus and non-insulin-dependent type 2 diabetes mellitus [ 2 , 3 ]. Globally, non-insulin-dependent diabetes mellitus is becoming more common, caused by insulin response dysfunction, referred to as sensitivity to insulin due to impaired insulin secretion or insensitivity on the part of insulin receptors that comes with insulin-dependent type 1 diabetes mellitus is associated with a complete absence of insulin secretion [ 4 ]. There is a 90% vast increase in diabetes cases worldwide caused mainly by T2DM. Consuming more red and processed meat, sugar-sweetened beverages, and refined carbohydrates is a sign of a diet connected with poor health results. Diabetes and its associated complications lend a huge worldwide health threat. Based on the most recent data, 643 million people between the ages of 20 to 79 years are expected to have diabetes by the year 2030, a sharp increase from the present projection of 537 million. In 2021 diabetes caused 6.7 million deaths, or one every five seconds [ 5 ]. Over the past 15 years, almost 966 billion USD in expenditures have been documented. Despite the fact that there are many therapeutic option various therapeutic options are in the market, many patients still do not reach their blood glucose levels [ 6 , 7 ]. Various efforts have been made to keep glucose normal in T2DM management through non-pharmacological interventions in recent years utilizing oral medications such as sulphonylureas, biguanides, meglitinides, thiazolidinediones, and alpha-glucosidase. Thus, it is possible to prevent its likely complications, such as retinopathy, neuropathy, and nephropathy. The elevated glucose levels can be regulated to the optimal level by classes of these drugs such as oral hypoglycemic agents, by various mechanisms, or by inhibition of the target enzymes, such as Alpha Glucosidase, Alpha-amylase, and Aldose reductase as a result of diabetes retinopathy [ 8 ] The continuous breakdown of disaccharides (cleaving α-1,4-glycopyranosidic linkage) to produce simple sugars such as Alpha glucose during the post-prandial hyperglycaemic state [ 9 , 10 ]. The polyol pathway is enriched by the aldose reductase enzyme, which catalyses nicotinamide adenosine dinucleotide phosphate-dependent reduction of glucose to sorbitol, resulting in an excessive buildup of intracellular reactive oxygen species (ROS) in numerous tissues of diabetes, such as the heart, vasculature, eyes, kidneys, and neurons [ 11 ]. The digestion of polysaccharide molecules, including glucose and maltose, is facilitated by the calcium metalloenzyme Alpha-amylase [ 12 ]. The diverse functions of essential enzymes, including alpha-glucosidase, alpha-amylase, and aldose reductase, have been connected with the DM processes [ 13 , 14 ]. Therefore, these enzymes have been a target for treating diabetes mellitus since it is crucial for the emergence of DM. Although many synthetic drugs under study were recently excluded from clinical studies, they have shortcomings ranging from poor pharmacokinetics to severe side effects, including weight gain, chronic tissue damage, and limited efficacy due to a lack of effectiveness and several undesirable effects [ 15 ]. The study of medicinal plants as anti-diabetic medicines represents an emergent paradigm change. It has been recognized that natural products and their derivatives are sources of therapeutic substances with various structural properties [ 16 ]. There is a lot of work being done on 4-hydroxy isoleucine due to its diverse pharmaceutical uses as an insulinotropic, anti-dyslipidaemia, and hyperglycaemic agent [ 17 , 18 ]. The first instance of 4-hydroxy isoleucine as a free acid was found in fenugreek seeds [ 19 , 20 ]. 4-OHIL conformations, dipeptides, and its amide derivatives have been found to encourage glucose absorption by skeletal muscle cells in a dose-dependent manner [ 20 ]. Regarding its effects on specific enzymes, 4-OHIL has been found to have an anti-diabetic effect, showing non-insulin-dependent action against alpha-glucosidase and alpha-amylase [ 21 ]. It has also been shown to have aldose reductase inhibitory activity [ 22 , 23 ]. In summary, 4-hydroxy isoleucine derivatives from fenugreek seeds have been found to have anti-diabetic effects, including improving insulin resistance and inhibiting alpha-glucosidase, alpha-amylase, and alpha-reductase. In the current study, we aim to identify high-affinity and potent inhibitors for Diabetes mellitus through docking investigations. Moreover, we employed Molecular Dynamics Simulations (MDS) and MMGBSA, along with Principal Component Analysis (PCA) and Free Energy Landscape (FEL) Studies, to obtain more precise understanding of the changes in protein-ligand interaction. By using this comprehensive approach, we can examine understand the molecular basis of inhibitor interactions with targets at the atomic level, which enable us calculate conformational changes and dynamic characteristics. The primary objective of our current research is to find a potent inhibitor that can bind to the diabetes targets. The most effective inhibitors were identified among the 23 compounds analysed. Methods 2.1 System Configuration The computational studies presented in this study were conducted using the Schrödinger Drug Discovery Suite (New York) Software and GROMACS 2022.3 suite, a Linux-based workstation, both installed on a DELL LINUX ENTERPRISE version 8.0 system equipped with an i7 processor and 64 GB of RAM [ 24 – 26 ]. 2.2 Protein & Ligand Preparation The 3D structure of the human form alpha-glucosidase, aldose reductase, and alpha-amylase was retrieved from the Protein Data Bank ( https://www.rcsb.org ; accessed on 26th June 2023) with the PDB ID of 5NN8, 4QX4, 4GQR (Fig. 1 ) and with a resolution of 2.45 Å,1.26 Å, 1.20 Å respectively. The protein preparation was carried out with the help of a Protein Preparation Wizard (Schrӧdinger, Inc., LLC, New York, USA) in the Schrodinger suite ( www.schrodinger.com ). The missing side chains and loops in the protein structure were filled with the help of the Prime (Schrödinger v12.3). The crystallographic water molecules and other interfering ligands were deleted, hydrogen molecules were included and formal charges along with bond orders were assigned during the protein preparation step. All other unwanted residues were removed. The protein structure was energy minimized and then was used in the various calculations. Ligand preparation was performed using the 23 molecules through the Ligprep module (Schrӧdinger, Inc., LLC, New York, USA). This pupose of this process was to enhance optimization, turn 2D structures into 3D ring conformers, and establish ionization and tautomer states, while being influenced by the OPLS-2005 force field, at a pH range of 7 ± 2 [ 27 ]. 2.3 Receptor Grid Generation and docking We selected the centroid XYZ coordinates of the co-crystal ligand within the receptor to create the receptor grid using the Receptor Grid Generation Glide module (Schrӧdinger, Inc., LLC, New York, USA). Default settings of the OPLS-2005 force field were applied, along with a Van der Waals radius scaling factor of 1Å and a partial charge cutoff within the range of 0.25 Å. Subsequently, the ligands were docked into the generated grid to validate the protein and ligand interactions. This step was performed using Glide XP (Schrӧdinger, Inc., LLC, New York, USA). 2.4 Binding Characterization A 2-D interaction diagram is often used to visualize and analyse the intermolecular interactions between a protein and ligand or small molecule compounds in studying binding mechanisms. The selected compounds or ligands are also represented in a simplified 2-D manner, usually as molecular structures or formulas. This representation helps identify the chemical properties and features of the ligands that are important for binding. Hydrogen bonds help identify molecular recognition and binding. In the interaction diagram, hydrogen bond interactions are represented by dashed lines between the donor and acceptor atoms involved in the hydrogen bond formation. Hydrophobic interactions are nonpolar regions or clusters within the protein and ligand structures. We can identify the critical amino acid residues in the protein that form hydrogen bonding with the ligands or observe hydrophobic regions where nonpolar ligand groups may interact favourably. 2.5 Validation of a Docking Protocol It is a crucial step to ensure its reliability and accuracy in predicting protein-ligand interactions. Metrics such as root mean square deviation (RMSD) and root mean square fluctuation (RMSF) can be determined to compare the similarity of predicted and experimental complexes [ 28 ]. It allows for determining the docking protocol’s ability to discriminate between ligands and correctly rank their binding affinities. MD simulation study MD simulation was performed to get insights into binding stability properties, protein compactness, and interactions; the top molecular complexes were subjected to simulation for 100ns using GROMACS 2022.3 suite ( http://www.gromacs.org ), a Linux-based workstation [ 29 ]. CHARMM27 all-atom force field was applied with TIP3P water to the complex and generated the topology of the complex. Under periodic boundary conditions, a cubic solvation box was employed, with a width of 10 Å distance between the box's edge and protein surface. The right amount of Na + and Cl- ions were added to neutralize the complexes. Energy minimization was done as the initial step, ensuring that our starting structure had an appropriate geometry and solvent orientation. Before starting actual dynamics, we must balance the solvent and ions around the protein. Two steps are commonly taken during equilibration. An NVT ensemble (fixed number of particles, volume, and temperature) performs the initial stage. Here, the system was equilibrated for 100 ps at 100k. It is obvious that the temperature of the system quickly arrives at the desired value (300k) and remains steady throughout the equilibration [ 28 ]. The second phase is completed in an NPT ensemble where particle number, pressure, and temperature remain constant. The system was also equilibrated for 100ps at 100k, with an average pressure of 12.62 ± 117.2 bar; it was evident that the system's pressure quickly arrived at the target value of 1 bar and remained steady throughout the equilibration. A density study was also done after pressure progression using energy. The typical value over 100 Ps is 1015 ± 2.3 kg m-3, not far from the experimental weight of 1000 kg m-3. 2.6. MM-GBSA- free Binding Energy Calculation Docking studies aren't always regarded as the most reliable or effective procedure. As a result, for lead hit detection, a more precise computational technique is necessary. The Molecular mechanics/generalized Born surface area (MM-GBSA) approach was used to estimate free binding energies for the best hit docked complexes utilizing MM force fields and implicit solvation. Prime MM-GBSA (Molecular Mechanics Generalized Born Surface Area) is a combination of three terms: MME (Molecular Mechanics Energies), SGB (Solvation model for polar solvation), and GNP (Non-Polar Solvation) term composed of nonpolar solvent accessible surface area and van der Waals interactions. The general formula for calculating total binding free energy is expressed as: [ 30 ] ∆G bind = G Complex – (G Ligand + G Receptor ) Where G = MME (molecular mechanics energies) + GSGB (SGB solvation model for polar solvation) + GNP (nonpolar solvation) Using frames recovered from MD simulation trajectories, the Prime MM-GBSA method was utilized to calculate the free energy of binding of each receptor-ligand complex. In the Molecular dynamics simulation step, a total of 1000 frames were calculated for each complex. One frame was extracted from each simulation trajectory at 1ns intervals (i.e. 100 frames for each simulation) to compute the MM-GBSA binding free energy using in-house Schrödinger Prime scripts and the VSGB 2.0 solvation model. 2.7 Trajectory analysis A system stability assessment was carried out after conducting MD simulations and it involved different methods and criteria, which involved analysing trajectories throughout the simulation period. The protein backbone's RMSD (Root Mean Square Deviation) was monitored during this analysis. The RMSD values that are acceptable for stability in MD simulations can differ depending on the system, but the threshold is generally around 2–3 Å and also the evaluation included assessing the RMSF (Root Mean Square Fluctuation) of distinct residues. Furthermore, the evaluation included the number of hydrogen bonds within inter-chain residues of the complex and occupancy of hydrogen bonds. 2.8 FEL & PCA Analysis To understand the importance of analysing the dynamic properties and changes in molecular systems, it is crucial to use Free Energy Landscape (FEL) and Principal Component Analysis (PCA). Our studies used PCA to identify key motion patterns and FEL analysis to visualize energy landscapes. Using both covariance matrix analysis and Gibbs free energy landscapes provides a complete framework to explore the compounds' dynamic patterns and structural energies, providing a clearer understanding of crucial molecular interactions and conformational changes that are of utmost importance in drug discovery and molecular dynamics. The approach employed in the study involves extracting coordinated movements from all trajectories to construct a covariance matrix. This analysis was done using the g-cover and g-analog modules, which were used to analyze selected carefully screened compounds and reference inhibitors. This process generated A set of eigenvectors by diagonalizing the covariance matrix. By utilizing these eigenvectors, we can acquire valuable insights into the energetic influence of each component on the molecular motions observed. We performed Gibbs free energy landscape (FEL) calculations to gain deeper understanding of the dynamic behavior of the studied system. By Utilizing the sham function in GROMACS, the predictions of the top two principal components were employed. This analysis provided a visualization and understanding of the energy landscapes that control conformational changes and interactions within the molecular systems under study [ 31 ]. Results 3.1. Docking Validation Docking studies provide valuable insights into the mechanism of interactions between drug and proteins. To confirm the accuracy, we repeated the docking of the co-crystallized inhibitor within its initial structure. Remarkably, this redocking process resulted in a low RMSD value of 0.2–0.4 Å. The docking outcomes were thoroughly examined, focusing on the binding conformation and interaction analysis detailed in (Supplementary Table 1, 2, 3). 3.2 Docking studies The docking studies involved diverse conformers of 4-hydroxyisoleucine and its mimetics to assess their binding affinity with key diabetes targets. These investigations identified the top 2 ligands for each target out of a total of 23 ligands based on their docking score and interactions resembling those of the respective target co-crystal ligands. The chosen compounds demonstrated substantial interactions with the key active site residues; analysing these detailed binding interactions and evaluating the therapeutic potential of the top-selected compounds can help guide further exploration and development of these promising drug candidates. This in-depth understanding of the compound-target interactions is crucial for assessing the viability of these compounds for continued drug development efforts. Upon docking studies, it was revealed that out of 23 ligands used in the study (supplementary material Table 2), ligand 4-OHIL-4 has the best docking score of -8.3 kcal/mol. As depicted from Fig. 1 (2b) ligand, 4-OHIL-4 shows many hydrogen bond interactions, salt bridge, and pi-pi stacking interactions with the target alpha glucosidase protein. Pi-pi stacking is at the distance of 5.13Å with the residue PHE649; hydrogen bonding interactions with the residues ASP404, ASP518, ARG600, HIE674, and LEU678 within the space of 1.94 Å, 1.65 Å, 2.12 Å, 2.22 Å, 2.54Å respectively. Compared with the co-crystal ligand Fig. 1 (1a), acarbose shows additional interaction. The amine group of the ligand shows pi-pi interaction at 5.13 Å with the aromatic ring of PHE649—two salt bridge formations with ASP518 and ASP616 at distances of 3.07 and 4.73 Å, respectively. The co-crystalized ligand of the target alpha-amylase Fig. 1 (2a) shows respective hydrogen bonds and pi-pi stacking interactions with the protein. Pi-pi stacking is at the distance of 3.85 Å with the residue TRP59; hydrogen bonding interactions with the residues GLN63, ASP197, and GLH233 within the space of 2.1, 1.5, 2.1, and 2.6 Å, respectively. 2R-3S-4R-4OHIL shows additional pi-cation and salt bridge interactions compared with the co-crystal ligand. The amine group of the ligand shows pi-cation interaction at 6.3Å with the aromatic ring of TYR62. Two salt bridge formations with ASP300 at 3.5 and 3.6 Å. The amino and carboxy groups of VAL-L-4-OHIL form salt bridges with negative and positively charged residues ASP197 and ARG195 of 2.68 and 4.76 Å. These results highlight the potential of these compounds as promising candidates for further investigation in drug development efforts. The co-crystal ligand of aldose reductase (4QX4) forms an essential Hydrogen bonding interaction with the polar and hydrophobic residues Fig. 1 (3a) HIE110, TRP111, TYR48, LEU300, and aromatic hydrogen bond with the VAL47. Compared with the co-crystal ligand, the 4-OHIL amide − 2 w a better binding affinity of -7.6 kcal/mol. 3.3 MM-GBSA The post – molecular docking analysis, which is essential for determining the stability of receptor-ligand complexes, was computed using the Prime MM-GBSA approach. Recent studies have shown that docking score findings can be consistently verified using the computation of binding free energy. The calculations for the energy required to bind are expressed as ΔG. In mathematical terms, a stable protein-ligand complex is characterized negative values of the Gibbs free energy (ΔG), while incorrect docking outcomes are identified by positive ΔG values. The six derivatives of the 4-OHIL formed a stable complex with Alpha-glucosidase, Alpha-amylase, and Aldose reductase. 4-OHIL-Amide-2 ,4-OHIL -Amide-3, 4-OHIL-4, 4-OHIL-5, 4-OHIL-6, 4-OHIL-7 exhibited binding free energy of -64.3Kcal/mol, -45.7Kcal/mol, -49.6Kcal/mol, -66.6Kcal/ mol, -55.4Kcal/mol and − 60.0 Kcal/mol (Fig. 2 ). 3.4 Molecular Dynamic Analysis The stability of interactions and conformations within physiological environments can be assessed by using molecular dynamic simulations. To evaluate the structural and flexibility properties of the top protein-ligand complexes and the reference compound for each antidiabetic target underwent a were 100 ns molecular dynamics simulation. The conformational and interaction stability of receptor-ligand complexes were assessed using root mean square deviation (RMSD) and root mean square fluctuation (RMSF). Notably, complexes involving 4-OHIL-4 and 4-OHIL of target Alpha Glucosidase(5NN8), 2R-3S-4R-4OHIL and 4-OHIL of target alpha-amylase (4GQR), 4-OHIL-Amide-2 and 4-OHIL of target aldose reductase (4QX4) exhibited remarkable stability RMSD when compared to known compounds. RMSD values of all the above-mentioned complexes fell between 0.2 to 0.4nm. RMSF analysis demonstrated minimal variation in residues across all complexes (Fig. 3 ), highlighting their constant stability. 3.5 Hydrogen Bonding Analysis The stabilization of the protein-ligand complex is greatly aided by hydrogen bonding. Figure 4 shows the number of hydrogen bonds formed in complexes during the last 100 ns trajectory. The binding affinity between the protein and ligand greatly influenced by their polar interactions. The occupancy percentages for hydrogen bonds indicate how many trajectories were involved in the formation of hydrogen bonds during a simulation. In the case of the Alpha Glucosidase ligand, 4-OHIL-4 shows many hydrogen bond interactions, with the specific residues ASP404, ASP518, ARG600, HIE674, and LEU678 within the space of 1.94 Å, 1.65 Å, 2.12 Å, 2.22 Å, 2.54Å respectively. Hydrogen bonding interaction with the co-crystal ligand characteristic residues ASP404, ASP518, ARG600, HIE674, and LEU678 within a range of 2.5 Å. The co-crystallized form of alpha-glucosidase contained 23 hydrogen bonds, in which the ligand shows the occupancy rate with Asp 616 and Asp 282 at 76.13% and 76.95%. Meanwhile, the 4-OHIL-4 exhibited a higher occupancy rate than the co-crystal ligand. The co-crystalized ligand of the target alpha amylase shows hydrogen bonding interactions with the residues GLN63, ASP197, and GLH233 within the space of 2.1, 1.5, 2.1, and 2.6 Å, respectively, 2R-3S-4R-4OHIL shows Hydrogen bonding interaction with characteristic residues ASP197, ARG195, GLH233, ASN 298 within a range of 2 Å, VAL-L-4-OHIL forms hydrogen bonding interaction at 1.7, 1.7, 1.8,1.9, 1.7 Å with characteristic residues such as ASP197, ARG195, GLH233, HIE299, ASP300. The co-crystallized form of alpha-amylase contained 32 hydrogen bonds, in which the ligand shows the occupancy rate with the Asp 300, Glu 233, and Asp 356 with 26.94%, 8.91%, and 11.05%. At the same time, the 2R-3S-4R-4OHIL was found to show a higher occupancy rate with the Asp 300, Glu 233, and Asp 356 with 30.77%, 20.26%, and 24.04% Compared with the co-crystal ligand. The co-crystal ligand of aldose reductase forms an essential Hydrogen bonding interaction with the polar and hydrophobic residues HIE110, TRP111, TYR48, LEU300, and aromatic hydrogen bond with the VAL47. The presence of significant number of hydrogen bonds with various target protein-ligand complexes indicates these are suitable inhibitors. It is possible that these residues could have a significant impact on the interaction between the protein-ligand complex. Specific residues involved in molecular interactions can be identified by analyzing hydrogen bond interactions. 3.6 Free energy landscape Gmx Covar, Gmx Anaeig, and Gmx Sham were utilized to calculate the Gibbs free energy landscape, using projections from their own first (PC1) and second (PC2) eigenvectors. In (Fig. 5 ), the Gibbs free energy landscape is shown in a color-coded manner. Trajectories are used to analyze the direction of fluctuation for all C atoms in complex structures is investigated by the Gibbs free energy landscape through the use of trajectories. Lower energy is indicated by a deeper blue colour corresponds to the free energy contour map. After the binding of these compounds, the primary free energy in the global free energy region was found to have undergone a complete change. These molecules stable conformational states are strongly indicated by these free energies. Different global minima of various targets are observed during the 100ns of MD simulations due to the binding of ligands to the respective targets. A small energy barrier separates the metastable conformational states from several distinguishable minima on the energy landscape. The most metastable conformational states were seen in the binding in case of (Alpha Glucosidase )5NN8 with 4-OHIL and 5NN8 with 4-OHIL-4, (Alpha amylase) 4GQR with 4-OHIL, 4GQR with 2R-3S-4R, (Aldose Reductase) 4QX4-4-OHIL, 4QX4-4OHIL - Amide 2 in which local minima were distributed to about three to four regions within the energy landscape. The above-mentioned complexes formed just two to three metastable conformations during the whole trajectories. 4. Shape- Based Generative Modeling for de Novo Drug Design Generative modelling provides an alternative approach to molecular discovery by reformulating molecular design as an inverse design problem. The number of compounds that have ever been synthesized lies around 10^8 while the total number of theoretically feasible compounds lies between 10^23 and 10^60. Conventional discovery methods are only capable of exploring a small fraction of chemical space. By identifying a function that maps a set of structures, generative models can rapidly identify diverse sets of molecules that are highly optimised for specific application. Generative modelling can be designed by sequence based and shape based. The sequence based are more biased towards the design of compounds obtainable by small molecule chemical modifications. The shape based provide generative novel scaffold inspired from structure-based design (spatial information is considered). Shape variational encoder using convolutional neural network to autoencoder compound representation, Combination of CNN and long short-term memory network to generate smile string. Variational autoencoder is a type of neural network that encodes shapes, create puzzles and decodes. Training, Featurization and Model Training (Ligdream) Canonical smiles notations were collected from Zinc 15 database, by using the RDkit random conformer generated, optimization of 3D structures using MMFF94 forcefield, random splitting into training and test sets, protein targets taken from DUDE database, docking. This approach has some challenges such as performance drops as the target sequence gets longer, H-bond donars and Hbond acceptors are difficult to recover as these properties are not specifically marked and are mainly dependent on its surrounding. This approach is applied to the top obtained molecules against each diabetes target, generative structures are subjected to further docking process against alpha glucosidase, alpha amylase, aldose reductase target shown in supplementary data (Table 4, Fig. 1 , 2 , 3 )[ 32 , 33 ]. Conclusion The study underscores the significance of 4-hydroxyisoleucine (4-OHIL) derived from fenugreek in the treatment of Diabetes mellitus by enhancing insulin production and sensitivity. Based on the computational analysis and discussions, including docking study, interaction analysis, MD simulation, binding free energy, H-bond and FEL calculations, were identified promising drug-like compounds with similar ligand binding kinetics to diabetes mellitus targets. The top compounds, including 4-OHIL, 4-OHIL-4, 2R-3S-4R-4OHIL, and 4-OHIL-Amide-2 were identified as potent inhibitors against specific enzyme targets implicated in diabetes. Shape based screening of Molecules Satisfying Anti-diabetic property Using Generative Model. This study provides valuable insights into potential therapeutic avenues for managing diabetes the wider anti-diabetic research community by providing a platform for predicting small molecules with subsequent validation for drug discovery and development. Declarations Author Contributions All authors contributed equally. Acknowledgements The authors thank the National Institute of Pharmaceutical Education and Research (NIPER) SAS Nagar, Department of pharmaceuticals, Ministry of chemicals and Fertilizers, New Delhi, Government of India for providing the facility Funding No funding was received to assist with the preparation of this manuscript. Conflict of interest The author has no competing interest to declare that are relevant to the content of this article. Ethical approval Not applicable Consent to participate Not applicable Consent for publication Not applicable Author Contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis wereperformed by Ms.LMK. The first draft of the manuscript was written by Ms. LMK, and all authors commented on previous versions. All authors read and approved the final manuscript. References Arumugam G, Manjula P, Paari N. 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Schuler LD, Daura X, Van Gunsteren WF. An Improved GROMOS96 Force Field for Aliphatic Hydrocarbons in the Condensed Phase. J Comput Chem. 2001. Abraham MJ, Murtola T, Schulz R, et al. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25. Schrödinger L. Protein Preparation Wizard. Schrodinger Release. Schrodinger Release. 2023;2. Filipe HAL, Loura LMS. Molecular Dynamics Simulations: Advances and Applications. Molecules. MDPI; 2022. Van Der Spoel D, Lindahl E, Hess B, et al. GROMACS: Fast, flexible, and free. J Comput Chem. 2005. p. 1701–1718. Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. Informa Healthcare; 2015. p. 449–461. Khan MT, Khan A, Rehman AU, et al. Structural and free energy landscape of novel mutations in ribosomal protein S1 (rpsA) associated with pyrazinamide resistance. Sci Rep. 2019;9. Klambauer G, Hochreiter S, Rarey M. Machine Learning in Drug Discovery. J Chem Inf Model. American Chemical Society; 2019. p. 945–946. Bilodeau C, Jin W, Jaakkola T, et al. Generative models for molecular discovery: Recent advances and challenges. Wiley Interdiscip Rev Comput Mol Sci. John Wiley and Sons Inc; 2022. Additional Declarations No competing interests reported. Supplementary Files GRAPHICALABSTRACT.docx Supplemetary.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 05 Jul, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 13 Jun, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4575416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315719302,"identity":"9ffef3ee-982a-4e8f-a616-ad694fa0da11","order_by":0,"name":"Lakshmi Mounika Kelam","email":"","orcid":"","institution":"National Institute of Pharmaceutical Education and Research (NIPER), S. A.S. Nagar (Mohali)","correspondingAuthor":false,"prefix":"","firstName":"Lakshmi","middleName":"Mounika","lastName":"Kelam","suffix":""},{"id":315719306,"identity":"2bf51549-da16-4a11-95c2-704216f22725","order_by":1,"name":"Manjinder Singh Gill","email":"","orcid":"","institution":"National Institute of Pharmaceutical Education and Research (NIPER), S. A.S. Nagar (Mohali)","correspondingAuthor":false,"prefix":"","firstName":"Manjinder","middleName":"Singh","lastName":"Gill","suffix":""},{"id":315719308,"identity":"3d50c0ef-3091-4ff4-8822-94559c3e9c25","order_by":2,"name":"M. Elizabeth Sobhia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYLACxgYJBn4gCecSp0WygUQtDAwGB4h1k257+8PHhTss5I1vN7c9+LiHQZ6/gbntAT4tZmfOGBvPPCNhuO3OwXbDGc8YDGccYGw3wKvlRg6bNG+bBOO2G4lt0jwHGBg3MDC2SeDVcv/5M5AW+80zgFr+HGCwJ6zlBoMZSEviBgmgFoYDDImEtZzJAfqlTSJ5xo3EdsOeA0DGYUJajh8HhlhbnW3/jPRnD34csLHtb29/hlcLCDBDaTYglkBwidUyCkbBKBgFowATAAAVc0mLi7k7hQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Pharmaceutical Education and Research (NIPER), S. A.S. Nagar (Mohali)","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"Elizabeth","lastName":"Sobhia","suffix":""}],"badges":[],"createdAt":"2024-06-13 10:16:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4575416/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4575416/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59566628,"identity":"4cd9f9da-3f58-4080-b9e4-64c0ec84d008","added_by":"auto","created_at":"2024-07-03 09:20:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123747,"visible":true,"origin":"","legend":"\u003cp\u003eTwo dimension (2D), Interaction schemes of binding poses Alpha Glucosidase 1(a), 1(b), Alpha amylase 2(a), 2(b)and Aldose Reductase3(a), 3(b) were predicted. The dashed line with an arrow in the 2D diagram is denoted by magenta indicates the hydrogen bond between the ligand and the side chain atoms of the protein residue. The critical residues in the binding pocket are identified in 2D\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/aebae426ffcdc12522aa3c80.jpg"},{"id":59566630,"identity":"de792684-0bbe-4b22-8a0c-c1cb16a24f9a","added_by":"auto","created_at":"2024-07-03 09:20:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80251,"visible":true,"origin":"","legend":"\u003cp\u003eMM-GBSA method was used to predict the binding free energy between the targets and the derivatives of the 4-OHIL.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/c22bd5b0e7fca73a9f349370.jpg"},{"id":59566047,"identity":"b669df32-ce4a-4133-9738-75d4686994fd","added_by":"auto","created_at":"2024-07-03 09:12:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100222,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD and RMSF analysis of the ligands binding to the target Alpha amylase(5NN8), Alpha amylase(4GQR), Aldose reductase(4QX4)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/1425fd2e3f5c50bb39cbaeed.jpg"},{"id":59566045,"identity":"11069fac-0464-403a-b803-30f39628e6a8","added_by":"auto","created_at":"2024-07-03 09:12:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115979,"visible":true,"origin":"","legend":"\u003cp\u003eH-bond occupancy result of protein- ligand complex MD simulations study\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/210d8c60b306c97a5a971ae9.jpg"},{"id":59566050,"identity":"6f2e2521-26de-4d63-9fb9-672806ebc54e","added_by":"auto","created_at":"2024-07-03 09:12:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171828,"visible":true,"origin":"","legend":"\u003cp\u003eThe Gibbs free energy landscape. The Gibbs free energy landscape plot obtained during 100 ns MD simulations for (A), (B), (C)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/f2a1a1c60484b19cd797cfac.jpg"},{"id":59567525,"identity":"f7235512-aa03-4b47-a274-8c29e0d9f105","added_by":"auto","created_at":"2024-07-03 09:28:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1079118,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/ff366bfb-63af-4331-9b63-b10d0aca0575.pdf"},{"id":59566048,"identity":"a47ee037-095d-4027-a48a-fcfb0ed54c28","added_by":"auto","created_at":"2024-07-03 09:12:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":385985,"visible":true,"origin":"","legend":"","description":"","filename":"GRAPHICALABSTRACT.docx","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/ea596bd78525717a8bdff519.docx"},{"id":59566052,"identity":"7ea06fe5-5560-4ca6-8978-f269aa088208","added_by":"auto","created_at":"2024-07-03 09:12:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2505495,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemetary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4575416/v1/04cb3878dd47ce2c97433e5f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revolutionizing Diabetes Treatment: Computational Insights into 4- Hydroxy isoleucine Derivatives and Advanced Molecular Screening for Anti-Diabetic Compounds","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes Mellitus is marked by an elevation in glucose levels in the blood, a chronic metabolic disease in which uncontrolled levels are associated with the many chronic complications that cause damage to the eyes, heart, and renal system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, there are four distinct types of diabetes; the two most common are insulin-dependent type 1 diabetes mellitus and non-insulin-dependent type 2 diabetes mellitus [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Globally, non-insulin-dependent diabetes mellitus is becoming more common, caused by insulin response dysfunction, referred to as sensitivity to insulin due to impaired insulin secretion or insensitivity on the part of insulin receptors that comes with insulin-dependent type 1 diabetes mellitus is associated with a complete absence of insulin secretion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a 90% vast increase in diabetes cases worldwide caused mainly by T2DM. Consuming more red and processed meat, sugar-sweetened beverages, and refined carbohydrates is a sign of a diet connected with poor health results. Diabetes and its associated complications lend a huge worldwide health threat. Based on the most recent data, 643\u0026nbsp;million people between the ages of 20 to 79 years are expected to have diabetes by the year 2030, a sharp increase from the present projection of 537\u0026nbsp;million. In 2021 diabetes caused 6.7\u0026nbsp;million deaths, or one every five seconds [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Over the past 15 years, almost 966\u0026nbsp;billion USD in expenditures have been documented. Despite the fact that there are many therapeutic option various therapeutic options are in the market, many patients still do not reach their blood glucose levels [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious efforts have been made to keep glucose normal in T2DM management through non-pharmacological interventions in recent years utilizing oral medications such as sulphonylureas, biguanides, meglitinides, thiazolidinediones, and alpha-glucosidase. Thus, it is possible to prevent its likely complications, such as retinopathy, neuropathy, and nephropathy. The elevated glucose levels can be regulated to the optimal level by classes of these drugs such as oral hypoglycemic agents, by various mechanisms, or by inhibition of the target enzymes, such as Alpha Glucosidase, Alpha-amylase, and Aldose reductase as a result of diabetes retinopathy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe continuous breakdown of disaccharides (cleaving α-1,4-glycopyranosidic linkage) to produce simple sugars such as Alpha glucose during the post-prandial hyperglycaemic state [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The polyol pathway is enriched by the aldose reductase enzyme, which catalyses nicotinamide adenosine dinucleotide phosphate-dependent reduction of glucose to sorbitol, resulting in an excessive buildup of intracellular reactive oxygen species (ROS) in numerous tissues of diabetes, such as the heart, vasculature, eyes, kidneys, and neurons [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The digestion of polysaccharide molecules, including glucose and maltose, is facilitated by the calcium metalloenzyme Alpha-amylase [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diverse functions of essential enzymes, including alpha-glucosidase, alpha-amylase, and aldose reductase, have been connected with the DM processes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, these enzymes have been a target for treating diabetes mellitus since it is crucial for the emergence of DM. Although many synthetic drugs under study were recently excluded from clinical studies, they have shortcomings ranging from poor pharmacokinetics to severe side effects, including weight gain, chronic tissue damage, and limited efficacy due to a lack of effectiveness and several undesirable effects [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The study of medicinal plants as anti-diabetic medicines represents an emergent paradigm change. It has been recognized that natural products and their derivatives are sources of therapeutic substances with various structural properties [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a lot of work being done on 4-hydroxy isoleucine due to its diverse pharmaceutical uses as an insulinotropic, anti-dyslipidaemia, and hyperglycaemic agent [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The first instance of 4-hydroxy isoleucine as a free acid was found in fenugreek seeds [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 4-OHIL conformations, dipeptides, and its amide derivatives have been found to encourage glucose absorption by skeletal muscle cells in a dose-dependent manner [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Regarding its effects on specific enzymes, 4-OHIL has been found to have an anti-diabetic effect, showing non-insulin-dependent action against alpha-glucosidase and alpha-amylase [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It has also been shown to have aldose reductase inhibitory activity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In summary, 4-hydroxy isoleucine derivatives from fenugreek seeds have been found to have anti-diabetic effects, including improving insulin resistance and inhibiting alpha-glucosidase, alpha-amylase, and alpha-reductase. In the current study, we aim to identify high-affinity and potent inhibitors for Diabetes mellitus through docking investigations.\u003c/p\u003e \u003cp\u003eMoreover, we employed Molecular Dynamics Simulations (MDS) and MMGBSA, along with Principal Component Analysis (PCA) and Free Energy Landscape (FEL) Studies, to obtain more precise understanding of the changes in protein-ligand interaction. By using this comprehensive approach, we can examine understand the molecular basis of inhibitor interactions with targets at the atomic level, which enable us calculate conformational changes and dynamic characteristics. The primary objective of our current research is to find a potent inhibitor that can bind to the diabetes targets. The most effective inhibitors were identified among the 23 compounds analysed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 System Configuration\u003c/h2\u003e \u003cp\u003eThe computational studies presented in this study were conducted using the Schrödinger Drug Discovery Suite (New York) Software and GROMACS 2022.3 suite, a Linux-based workstation, both installed on a DELL LINUX ENTERPRISE version 8.0 system equipped with an i7 processor and 64 GB of RAM [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Protein \u0026amp; Ligand Preparation\u003c/h2\u003e \u003cp\u003eThe 3D structure of the human form alpha-glucosidase, aldose reductase, and alpha-amylase was retrieved from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 26th June 2023) with the PDB ID of 5NN8, 4QX4, 4GQR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and with a resolution of 2.45 Å,1.26 Å, 1.20 Å respectively. The protein preparation was carried out with the help of a Protein Preparation Wizard (Schrӧdinger, Inc., LLC, New York, USA) in the Schrodinger suite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.rcsb.org\" target=\"_blank\"\u003ewww.schrodinger.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.schrodinger.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The missing side chains and loops in the protein structure were filled with the help of the Prime (Schrödinger v12.3). The crystallographic water molecules and other interfering ligands were deleted, hydrogen molecules were included and formal charges along with bond orders were assigned during the protein preparation step. All other unwanted residues were removed. The protein structure was energy minimized and then was used in the various calculations. Ligand preparation was performed using the 23 molecules through the Ligprep module (Schrӧdinger, Inc., LLC, New York, USA). This pupose of this process was to enhance optimization, turn 2D structures into 3D ring conformers, and establish ionization and tautomer states, while being influenced by the OPLS-2005 force field, at a pH range of 7 ± 2 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Receptor Grid Generation and docking\u003c/h2\u003e \u003cp\u003eWe selected the centroid XYZ coordinates of the co-crystal ligand within the receptor to create the receptor grid using the Receptor Grid Generation Glide module (Schrӧdinger, Inc., LLC, New York, USA). Default settings of the OPLS-2005 force field were applied, along with a Van der Waals radius scaling factor of 1Å and a partial charge cutoff within the range of 0.25 Å. Subsequently, the ligands were docked into the generated grid to validate the protein and ligand interactions. This step was performed using Glide XP (Schrӧdinger, Inc., LLC, New York, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Binding Characterization\u003c/h2\u003e \u003cp\u003eA 2-D interaction diagram is often used to visualize and analyse the intermolecular interactions between a protein and ligand or small molecule compounds in studying binding mechanisms. The selected compounds or ligands are also represented in a simplified 2-D manner, usually as molecular structures or formulas. This representation helps identify the chemical properties and features of the ligands that are important for binding. Hydrogen bonds help identify molecular recognition and binding. In the interaction diagram, hydrogen bond interactions are represented by dashed lines between the donor and acceptor atoms involved in the hydrogen bond formation. Hydrophobic interactions are nonpolar regions or clusters within the protein and ligand structures. We can identify the critical amino acid residues in the protein that form hydrogen bonding with the ligands or observe hydrophobic regions where nonpolar ligand groups may interact favourably.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Validation of a Docking Protocol\u003c/h2\u003e \u003cp\u003eIt is a crucial step to ensure its reliability and accuracy in predicting protein-ligand interactions. Metrics such as root mean square deviation (RMSD) and root mean square fluctuation (RMSF) can be determined to compare the similarity of predicted and experimental complexes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It allows for determining the docking protocol’s ability to discriminate between ligands and correctly rank their binding affinities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMD simulation study\u003c/h2\u003e \u003cp\u003eMD simulation was performed to get insights into binding stability properties, protein compactness, and interactions; the top molecular complexes were subjected to simulation for 100ns using GROMACS 2022.3 suite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gromacs.org\u003c/span\u003e\u003cspan address=\"http://www.gromacs.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a Linux-based workstation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. CHARMM27 all-atom force field was applied with TIP3P water to the complex and generated the topology of the complex. Under periodic boundary conditions, a cubic solvation box was employed, with a width of 10 Å distance between the box's edge and protein surface. The right amount of Na + and Cl- ions were added to neutralize the complexes. Energy minimization was done as the initial step, ensuring that our starting structure had an appropriate geometry and solvent orientation. Before starting actual dynamics, we must balance the solvent and ions around the protein. Two steps are commonly taken during equilibration. An NVT ensemble (fixed number of particles, volume, and temperature) performs the initial stage. Here, the system was equilibrated for 100 ps at 100k. It is obvious that the temperature of the system quickly arrives at the desired value (300k) and remains steady throughout the equilibration [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe second phase is completed in an NPT ensemble where particle number, pressure, and temperature remain constant. The system was also equilibrated for 100ps at 100k, with an average pressure of 12.62 ± 117.2 bar; it was evident that the system's pressure quickly arrived at the target value of 1 bar and remained steady throughout the equilibration. A density study was also done after pressure progression using energy. The typical value over 100 Ps is 1015 ± 2.3 kg m-3, not far from the experimental weight of 1000 kg m-3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6. MM-GBSA- free Binding Energy Calculation\u003c/h2\u003e \u003cp\u003eDocking studies aren't always regarded as the most reliable or effective procedure. As a result, for lead hit detection, a more precise computational technique is necessary. The Molecular mechanics/generalized Born surface area (MM-GBSA) approach was used to estimate free binding energies for the best hit docked complexes utilizing MM force fields and implicit solvation. Prime MM-GBSA (Molecular Mechanics Generalized Born Surface Area) is a combination of three terms: MME (Molecular Mechanics Energies), SGB (Solvation model for polar solvation), and GNP (Non-Polar Solvation) term composed of nonpolar solvent accessible surface area and van der Waals interactions. The general formula for calculating total binding free energy is expressed as: [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e∆G \u003csub\u003ebind\u003c/sub\u003e = G \u003csub\u003eComplex\u003c/sub\u003e – (G \u003csub\u003eLigand\u003c/sub\u003e + G \u003csub\u003eReceptor\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eWhere G = MME (molecular mechanics energies) + GSGB (SGB solvation model for polar solvation) + GNP (nonpolar solvation)\u003c/p\u003e \u003cp\u003eUsing frames recovered from MD simulation trajectories, the Prime MM-GBSA method was utilized to calculate the free energy of binding of each receptor-ligand complex. In the Molecular dynamics simulation step, a total of 1000 frames were calculated for each complex. One frame was extracted from each simulation trajectory at 1ns intervals (i.e. 100 frames for each simulation) to compute the MM-GBSA binding free energy using in-house Schrödinger Prime scripts and the VSGB 2.0 solvation model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Trajectory analysis\u003c/h2\u003e \u003cp\u003eA system stability assessment was carried out after conducting MD simulations and it involved different methods and criteria, which involved analysing trajectories throughout the simulation period. The protein backbone's RMSD (Root Mean Square Deviation) was monitored during this analysis. The RMSD values that are acceptable for stability in MD simulations can differ depending on the system, but the threshold is generally around 2–3 Å and also the evaluation included assessing the RMSF (Root Mean Square Fluctuation) of distinct residues. Furthermore, the evaluation included the number of hydrogen bonds within inter-chain residues of the complex and occupancy of hydrogen bonds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.8 FEL \u0026amp; PCA Analysis\u003c/h2\u003e \u003cp\u003eTo understand the importance of analysing the dynamic properties and changes in molecular systems, it is crucial to use Free Energy Landscape (FEL) and Principal Component Analysis (PCA). Our studies used PCA to identify key motion patterns and FEL analysis to visualize energy landscapes. Using both covariance matrix analysis and Gibbs free energy landscapes provides a complete framework to explore the compounds' dynamic patterns and structural energies, providing a clearer understanding of crucial molecular interactions and conformational changes that are of utmost importance in drug discovery and molecular dynamics. The approach employed in the study involves extracting coordinated movements from all trajectories to construct a covariance matrix. This analysis was done using the g-cover and g-analog modules, which were used to analyze selected carefully screened compounds and reference inhibitors. This process generated A set of eigenvectors by diagonalizing the covariance matrix. By utilizing these eigenvectors, we can acquire valuable insights into the energetic influence of each component on the molecular motions observed. We performed Gibbs free energy landscape (FEL) calculations to gain deeper understanding of the dynamic behavior of the studied system. By Utilizing the sham function in GROMACS, the predictions of the top two principal components were employed. This analysis provided a visualization and understanding of the energy landscapes that control conformational changes and interactions within the molecular systems under study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e3.1. Docking Validation\u003c/h2\u003e\u003cp\u003eDocking studies provide valuable insights into the mechanism of interactions between drug and proteins. To confirm the accuracy, we repeated the docking of the co-crystallized inhibitor within its initial structure. Remarkably, this redocking process resulted in a low RMSD value of 0.2–0.4 Å. The docking outcomes were thoroughly examined, focusing on the binding conformation and interaction analysis detailed in (Supplementary Table\u0026nbsp;1, 2, 3).\u003c/p\u003e\u003ch2\u003e3.2 Docking studies\u003c/h2\u003e\u003cp\u003eThe docking studies involved diverse conformers of 4-hydroxyisoleucine and its mimetics to assess their binding affinity with key diabetes targets. These investigations identified the top 2 ligands for each target out of a total of 23 ligands based on their docking score and interactions resembling those of the respective target co-crystal ligands. The chosen compounds demonstrated substantial interactions with the key active site residues; analysing these detailed binding interactions and evaluating the therapeutic potential of the top-selected compounds can help guide further exploration and development of these promising drug candidates. This in-depth understanding of the compound-target interactions is crucial for assessing the viability of these compounds for continued drug development efforts.\u003c/p\u003e\u003cp\u003eUpon docking studies, it was revealed that out of 23 ligands used in the study (supplementary material Table\u0026nbsp;2), ligand 4-OHIL-4 has the best docking score of -8.3 kcal/mol. As depicted from Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (2b) ligand, 4-OHIL-4 shows many hydrogen bond interactions, salt bridge, and pi-pi stacking interactions with the target alpha glucosidase protein. Pi-pi stacking is at the distance of 5.13Å with the residue PHE649; hydrogen bonding interactions with the residues ASP404, ASP518, ARG600, HIE674, and LEU678 within the space of 1.94 Å, 1.65 Å, 2.12 Å, 2.22 Å, 2.54Å respectively. Compared with the co-crystal ligand Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (1a), acarbose shows additional interaction. The amine group of the ligand shows pi-pi interaction at 5.13 Å with the aromatic ring of PHE649—two salt bridge formations with ASP518 and ASP616 at distances of 3.07 and 4.73 Å, respectively.\u003c/p\u003e\u003cp\u003eThe co-crystalized ligand of the target alpha-amylase Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (2a) shows respective hydrogen bonds and pi-pi stacking interactions with the protein. Pi-pi stacking is at the distance of 3.85 Å with the residue TRP59; hydrogen bonding interactions with the residues GLN63, ASP197, and GLH233 within the space of 2.1, 1.5, 2.1, and 2.6 Å, respectively. 2R-3S-4R-4OHIL shows additional pi-cation and salt bridge interactions compared with the co-crystal ligand. The amine group of the ligand shows pi-cation interaction at 6.3Å with the aromatic ring of TYR62. Two salt bridge formations with ASP300 at 3.5 and 3.6 Å. The amino and carboxy groups of VAL-L-4-OHIL form salt bridges with negative and positively charged residues ASP197 and ARG195 of 2.68 and 4.76 Å. These results highlight the potential of these compounds as promising candidates for further investigation in drug development efforts.\u003c/p\u003e\u003cp\u003eThe co-crystal ligand of aldose reductase (4QX4) forms an essential Hydrogen bonding interaction with the polar and hydrophobic residues Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (3a) HIE110, TRP111, TYR48, LEU300, and aromatic hydrogen bond with the VAL47. Compared with the co-crystal ligand, the 4-OHIL amide − 2 w a better binding affinity of -7.6 kcal/mol.\u003c/p\u003e\u003ch2\u003e3.3 MM-GBSA\u003c/h2\u003e\u003cp\u003eThe post – molecular docking analysis, which is essential for determining the stability of receptor-ligand complexes, was computed using the Prime MM-GBSA approach. Recent studies have shown that docking score findings can be consistently verified using the computation of binding free energy. The calculations for the energy required to bind are expressed as ΔG. In mathematical terms, a stable protein-ligand complex is characterized negative values of the Gibbs free energy (ΔG), while incorrect docking outcomes are identified by positive ΔG values. The six derivatives of the 4-OHIL formed a stable complex with Alpha-glucosidase, Alpha-amylase, and Aldose reductase. 4-OHIL-Amide-2 ,4-OHIL -Amide-3, 4-OHIL-4, 4-OHIL-5, 4-OHIL-6, 4-OHIL-7 exhibited binding free energy of -64.3Kcal/mol, -45.7Kcal/mol, -49.6Kcal/mol, -66.6Kcal/ mol, -55.4Kcal/mol and − 60.0 Kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003e3.4 Molecular Dynamic Analysis\u003c/h2\u003e\u003cp\u003eThe stability of interactions and conformations within physiological environments can be assessed by using molecular dynamic simulations. To evaluate the structural and flexibility properties of the top protein-ligand complexes and the reference compound for each antidiabetic target underwent a were 100 ns molecular dynamics simulation. The conformational and interaction stability of receptor-ligand complexes were assessed using root mean square deviation (RMSD) and root mean square fluctuation (RMSF). Notably, complexes involving 4-OHIL-4 and 4-OHIL of target Alpha Glucosidase(5NN8), 2R-3S-4R-4OHIL and 4-OHIL of target alpha-amylase (4GQR), 4-OHIL-Amide-2 and 4-OHIL of target aldose reductase (4QX4) exhibited remarkable stability RMSD when compared to known compounds. RMSD values of all the above-mentioned complexes fell between 0.2 to 0.4nm. RMSF analysis demonstrated minimal variation in residues across all complexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), highlighting their constant stability.\u003c/p\u003e\u003ch2\u003e3.5 Hydrogen Bonding Analysis\u003c/h2\u003e\u003cp\u003eThe stabilization of the protein-ligand complex is greatly aided by hydrogen bonding. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the number of hydrogen bonds formed in complexes during the last 100 ns trajectory. The binding affinity between the protein and ligand greatly influenced by their polar interactions. The occupancy percentages for hydrogen bonds indicate how many trajectories were involved in the formation of hydrogen bonds during a simulation. In the case of the Alpha Glucosidase ligand, 4-OHIL-4 shows many hydrogen bond interactions, with the specific residues ASP404, ASP518, ARG600, HIE674, and LEU678 within the space of 1.94 Å, 1.65 Å, 2.12 Å, 2.22 Å, 2.54Å respectively. Hydrogen bonding interaction with the co-crystal ligand characteristic residues ASP404, ASP518, ARG600, HIE674, and LEU678 within a range of 2.5 Å. The co-crystallized form of alpha-glucosidase contained 23 hydrogen bonds, in which the ligand shows the occupancy rate with Asp 616 and Asp 282 at 76.13% and 76.95%. Meanwhile, the 4-OHIL-4 exhibited a higher occupancy rate than the co-crystal ligand.\u003c/p\u003e\u003cp\u003eThe co-crystalized ligand of the target alpha amylase shows hydrogen bonding interactions with the residues GLN63, ASP197, and GLH233 within the space of 2.1, 1.5, 2.1, and 2.6 Å, respectively, 2R-3S-4R-4OHIL shows Hydrogen bonding interaction with characteristic residues ASP197, ARG195, GLH233, ASN 298 within a range of 2 Å, VAL-L-4-OHIL forms hydrogen bonding interaction at 1.7, 1.7, 1.8,1.9, 1.7 Å with characteristic residues such as ASP197, ARG195, GLH233, HIE299, ASP300. The co-crystallized form of alpha-amylase contained 32 hydrogen bonds, in which the ligand shows the occupancy rate with the Asp 300, Glu 233, and Asp 356 with 26.94%, 8.91%, and 11.05%. At the same time, the 2R-3S-4R-4OHIL was found to show a higher occupancy rate with the Asp 300, Glu 233, and Asp 356 with 30.77%, 20.26%, and 24.04% Compared with the co-crystal ligand. The co-crystal ligand of aldose reductase forms an essential Hydrogen bonding interaction with the polar and hydrophobic residues HIE110, TRP111, TYR48, LEU300, and aromatic hydrogen bond with the VAL47. The presence of significant number of hydrogen bonds with various target protein-ligand complexes indicates these are suitable inhibitors. It is possible that these residues could have a significant impact on the interaction between the protein-ligand complex. Specific residues involved in molecular interactions can be identified by analyzing hydrogen bond interactions.\u003c/p\u003e\u003ch2\u003e3.6 Free energy landscape\u003c/h2\u003e\u003cp\u003eGmx Covar, Gmx Anaeig, and Gmx Sham were utilized to calculate the Gibbs free energy landscape, using projections from their own first (PC1) and second (PC2) eigenvectors. In (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the Gibbs free energy landscape is shown in a color-coded manner. Trajectories are used to analyze the direction of fluctuation for all C atoms in complex structures is investigated by the Gibbs free energy landscape through the use of trajectories. Lower energy is indicated by a deeper blue colour corresponds to the free energy contour map. After the binding of these compounds, the primary free energy in the global free energy region was found to have undergone a complete change. These molecules stable conformational states are strongly indicated by these free energies. Different global minima of various targets are observed during the 100ns of MD simulations due to the binding of ligands to the respective targets. A small energy barrier separates the metastable conformational states from several distinguishable minima on the energy landscape. The most metastable conformational states were seen in the binding in case of (Alpha Glucosidase )5NN8 with 4-OHIL and 5NN8 with 4-OHIL-4, (Alpha amylase) 4GQR with 4-OHIL, 4GQR with 2R-3S-4R, (Aldose Reductase) 4QX4-4-OHIL, 4QX4-4OHIL - Amide 2 in which local minima were distributed to about three to four regions within the energy landscape. The above-mentioned complexes formed just two to three metastable conformations during the whole trajectories.\u003c/p\u003e\u003ch2\u003e4. Shape- Based Generative Modeling for de Novo Drug Design\u003c/h2\u003e\u003cp\u003eGenerative modelling provides an alternative approach to molecular discovery by reformulating molecular design as an inverse design problem. The number of compounds that have ever been synthesized lies around 10^8 while the total number of theoretically feasible compounds lies between 10^23 and 10^60. Conventional discovery methods are only capable of exploring a small fraction of chemical space. By identifying a function that maps a set of structures, generative models can rapidly identify diverse sets of molecules that are highly optimised for specific application. Generative modelling can be designed by sequence based and shape based. The sequence based are more biased towards the design of compounds obtainable by small molecule chemical modifications.\u003c/p\u003e\u003cp\u003eThe shape based provide generative novel scaffold inspired from structure-based design (spatial information is considered). Shape variational encoder using convolutional neural network to autoencoder compound representation, Combination of CNN and long short-term memory network to generate smile string. Variational autoencoder is a type of neural network that encodes shapes, create puzzles and decodes.\u003c/p\u003e\u003ch2\u003eTraining, Featurization and Model Training (Ligdream)\u003c/h2\u003e\u003cp\u003eCanonical smiles notations were collected from Zinc 15 database, by using the RDkit random conformer generated, optimization of 3D structures using MMFF94 forcefield, random splitting into training and test sets, protein targets taken from DUDE database, docking. This approach has some challenges such as performance drops as the target sequence gets longer, H-bond donars and Hbond acceptors are difficult to recover as these properties are not specifically marked and are mainly dependent on its surrounding. This approach is applied to the top obtained molecules against each diabetes target, generative structures are subjected to further docking process against alpha glucosidase, alpha amylase, aldose reductase target shown in supplementary data (Table\u0026nbsp;4, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study underscores the significance of 4-hydroxyisoleucine (4-OHIL) derived from fenugreek in the treatment of Diabetes mellitus by enhancing insulin production and sensitivity. Based on the computational analysis and discussions, including docking study, interaction analysis, MD simulation, binding free energy, H-bond and FEL calculations, were identified promising drug-like compounds with similar ligand binding kinetics to diabetes mellitus targets. The top compounds, including 4-OHIL, 4-OHIL-4, 2R-3S-4R-4OHIL, and 4-OHIL-Amide-2 were identified as potent inhibitors against specific enzyme targets implicated in diabetes. Shape based screening of Molecules Satisfying Anti-diabetic property Using Generative Model. This study provides valuable insights into potential therapeutic avenues for managing diabetes the wider anti-diabetic research community by providing a platform for predicting small molecules with subsequent validation for drug discovery and development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the National Institute of Pharmaceutical Education and Research (NIPER)\u003c/p\u003e\n\u003cp\u003eSAS Nagar, Department of pharmaceuticals, Ministry of chemicals and Fertilizers, New Delhi, Government of India for providing the facility\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has no competing interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis wereperformed by Ms.LMK. The first draft of the manuscript was written by Ms. LMK, and all authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArumugam G, Manjula P, Paari N. A review: Anti diabetic medicinal plants used for diabetes mellitus. Journal of Acute Disease. 2013;2:196\u0026ndash;200.\u003c/li\u003e\n\u003cli\u003eJalilian H, Javanshir E, Torkzadeh L, et al. Prevalence of type 2 diabetes complications and its association with diet knowledge and skills and self-care barriers in Tabriz, Iran: A cross-sectional study. Health Sci Rep. 2023;6.\u003c/li\u003e\n\u003cli\u003eEizirik DL, Pasquali L, Cnop M. Pancreatic \u0026beta;-cells in type 1 and type 2 diabetes mellitus: different pathways to failure. Nat Rev Endocrinol. Nature Research; 2020. p. 349\u0026ndash;362.\u003c/li\u003e\n\u003cli\u003eGupta RC, Chang D, Nammi S, et al. Interactions between antidiabetic drugs and herbs: An overview of mechanisms of action and clinical implications. Diabetol Metab Syndr. BioMed Central Ltd.; 2017.\u003c/li\u003e\n\u003cli\u003eSun H SPKSPMOKDBSCBACJMJPMRAWSJSHWZPBCKSBEMD. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2021;183:109119.\u003c/li\u003e\n\u003cli\u003eSapra A BP. Diabetes Mellitus. Treasure Island (FL): StatPearls Publishing; 2021.\u003c/li\u003e\n\u003cli\u003eWestman EC. Type 2 Diabetes Mellitus: A Pathophysiologic Perspective. Front Nutr. 2021;8.\u003c/li\u003e\n\u003cli\u003eDodds S. The How-To for Type 2: An Overview of Diagnosis and Management of Type 2 Diabetes Mellitus. Nursing Clinics of North America. W.B. Saunders; 2017. p. 513\u0026ndash;522.\u003c/li\u003e\n\u003cli\u003eKazmi M, Zaib S, Ibrar A, et al. A new entry into the portfolio of \u0026alpha;-glucosidase inhibitors as potent therapeutics for type 2 diabetes: Design, bioevaluation and one-pot multi-component synthesis of diamine-bridged coumarinyl oxadiazole conjugates. Bioorg Chem. 2018;77:190\u0026ndash;202.\u003c/li\u003e\n\u003cli\u003eLiu SK, Hao H, Bian Y, et al. Discovery of New \u0026alpha;-Glucosidase Inhibitors: Structure-Based Virtual Screening and Biological Evaluation. Front Chem. 2021;9.\u003c/li\u003e\n\u003cli\u003eTang WH, Martin KA, Hwa J. Aldose reductase, oxidative stress, and diabetic mellitus. Front Pharmacol. 2012;3 MAY.\u003c/li\u003e\n\u003cli\u003eNavjot Kaur VKSKNPWPKSKS. Alpha-amylase as molecular target for treatment of diabetes mellitus: A comprehensive review. Chem Biol Drug Des. 2021;\u003c/li\u003e\n\u003cli\u003eYan J, Zhao J, Yang R, et al. Bioactive peptides with antidiabetic properties: a review. Int J Food Sci Technol. Blackwell Publishing Ltd; 2019. p. 1909\u0026ndash;1919.\u003c/li\u003e\n\u003cli\u003eQurtam AA, Mechchate H, Es-Safi I, et al. Citrus flavanone narirutin, in vitro and in silico mechanistic antidiabetic potential. Pharmaceutics. 2021;13.\u003c/li\u003e\n\u003cli\u003eSchuster D, Laggner C, Langer T. Why Drugs Fail-A Study on Side Effects in New Chemical Entities. Curr Pharm Des. 2005.\u003c/li\u003e\n\u003cli\u003ePatel DK, Kumar R, Laloo D, et al. Diabetes mellitus: An overview on its pharmacological aspects and reported medicinal plants having antidiabetic activity. Asian Pac J Trop Biomed. 2012;2:411\u0026ndash;420.\u003c/li\u003e\n\u003cli\u003eNarender T, Puri A, Shweta, et al. 4-Hydroxyisoleucine an unusual amino acid as antidyslipidemic and antihyperglycemic agent. Bioorg Med Chem Lett. 2006;16:293\u0026ndash;296.\u003c/li\u003e\n\u003cli\u003eKorthikunta V, Pandey J, Singh R, et al. In vitro anti-hyperglycemic activity of 4-hydroxyisoleucine derivatives. Phytomedicine. 2015;22:66\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eSauvaire Y, Petit P, Broca C, et al. 4-Hydroxyisoleucine A Novel Amino Acid Potentiator of Insulin Secretion.\u003c/li\u003e\n\u003cli\u003eFowden L, Pratt HM, Smith A. 4-HYDROXYISOLEUCINE FROM SEED OF TRIGONELLA FOENUM-GRAECUM. Phytochemistry. Pergamon Press. Printed in England; 1973.\u003c/li\u003e\n\u003cli\u003eAnaguiven Avalos-Soriano RD la C-CJLR and TG-G. 4-Hydroxyisoleucine from Fenugreek (Trigonella foenum-graecum): Effects on Insulin Resistance Associated with Obesity. Molecules. 2016;\u003c/li\u003e\n\u003cli\u003eRawat AK, Korthikunta V, Gautam S, et al. 4-Hydroxyisoleucine improves insulin resistance by promoting mitochondrial biogenesis and act through AMPK and Akt dependent pathway. Fitoterapia. 2014;99:307\u0026ndash;317.\u003c/li\u003e\n\u003cli\u003eGupta P, Bala M, Gupta S, et al. Efficacy and risk profile of anti-diabetic therapies: Conventional vs traditional drugs\u0026mdash;A mechanistic revisit to understand their mode of action. Pharmacol Res. Academic Press; 2016. p. 636\u0026ndash;674.\u003c/li\u003e\n\u003cli\u003eVan Der Spoel D, Lindahl E, Hess B, et al. GROMACS: Fast, flexible, and free. J Comput Chem. 2005. p. 1701\u0026ndash;1718.\u003c/li\u003e\n\u003cli\u003eSchuler LD, Daura X, Van Gunsteren WF. An Improved GROMOS96 Force Field for Aliphatic Hydrocarbons in the Condensed Phase. J Comput Chem. 2001.\u003c/li\u003e\n\u003cli\u003eAbraham MJ, Murtola T, Schulz R, et al. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1\u0026ndash;2:19\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;dinger L. Protein Preparation Wizard. Schrodinger Release. Schrodinger Release. 2023;2.\u003c/li\u003e\n\u003cli\u003eFilipe HAL, Loura LMS. Molecular Dynamics Simulations: Advances and Applications. Molecules. MDPI; 2022.\u003c/li\u003e\n\u003cli\u003eVan Der Spoel D, Lindahl E, Hess B, et al. GROMACS: Fast, flexible, and free. J Comput Chem. 2005. p. 1701\u0026ndash;1718.\u003c/li\u003e\n\u003cli\u003eGenheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. Informa Healthcare; 2015. p. 449\u0026ndash;461.\u003c/li\u003e\n\u003cli\u003eKhan MT, Khan A, Rehman AU, et al. Structural and free energy landscape of novel mutations in ribosomal protein S1 (rpsA) associated with pyrazinamide resistance. Sci Rep. 2019;9.\u003c/li\u003e\n\u003cli\u003eKlambauer G, Hochreiter S, Rarey M. Machine Learning in Drug Discovery. J Chem Inf Model. American Chemical Society; 2019. p. 945\u0026ndash;946.\u003c/li\u003e\n\u003cli\u003eBilodeau C, Jin W, Jaakkola T, et al. Generative models for molecular discovery: Recent advances and challenges. Wiley Interdiscip Rev Comput Mol Sci. John Wiley and Sons Inc; 2022.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"natural-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"naco","sideBox":"Learn more about [Natural Computing](http://link.springer.com/journal/11047)","snPcode":"11047","submissionUrl":"https://submission.nature.com/new-submission/11047/3","title":"Natural Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Diabetes, Alpha Glucosidase, Alpha amylase, Aldose reductase, inhibitors, docking, Molecular dynamics, Binding free energy","lastPublishedDoi":"10.21203/rs.3.rs-4575416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4575416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe present study focuses on the importance of, a peculiar nonprotein amino acid 4-hydroxy isoleucine (4OHIle) as a constituent isolated from fenugreek (Trigonella foenum-graecum), which plays a vital role in the treatment of Diabetes mellitus. Here, we represent 4-hydroxy isoleucine derivatives has the ability to simulate insulin production and increase insulin sensitivity in diabetes mellitus targets.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eIn this study, using computational methods we search for suitable drug like compounds that have similar ligand binding kinetics to target diabetes mellitus.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe top drug like compounds are selected based on computational methods such as Molecular Docking, Molecular Dynamic Simulation, Gibbs Free Energy calculations and Free Energy Landscape (FEL), shape based generative modelling for \u003cem\u003ede novo\u003c/em\u003e drug design.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDocking-based simulation screened out best 2 compounds against each targeted enzyme implicated in diabetes. Further, their dynamics studies reveal that the compounds 4-OHIL, 4-OHIL-4, 2R-3S-4R-4OHIL and 4-OHIL-Amide-2 were affirmed as the best inhibitors of respective enzyme targets. The best inhibitors are further optimised using generative model (Ligdream)\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAnticipating the competitive inhibition of target protein expression in diabetes mellitus, we envision that the best inhibitors of respective enzyme targets. The findings from this current investigation carry significant modifications for the advancement in order to improve their potential to treat type 2 diabetes\u003c/p\u003e","manuscriptTitle":"Revolutionizing Diabetes Treatment: Computational Insights into 4- Hydroxy isoleucine Derivatives and Advanced Molecular Screening for Anti-Diabetic Compounds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 09:12:04","doi":"10.21203/rs.3.rs-4575416/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-05T08:21:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-18T05:57:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Computing","date":"2024-06-13T10:14:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"naco","sideBox":"Learn more about [Natural Computing](http://link.springer.com/journal/11047)","snPcode":"11047","submissionUrl":"https://submission.nature.com/new-submission/11047/3","title":"Natural Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"949495f8-0180-4dd4-8733-e42ed6a55115","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-03T09:12:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 09:12:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4575416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4575416","identity":"rs-4575416","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}