In silico prospection of receptors associated with the biological activity of U1-SCTRX-lg1a: an antimicrobial peptide isolated from the venom of Loxosceles gaucho

In silico prospection of receptors associated with the biological activity of U1-SCTRX-lg1a: an antimicrobial peptide isolated from the venom of Loxosceles gaucho | 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 In silico prospection of receptors associated with the biological activity of U1-SCTRX-lg1a: an antimicrobial peptide isolated from the venom of Loxosceles gaucho André Souza de Oliveira, Elias Jorge Muniz Seif, Pedro Ismael da Silva Junior This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3043813/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 Background: The emergence of antibiotic-resistant pathogens generates impairment to human health. U1-SCTRX-lg1a is a peptide isolated from a phospholipase D extracted from the spider venom of Loxosceles gaucho with antimicrobial activity against Gram-negative bacteria (between 1.15 μM to 4.6 μM). The aim of this study was to suggest potential receptors associated with the antimicrobial activity of U1-SCTRX-lg1a using in silico bioinformatics tools. Methods: The search for potential targets of U1-SCRTX-lg1a was performed using the PharmMapper server. Molecular docking between U1-SCRTX-lg1a and the receptor was performed using PatchDock software. The prediction of ligand sites for each receptor was conducted using the PDBSum server. Chimera 1.6 software was used to perform molecular dynamics simulations only for the best dock score receptor. In addition, U1-SCRTX-lg1a and native ligand interactions were compared using AutoDock Vina software. Finally, predicted interactions were compared with the ligand site previously described in the literature. Results and discussion: The bioprospecting of U1-SCRTX-lg1a resulted in the identification of forty-nine intracellular proteins originating from Gram-negative microorganisms. Among these, NH 3 -dependent NAD + synthetase showed the highest dock score. This result suggests that the peptide derived from brown spider venom may interact with residues SER48 and THR160. In addition, the C-terminus has greater affinity for the receptor than the N-terminus. Conclusion: The in silico bioprospecting of receptors suggests that U1-SCRTX-lg1a may interfere with NAD + production in Escherichia coli , a Gram-negative bacterium, altering the homeostasis of the microorganism and impairing growth. Animal Science General Microbiology Bioinformatics Computational Biology Molecular Docking Intracellular Targets Spider Venom Bioprospecting. Figures Figure 1 Figure 2 Figure 3 Introduction Loxosceles sp. (Araneae, Sicariidae) belongs to the subphylum Chelicerata, one of the three evolutionary lineages of arthropods. Systematically, within this subphylum, the class Arachnida comprises most of the Chelicerata, including forms such as spiders, scorpions, mites, and ticks (Ruppert & Barnes, 1996). Brown spiders are a group of spiders that produce venoms with human clinical manifestations. This venom has been studied for at least 60 years in different research groups worldwide. The extraction and characterization of this venom was motivated by several cases related to loxoscelism, injury caused by spider bites (Chaves-Moreira et al., 2017 ). Three main toxin families are present in spider venom: phospholipases-D, astacin-like metalloproteases, and inhibitor cystine knot (ICK) peptides. Additionally, serine proteases, serpins, hyaluronidases, venom allergens, and translationally controlled tumor protein (TCTP) are also present. These toxins have essential biological properties that enable them to interact with a range of distinct molecular targets. Therefore, this toxin can be a source of bioactive molecules for use in the pharmaceutical industry (Chaves-Moreira et al., 2019). The emergence of new multiresistant microorganisms is increasing each year, which can increase the risks of mortality and morbidity, consequently overloading public health systems and causing financial losses for countries stricken. Otherwise, the low efficiency of traditional medicines and treatments against these organisms requires research for the development of new bioactive molecules to control diseases caused by microorganisms (FERRI, Maurizio et al., 2013). U1-UCRTX-lg1a (VGTDFSGNDDISDVQK) is an anionic peptide derived from phospholipase-D isolated from spider L. gaucho venom. This peptide showed antibacterial activity against Escherichia coli , Pseudomonas aeruginosa , Enterobacter cloacae presented minimal inhibitory concentrations between 2 and 5 µM. In this way, activity was only observed in gram-negative bacteria. Furthermore, in an experiment with human erythrocytes, hemolytic activity was not observed with U1-SCRTX-lg1a at within the inhibitory concentrations. (Segura. P and Junior. P, 2018). However, your active mechanism and molecular targets are not well elucidated. Virtual ligand screening and molecular docking are computational methods to identify protein targets and interactions. Among them, those methods can be used for the initial development of new drugs; likewise, simulations have been performed using many parameters at the same time since they are faster and more efficient than in vitro experiments (GLAAB, Enrico 2016 ). In this process, there are steps: protein and ligand preparation, creation of molecular models, molecular docking, analysis, and visualization of the results (Seif. EJM et al., 2023). In contrast, computational biology requires a high processing rate; however, this barrier has been superseded due to computational advancement and the emergence of online servers to perform analysis (Andrusier & Duhovny, 2002). Therefore, the aim of this study was to use free bioinformatics tools to perform virtual screening and identify potential receptors associated with the antimicrobial activity of the U1-SCRTX-lg1a peptide and to further describe those ligand-receptor interactions. Methods Peptide characterization and minimum free energy The Heliquest server ( https://heliquest.ipmc.cnrs.fr/ ) (Gautier et al., 2008 ) was used to determine the physicochemical parameters (net charge, hydrophobicity moment and molecular weight) of U1-SCRTX-lg1peptide (VGTDFSGNDDISDVQK). Theoretical pI, instability index, and grand average of hydropathicity (GRAVY) were obtained by the Expasy Server ( https://web.expasy.org/cgi-bin/protparam/ ) (Gasteiger E. et al, 2005 ) and Pepcalc server ( https://pepcalc.com/ ). To build the peptide tridimensional structure, free energy mini was used by the server I-TASSER ( https://zhanggroup.org/I-TASSER/ ) (Zheng W et al, 2021 ). The state of minimum free energy was obtained using UCSF Chimera software ( https://www.cgl.ucsf.edu/chimera/ ) (Pettersen et al., 2004 ). The following settings were used: steepest descendent steps (100000); steepest descendent steps size Å (0.02); conjugate gradient steps (10), conjugate gradient steps size Å (0.02), update interval (10), fixed atoms (none) after H, H-B, charges, and SR (amber force field ff14SB). Search for potential Gram-negative targets To identify potential receptors for U1-SCRTX-lg1a, the PharmMapper server ( http://www.lilab-ecust.cn/pharmmapper/ ) was used (Wang et al., 2017). The peptide structure was submitted using the following parameters: generate conformers (yes); maximum generated conformations (300); full/complete pharmacophore mapping; all targets selected (v2010, 7302), and number of reserved matched targets (300). Those results were ranked by normalized fit score. All targets were classified based on origin and catalytic activity ( https://www.uniprot.org ) in the microorganism. It selected a total of forty-nine proteins derived from Gram-negative bacteria without mutations in their models, and those structures were provided by the Protein Data Bank (PDB) server ( https://www.rcsb.org/ ). Target and peptide molecular docking Molecular docking between U1-SCRTX-lg1a and the selected targets was performed by PatchDock software ( https://bioinfo3d.cs.tau.ac.il/PatchDock/ ) (Schneidman-Duhovny et al., 2005). The PatchDock parameters were set to clustering RMSD (4.0) and complex type (default). The docking results were ranked based on the major score value to determine the optimal binding of U1-SCRTX-lg1a. AREA, ACE (effective atomic contact energies), and transformation values were also collected. The docking results were classified by the major score value. In addition, the better target found in PatchDock was also performed by AutoDock vina (TROTT, Oleg; OLSON, Arthur J, 2010) to examine the affinity of the C-terminus and N-terminus segments and native ligand. Analysis of ligand and receptor interactions The U1-SCRTX-lg1a and receptor interaction was analyzed using UCSF Chimera software ( https://www.cgl.ucsf.edu/chimera/ ) (Pettersen et al., 2004 ). The find bond tool was used with relaxed constraint binding (2 Å and 20 degrees) to examine the interaction. In this study, only hydrogen bonds with distances less than 4 Å between heavy atoms were considered. Furthermore, information on residue electron donors and acceptors was collected. Comparison of predicted ligand sites Receptor ligand sites were predicted using the PDBsum server ( http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/ ). For this, the UniProt code ( https://www.uniprot.org/ ) and the FASTA sequence of better scores found in molecular docking analysis were used. The results were compared to the ligand site obtained by molecular docking. Molecular dynamics simulation Molecular dynamics simulations were performed between PDB id :1wxi as a receptor and U1-SCRTX-lg1a as a ligand using Chimera 1.6 software. The simulations utilized the TIP3BOX solvent model, a 1:1 ratio, and 100 steps from steepest descent with a size of 0.02 Å to steepest descent and conjugate gradient steps with a size of 0.02 Å. Intermodal and intramodal hydrogen bonds were identified with constraints relaxed at 2 angstroms and 20 degrees. Results and Discussion Peptide Characterization and Minimum Free Energy The physicochemical analysis using Heliquest, Expasy, and Pepcal servers showed that the U1-SCTRX-lg1a peptide has a molecular weight of 1695.7 g/mol, a net charge of -3, and a hydrophobic index/hydrophobic moment of 0.083/0.189. Energy minimization of the peptide structure resulted in a free energy of -1502.51 kJ/mol (figure 1). The peptide contains four acidic, one aromatic, one alkaline, three aliphatic, and five polar amino acids. Aspartate has a negatively charged R group, phenylalanine has an aromatic side chain with relatively hydrophobic characteristics, lysine is positively charged and hydrophilic, and valine and isoleucine tend to group together inside proteins, stabilizing the protein structure through hydrophobic interactions. Serine, threonine, asparagine, and glutamine are more water-soluble amino acids that contain functional groups capable of forming hydrogen bonds (NELSON, David L.; COX, Michael M., 2022). The peptide presented a theoretical mass of approximately 1695.75 g/mol and is relatively small. The model obtained from the I-TASSER server showed a confidence score (C-score) of -0.90, which was used to estimate the quality of the predicted models. C-scores range from [-5,2], where a higher C-score indicates a more confident model (Jianyi Yang et al., 2015). The best model was used for target search and molecular docking. Physicochemical properties such as charge, hydrophobicity and residue number are usually found in membranolytic antimicrobial peptides, and membrane injury is the main activation mechanism due to potential differences between membranes and peptides (BENFIELD, A. H. and HENRIQUES, S. T., 2020). In contrast, hemolytic activity was not observed with U1-SCRTX-lg1a at inhibitory concentrations. Therefore, this peptide uses nonmembranolytic activity mechanisms to inhibit bacterial growth. Thus, antimicrobial activity occurs by interacting with intracellular targets, promoting homeostasis impairment and cell death. This mechanism is particularly found in noncharged or low-charged peptides, such as Doderlin (DA SILVA, Bruna S. et al, 2023), Rondonin (RICILUCA, K. C. T. et al, 2012) and Crotamine (DAL MAS, Caroline et al, 2019). Potential Gram-negative target identification The first step of bioprospecting receptors for U1-SCRTX-lg1a was determined by the PharmMapper Server. This is an online tool that uses pharmacophore mapping techniques to identify potential drug targets that can be used for virtual screening. The tool has a database of more than 7,000 proteins built based on protein information and pharmacophore models. When inserting a molecule, the tool generates a ranked list of proteins based on the similarity of their pharmacophores. This enables the identification of possible drug targets and the development of new compounds with therapeutic potential. (LIU, Xiaofeng et al., 2010). The PharmMapper search resulted in 300 general targets (table S1), among which 49 originated from Gram-negative targets (table S2); however, in this research, only 10 had better scores based on the PatchDock results (table 1). The PharmMapper results were normalized to fit the score between 8.216 and 9.998. The better receptors found using this method were peptidyl-dipeptidase dcp [PDB id : 1y79], phosphoenolpyruvate carboxylase [PDB id : 1jqn] and thermoresistant gluconokinase [PDB id : 1ko8], all of which originated from Escherichia coli. The U1-SCTRX-lg1a peptide exhibited antimicrobial activity against Escherichia coli strains SBS636 and D31 with a minimum inhibitory concentration of 4.6 μM (Segura. P and Junior. P, 2018). Table 1: The 10 targets originating from Gram-negative microorganisms resulting from PharmMapper based on molecular docking. The rank of targets was ordered by major normalized fit score. PM Rank ID PDB Normalized Fit Score Target Name Catalytic activity Origin 41 1y79 9.782 Peptidyl-dipeptidase dcp Hydrolysis of unblocked Escherichia coli 74 1jqn 9.592 Phosphoenolpyruvate carboxylase oxaloacetate + phosphate = hydrogencarbonate + phosphoenolpyruvate Escherichia coli K12 81 1ko8 9.558 Thermoresistant gluconokinase ATP + D-gluconate = 6-phospho-D-gluconate + ADP + H + Escherichia coli K12 113 1m2x 9.433 Metallo-beta-lactamase type 2 a beta-lactam + H2O = a substituted beta-amino acid Elizabethkingia meningoseptica 155 1hmu 9.188 Chondroitinase-AC Eliminative degradation of polysaccharides containing 1,4-beta-D-hexosamine and 1,3-beta-D-glucuronosyl linkages to disaccharides containing 4-deoxy-beta-D-gluc-4-enuronosyl groups. Pedobacter heparinus 215 1wxi 8812 NH 3 -dependent NAD synthetase ATP + deamido-NAD + + NH4 + = AMP + diphosphate + H + + NAD + Escherichia coli K12 260 1k4m 8.513 Nicotinate-nucleotide adenylyltransferase ATP + H + + nicotinate beta-D-ribonucleotide = deamido-NAD + + diphosphate Escherichia coli K12 271 1geg 8.412 Diacetyl reductase [(S)-acetoin forming] (S)-acetoin + NAD + = diacetyl + H + + NADH Klebsiella pneumoniae 291 1kp2 829 Argininosuccinate synthase ATP + L-aspartate + L-citrulline = 2-(N(omega)-L-arginino) succinate + AMP + diphosphate + H + Escherichia coli K12 ID PDB - Identification code in protein data bank, PM Rank - Rank in PharmMapper server, Normalized Fit Score - value obtained by ratio of fit score and number of features, Catalytic activity - obtained from uniprot.org Origin - Species from the target was isolated None - catalytic activity not found. Target and peptide molecular docking PatchDock is software that utilizes a docking technique based on complementarity principles to model the molecular docking between two proteins. This allows for the simulation of protein docking in different contexts. Molecular docking is a technique used to study the interaction between molecules, especially between a protein and a ligand, with the aim of predicting their binding affinity (Schneidman-Duhovny, Dina et al, 2005). Molecular docking was performed by PatchDock for all 49 targets originating from Gram-negative organisms (table S3); however, in this work, only 10 targets with better docking scores were identified (table 2). The docking results indicated score (10702 to 6066), area (1498.70 to 728.40), and ACE (417.90 to -152.8). The ranking of receptors observed in PatchDock differs from that in the PharmMapper search. Among all proteins studied, the highest dock score was found for peptidyl-dipeptidase dcp [PDB id : 1y79], nicotinate-nucleotide adenylyltransferase [PDB id : 1k4m], phosphoenolpyruvate carboxylase [PDB id : 1jqn], nitrogenase molybdenum-iron protein alpha chain [PDB id : 1h1l], metallo-beta-lactamase type 2 [PDB id : 1m2x], and NH 3 -dependent NAD + synthetase [PDB id : 1wxi] (Table 2). Table 2 : Results of rigid docking obtained from PatchDock. U1-SCRTX-lg1a was used as the ligand, and the receptor target obtained by PharmMapper was used. The table is arranged according to the major scores. PM Rank ID PDB Receptor Name Score Area ACE (kj/mol) 41 1y79 Peptidyl-dipeptidase dcp 10702 1296.30 91.16 260 1k4m Nicotinate-nucleotide adenylyltransferase 10126 1256.60 417.90 74 1jqn Phosphoenolpyruvate carboxylase 9974 1292.40 212.58 289 1h1l Nitrogenase molybdenum-iron protein alpha chain 9924 1446.50 265.41 113 1m2x Metallo-beta-lactamase type 2 9748 1285.10 357.24 215 1wxi NH 3 -dependent NAD + synthetase 9742 1223.6 38.39 81 1ko8 Thermoresistant gluconokinase 9556 1317.90 77.66 271 1geg Acetoin(diacetyl) reductase 9534 1188.80 218.18 291 1kp2 Argininosuccinate synthase 9358 1488.70 367.27 155 1hmu Chondroitinase-AC 9340 1185.90 214.19 ID PDB (Identification code in protein data bank), PM Rank (Rank in PharmMapper server), Score (Geometric shape complementarity score), Area ( Approximate interface area of the complex), ACE ( Atomic effective contact energy). Analysis of ligand and receptor interactions Receptor-ligand interaction analysis plays a significant role in all biological processes, and computational tools are used to simulate these biological phenomena, such as three-dimensional structural modeling and molecular docking between ligands and receptors. Rigid docking is also a crucial tool in computational drug design, which enables efficient identification of connections between rigid molecules without any initial restrictions on position or orientation. The use of this approach enables a faster and more efficient exploration of optimal solutions, significantly reducing the computation time required to identify the correct binding configurations (Andrusier & Duhovny, 2002). To better understand the docking analysis, we studied only stronger hydrogen bonds (≤ 4 Å) between U1-SCRTX-lg1a and the receptor. The H bonds of six highest docking scores are summarized in table 3. These results showed that peptide as a donor or acceptor electrons in hydrogen bond interactions. Rigid docking using PatchDock produced the following interactions: peptidyl-dipeptidase Dcp from Escherichia coli with 7 bonds and phosphoenolpyruvate carboxylase from Escherichia coli K12 with 8 bonds. The NH 3 -dependent NAD + synthetase from Escherichia coli K12 showed the highest number of hydrogen bonds (10 bonds) (figure 2). Table 3 : Hydrogen bonds and distance (≤ 4 Å) for the six highest scores receptors and U1-SCRTX-lg1a obtained by a docking result by PatchDock. PM Rank PDB ID Receptor Name Energy Kg/mol Donor Acceptor D-A distance (Â) 41 1y79 Peptidyl-dipeptidase dcp -75558.81 ARG 141.A NH1 ARG 141.A NH2 ARG 268.A NH2 ASN 428.1A ND2 ASN 491.A ND2 ASN 8.L ND2 ASP 9.L N SER 6.LOG ASP 9.L OD1 GLN 15.L O ASP 4.L OD1 ASP 4.L O THR 69.A OG1 TYR 106.A OH 2.008 3.966 3.814 2.014 3.633 3.941 3.110 260 1k4m Nicotinate-nucleotide adenylyltransferase -55867.29 PHE 5.L N LEU 69.A N LYS 21.B NZ LYS 67.B NZ ASP 66.C O GLN 15.L OE1 ASP 10.L OD1 SER 6.L OG 3.908 3.932 3.424 3.824 74 1jqn Phosphoenolpyruvate carboxylase -91386.58 ASN 8.L N ASP 9.L N GLN 15.L NE2 LYS 16.L NZ ARG 244.A ARG 244.A LYS 384.A ARG 392.A GLN 532.A GLN 532.A ALA 470.A GLU 574.A VAL 14.L VAL 14.L ASP 4.L ILE 11.L 2.754 2.303 2.047 3.506 3.167 2.523 2.575 3.406 289 1h1l Nitrogenase molybdenum-iron protein alpha chain -201573.34 VAL 1.L N LYS 361.B NZ LYS 6.D NZ ASN 8.D ND2 SER 9.D OG VAL 1.L O ASP 4.L O ASP 10.L OD1 3.673 2.483 3.789 3.998 113 1m2x Metallo-beta-lactamase type 2 -66967.77 LYS 16.L NZ LYS 16.L NZ LYS 43.B NZ LYS 43.B NZ LYS 66.B NZ LYS 43.D NZ LYS 66.D N SER 225.D OG.A TRP 277.D N LYS 66.B O ASP 227.B OD2 SER 6.L OG ASP 10.L OD1 SER 12.L OG ILE 11.L O PHE 5.L O VAL 1.L O GLN 15.L OE1 3.493 3.724 2.290 1.414 3.311 3.921 2.528 3.830 3.674 215 1wxi NH 3 -dependent NAD + synthetase -29952.81 SER 6.L N GLY 7.L N SER 48.A OG GLN 51.A N ASN 136.A ND2 ARG 142.A NH2 THR 160.A OG1 THR 172.A N ASP 176.A N ASP 223.A N ASP 223.A OD2 ASP 223.A OD2 ASP 9.L O ASP 9.L OD1 VAL 1.L O GLN 15.L OE1 ASP 13.L OD1 VAL 14.L O VAL 14.L O ASP 10.L OD1 2.890 3.796 3.331 3.961 2.571 0.646 2.679 3.869 3.998 1.815 Donor (electron donor residue), Acceptor (electron acceptor residue), D-A distance (distance between heavy atoms of electron acceptor and donor), THR 3. L N (Residue name/ Number/ Chain ID/ Shared electron atom), L (Ligand ID). Ligand binding site analysis and molecular dynamic simulation Comparison between predicted binding sites from the PDBsum and those observed using Chimera 1.6 software revealed that the U1-SCRTX-lg1a interaction may be colocalized or closely related to residues of ligand sites of each receptor found in this work. Among the models with the six receptors with the highest dock scores, NH 3 -dependent NAD + synthetase showed the most satisfactory peptide interaction. It revealed 4 closely and two colocalized residues of the active site. Although peptidyl-dipeptidase dcp and phosphoenolpyruvate carboxylase both presented only one close binding site, the other 3 receptors did not present interactions with active sites (table 4). To better elucidate these interactions, molecular dynamics simulation was performed only between the peptide and NH 3 -dependent NAD + synthetase receptors. This analysis resulted in an energy value for the protein complex of -214,890.21 kJ/mol, whereas with rigid docking, this value was -29.952.81. This result suggests that after the molecular dynamics simulation, the complex exhibits a more favorable energy value compared to the previous state (fig 3). NAD + synthetase [1xwi] from E. coli catalyzes ATP-dependent starch-NAD amidation to form NAD + . NAD plays roles in processes as diverse as calcium mobilization, DNA repair, and posttranslational modification of proteins in eukaryotes. (JAUCH, Ralf et al. 2005). Molecular docking showed that U1-SCTRX-lg1a binds to SER 48 and THR 160 of the chain, occupying an important active site for this protein. Therefore, this interaction suggests that U1-SCTRX-lg1a may compete with the ligand site of this receptor and can reduce its enzymatic activity, consequently modifying intracellular functions such as nucleic acid metabolism and altering homeostasis, resulting in Gram-negative bacterial death. Table 4: Study of receptor binding location prediction using the PDBsum server compared to PatchDock docking analysis and molecular dynamics simulation. The bold residue is shared among binding sites, and the underlined residues are those near the binding amino acids. PM Rank PDB ID Sites with interactions involving ligands (PDBsum) Ligand binding site residues (PatchDock) Molecular Dynamics Simulation 41 1y79 426(a), 469(a), 473(a), 498(a) , 593(a), 594(a) 601(a), 607(a), 611(a), 614(a), 700(a), 702(a) 69(a), 106(a),141(a), 268(a), 428(a), 491(a) NONE 260 1k4m 11(a), 12(a), 40(a), 45(a), 46(a) 85(a), 107(a), 109(a), 110(a), 118(a), 134(a), 177(a), 179(a), 181(a), 182(a), 185(a) 69(a), 21(b), 67(b), 66(c) NONE 74 1jqn 396(a), 506(a), 543(a), 587(a), 699(a), 773(a), 832(a), 881(a), 901(a), 902(a) 244(a), 384(a), 392(a) , 470(a), 532(a), 574(a) NONE 289 1h1l 61(a), 87(a), 95(a), 153(a), 190(a), 194(a), 273(a), 355(a), 440(a), 1479(a), 1480(a), 68(b), 93(b), 106 (b), 107(b), 151(b), 349(d), 353(d) 361(b), 6(d), 8(d), 9(d) NONE 113 1m2x 116(a), 118(a), 119(a), 120(a), 167(a), 196(a), 237(a), 221(a), 263(a), 285(a), 288(a), 811(a), 901(a), 902(a) 43(b), 66(b), 227(b), 43(d), 66(d), 225(d), 277(d) NONE 215 1wxi 46(a), 48(a) , 52(a) , 53(a) , 82(a), 88(a), 140(a) , 173(a) , 160(a) , 165(a), 189(a), 400(a), 500(a), 600(a), 700(a), 223(a), 48(a) , 51(a) , 136(a), 142(a), 160(a) , 172(a) , 176(a) 48(a) , 51(a) , 90(a) , 142(a) , 146(a) , 172(a) , 223(a), 225(a), 226(a) ID PDB (Identification code in protein data bank), PM Rank (Rank in PharmMapper server), 86(a) (Residue number/ Chain ID). AutoDock Vina analysis The application of AutoDock Vina allowed a more detailed analysis of the protein-ligand interactions, providing valuable information about the binding affinity and the main amino acid residues involved in the interaction. A higher score represents a more favorable interaction between molecules (TROTT, Oleg and OLSON, Arthur J. 2010). The models of the ligands indicated in the literature were obtained for comparison between their docking and the two lateral portions of the U1-SCRTX-lg1 toxin derived from Loxosceles gaucho venom. The results suggest that the C-terminus of the U1-SCRTX-lg1a peptide exhibits a more favorable interaction with the 1wxi receptor than the N-terminus. Furthermore, the results showed that the binding between the NH 3 -dependent NAD + synthetase receptor and the diphosphate ligand (O7P24), which is maintained through stacking interactions involving ARG 142 , ILE 47 and SER 48 with the adenine ring of the molecule (JAUCH, Ralf et al. 2005), was the one with the highest score. This connection was found in the literature, in docking with PatchDock, in molecular dynamics simulation and most favorably in AutoDock Vina. Table 5: Interaction results between 1wxi and U1-SCRTX-lg1a using AutoDock Vina sorted from lowest score to highest score. Score Ligand RMSD l.b. / u.b. -7.7 U1-SCRTX-lg1a - N term (VGTDFSGNDD) 0.0 -7.4 Adenosine Monophosphate (C10H14N5O7P) 0.0 -7.1 U1-SCRTX-lg1a - C term (GNDDISDVQK) 0.0 -4.7 Diphosphate (O7P24) 0.0 Score - measure used in the molecular docking process to assess the quality of the interaction between two molecules, Ligand - Ligand name - RMSD l.b. / u.b. (Root Mean Square Deviation), range of RMSD values that are considered acceptable for assessing process accuracy. The l.b. (lower bound) refers to the lower bound of the range, while the u.b. (upper bound) refers to the upper bound. Furthermore, in crystalline by JAUCH, Ralf et al, 2005, the ribose portions of the adenosine monophosphate nucleotides that engage in hydrogen bonding interactions with the hydroxyl side chain of the THR 160 group did not show greater affinity compared to the C-terminal portion of the peptide derived from spider venom in Autodock vina. This connection was found in the PatchDock results and in the literature, but it was broken after the molecular dynamics simulation. Conclusion The adoption of different bioinformatic tools was successful in prospecting potential receptors associated with the antimicrobial activity of U1-SCRTX-lg1a. It was used by PharmMapper to search receptors, PatchDock and AutoDock vina to mensurate interactions and UCSF chimera to molecular dynamics. At the end of this study, 6 potential receptors originating from Gram-negative organisms were found. The NH 3 -dependent NAD + synthetase presented the best result, which was associated with NAD + production, an important precursor in several cellular pathways from Escherichia coli K12. Therefore, the U1-SCRTX-lg1a interaction may disrupt the normal function of this enzyme, generating intracellular alteration and growth impairment, corroborating bacterial in vitro experiments. Finally, this study opens new ways to perform in vitro experiments to validate in silico results, as well as to design analogs to improve the biological activity of this peptide. Declarations Data and Software Availability The physicochemical properties were determined via the Heliquest server https://heliquest.ipmc.cnrs.fr/. Potential receptors were screened through PharmMapper available at http://www.lilab-ecust.cn/pharmmapper/. Sequence and files of receptors were downloaded from the website protein data bank https://www.rcsb.org/. A molecular docking method was used: PatchDock https://bioinfo3d.cs.tau.ac.il/PatchDock/ and https://vina.scripps.edu/. Ligand and receptor interactions and molecular presentation were built by the free software UCSF chimera (version 1.16) https://www.cgl.ucsf.edu/chimera/. For the prediction of ligand sites, the PDBsum server was used http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/. All files used in this study are available in https://github.com/loxoscelesgaucho/loxosceles-in-silico.git. Acknowledgements We thank all the team of the Protein Chemistry Laboratory at the Laboratory from Applied Toxinology (LETA - Butantan Institute, Brazil) for the constant support and encouragement. Funding This research received financial support from the Research Support Foundation of the State of São Paulo (FAPESP/CeTICS), grant number 2013/07467-1, National Council for Scientific and Technological Development (CNPq) process 472744/2012-7 and from Higher Education Personnel Improvement Coordination (CAPES) process number 88887.663437/2022-00. Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References BANERJEE, Soojay et al. Structural studies on ADP activation of mammalian glutamate dehydrogenase and the evolution of regulation. Biochemistry, v. 42, n. 12, p. 3446–3456, 2003. BENFIELD, A. H.; HENRIQUES, S. T. Mode-of-action of antimicrobial peptides: membrane disruption vs. intracellular mechanisms. Front Med Technol 2: 610997. 2020. CAYCEDO, María Inés et al. Antibiotic Resistance: Origins, evolution and healthcare-associated infections. Revista Salud Uninorte, v. 34, n. 2, p. 494–505, 2018. CHAN, Carmen et al. Structural basis of activity and allosteric control of diguanylate cyclase. Proceedings of the National Academy of Sciences, v. 101, n. 49, p. 17084–17089, 2004. Chaves-Moreira, D. et al. (2017) ‘Highlights in the knowledge of brown spider toxins’, Journal of Venomous Animals and Toxins Including Tropical Diseases . BioMed Central Ltd., 23(1), pp. 1–12. doi: 10.1186/s40409-017-0097-8 . Chengxin Zhang, Peter L. Freddolino, and Yang Zhang. COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information. Nucleic Acids Research, 45: W291-299 (2017). CHAVES-MOREIRA, Daniele et al. Brown spider (Loxosceles) venom toxins as potential biotools for the development of novel therapeutics. Toxins, v. 11, n. 6, p. 355, 2019. DA SILVA, Bruna S. et al. Doderlin: Isolation and characterization of a broad-spectrum antimicrobial peptide from Lactobacillus acidophilus. Research in Microbiology, v. 174, n. 3, p. 103995, 2023. DAL MAS, Caroline et al. Effects of the natural peptide crotamine from a South American rattlesnake on Candida auris, an emergent multidrug antifungal resistant human pathogen. Biomolecules, v. 9, n. 6, p. 205, 2019. DUHOVNY, Dina; NUSSINOV, Ruth; WOLFSON, Haim J. Efficient unbound docking of rigid molecules. In: International workshop on algorithms in bioinformatics. Springer, Berlin, Heidelberg, 2002. p. 185–200. FERRI, Maurizio et al. Antimicrobial resistance: a global emerging threat to public health systems. Critical reviews in food science and nutrition, v. 57, n. 13, p. 2857–2876, 2017. Frontiers in microbiology, v. 4, p. 353, 2013 Gasteiger E., Hoogland C., Gattiker A., Duvaud S., Wilkins M.R., Appel R.D., Bairoch A.; Protein Identification and Analysis Tools on the Expasy Server; (In) John M. Walker (ed): The Proteomics Protocols Handbook, Humana Press (2005). pp. 571–607. Gautier,R. et al. (2008) HELIQUEST: A web server to screen sequences with specic α-helical properties.Bioinformatics, 24, 2101–2102. GLAAB, Enrico. Building a virtual ligand screening pipeline using free software: a survey. Briefings in Bioinformatics, v. 17, n. 2, p. 352–366, 2016. Harris, F.; Dennison, S.R.; Phoenix, D.A. Anionic antimicrobial peptides from eukaryotic organisms. Curr. Protein Pept. Sci. 2009, 10 , 585–606. Heineken and Lowe (1832) ‘Descriptions of two species of Araneidae, natives of Madeira’, The Zoological Journal, 5, pp. 320–326. JAUCH, Ralf et al. Structures of Escherichia coli NAD synthetase with substrates and products reveal mechanistic rearrangements. Journal of Biological Chemistry, v. 280, n. 15, p. 15131–15140, 2005. Jianyi Yang, Yang Zhang. I-TASSER server: new development for protein structure and function predictions, Nucleic Acids Research, 43: W174-W181, 2015 LANDECKER H. Antibiotic Resistance and the Biology of History. Body & Society. 2015; 22(4):19–52. LIU, Xiaofeng et al. PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic acids research, v. 38, n. suppl_2, p. W609-W614, 2010. MUNIZ SEIF, Elias Jorge; ICIMOTO, Marcelo Yudi; DA SILVA JUNIOR, Pedro Ismael. In silico bioprospecting of receptors for Doderlin: an antimicrobial peptide isolated from Lactobacillus acidophilus. In Silico Pharmacology, v. 11, n. 1, p. 11, 2023. SCHNEIDMAN-DUHOVNY, Dina et al. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic acids research, v. 33, n. suppl_2, p. W363-W367, 2005. SEGURA-RAMÍREZ, Paula J.; SILVA JÚNIOR, Pedro I. Loxosceles gaucho spider venom: an untapped source of antimicrobial agents. Toxins, v. 10, n. 12, p. 522, 2018. UCSF Chimera–a visualization system for exploratory research and analysis. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. J Comput Chem. 2004 Oct;25(13):1605-12. NELSON, David L.; COX, Michael M. Princípios de bioquímica de Lehninger. In: Aminoácidos, peptídeos e proteínas. p78-84. Artmed Editora, 2022. PIONTEK, Klaus et al. Structure determination and refinment of Bacillus stearothermophilus lactate dehydrogenase. Proteins: Structure, Function, and Bioinformatics, v. 7, n. 1, p. 74–92, 1990. RICILUCA, K. C. T. et al. Rondonin an antifungal peptide from spider (Acanthoscurria rondoniae) haemolymph. Results in immunology, v. 2, p. 66–71, 2012. TROTT, Oleg; OLSON, Arthur J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, v. 31, n. 2, p. 455–461, 2010. W Zheng, C Zhang, Y Li, R Pearce, EW Bell, Y Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014 (2021). Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014 (2021). Xia Wang, et al . Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs. J. Chem. Inf. Model., 2016, 56, 1175–1183 Xia Wang, et al . PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res., 2017, 45, W356-W360. Xia Wang, Yihang Shen, Shiwei Wang, Shiliang Li, Weilin Zhang, Xiaofeng Liu, Luhua Lai, Jianfeng Pei, Honglin Li. Atualização do PharmMapper 2017: um servidor da Web para identificação potencial de alvos de drogas com um banco de dados abrangente de farmacoforos alvo. Res. de Ácidos Nucleicos., 2017, 45, W356-W360. WELFORD, Richard WD et al. Incorporation of oxygen into the succinate co-product of iron (II) and 2-oxoglutarate dependent oxygenases from bacteria, plants and humans. FEBS letters, v. 579, n. 23, p. 5170–5174, 2005. WILMOUTH, Rupert C. et al. Structure and mechanism of anthocyanidin synthase from Arabidopsis thaliana. Structure, v. 10, n. 1, p. 93–103, 2002. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx 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. 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-3043813","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":208421909,"identity":"c22c0c64-6460-40ee-948e-49a80d07e00d","order_by":0,"name":"André Souza de Oliveira","email":"","orcid":"","institution":"Butantan Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"André","middleName":"Souza","lastName":"de Oliveira","suffix":""},{"id":208421910,"identity":"c3804b95-0baf-4777-862f-51bee3fb879a","order_by":1,"name":"Elias Jorge Muniz Seif","email":"","orcid":"https://orcid.org/0000-0001-9914-8897","institution":"Butantan Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elias","middleName":"Jorge Muniz","lastName":"Seif","suffix":""},{"id":208421911,"identity":"e18610a4-7a9f-4b90-a1de-9d970f7905fb","order_by":2,"name":"Pedro Ismael da Silva Junior","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACZgYGA4aKAyhibERoOXOAgYeBgbGBOC0gwNhGihZzdvYHxZXz7iTuZz97/MHPHQx2/RIJbI8r8GixbOYxMDy77VliD09eYmPvGYbkmTMS2A3P4NFicJiHwbBx2+HEHoYcwwbeNoZkgzMH2CQb8Gphf2DYOAeohf+NYeNf4rQwGBg2NgC1SOQYNgNtsTM43oBfC9gvDceeGffceGM4W7ZNIkGyvbHdEJ8Wc/7jzwwbau7ItvfnGHx822Zjz8/MfOwhXocBY8EAiS+R2ICIH5xamB8gC9jjVT4KRsEoGAUjEgAAe5tO5jdIsiAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6619-6489","institution":"Butantan Institute","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"Ismael da Silva","lastName":"Junior","suffix":""}],"badges":[],"createdAt":"2023-06-09 14:51:50","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3043813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3043813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38416243,"identity":"d30a253e-5acc-42b2-9756-bf3290463d4f","added_by":"auto","created_at":"2023-06-12 16:50:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289737,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization and physicochemical propertiesof U1-SCRTx-lg1a. \u003cstrong\u003e(A)\u003c/strong\u003e peptide helicoidal projection top view, \u003cstrong\u003eyellow\u003c/strong\u003erepresents nonpolar residues, \u003cstrong\u003eblue\u003c/strong\u003erepresents basic residues, \u003cstrong\u003epink\u003c/strong\u003erepresents asparagine and glutamine, \u003cstrong\u003egray\u003c/strong\u003e represents alanine and glycine, \u003cstrong\u003ered\u003c/strong\u003e represents aspartic acid, \u003cstrong\u003elilac\u003c/strong\u003e represents serine and tyrosine. The arrow indicates the hydrophobic portion of this projection. \u003cstrong\u003e(B)\u003c/strong\u003e Three-dimensional structure designed by UCSF software relative to the position of each amino acid residue. \u003cstrong\u003e(C)\u003c/strong\u003e The hydrophobicity is represented; \u003cstrong\u003ered\u003c/strong\u003e represents acidic, \u003cstrong\u003elight green\u003c/strong\u003e aromatic, \u003cstrong\u003eblue\u003c/strong\u003e basic, \u003cstrong\u003egray\u003c/strong\u003e aliphatic and \u003cstrong\u003eblack\u003c/strong\u003e polar. \u003cstrong\u003e(D)\u003c/strong\u003eSequence peptide primary structure. The table at the end shows the amino acid sequence, molecular weight, net charge (NC), hydrophobicity index/hydrophobic moment (H/μH), theoretical isoelectric point (pl), instability index (estimate of the stability of peptide in a test tube) and GRAVY (value for a peptide calculated as the sum of hydropathy values of all the amino acids divided by the number of residues in the sequence).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3043813/v1/c184d360a3b7e63f2edf0669.png"},{"id":38416248,"identity":"a61aff7f-b203-4e41-8a8f-8b79cc6d7e04","added_by":"auto","created_at":"2023-06-12 16:50:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":417250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e NH(3)-dependent NAD(+) synthetase (dark gray) and U1-SCRTX-lg1a (red) formed a complex after molecular docking. \u003cstrong\u003e(B)\u003c/strong\u003e Ten hydrogen bonds (blue) were identified between the acceptor and ligand, with distances less than 4 angstroms.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3043813/v1/9db8bf81ce4c5e5c52fda61b.png"},{"id":38416247,"identity":"80bf544a-b9a2-4f29-9f56-12acd25c1996","added_by":"auto","created_at":"2023-06-12 16:50:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1072493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e NH(3)-dependent NAD(+) synthetase and U1-SCRTX-lg1a (center) formed a complex after dynamic molecular simulation and solvent (withe): energy free -214,890.21 kJ/mol. \u003cstrong\u003e(B)\u003c/strong\u003e Focus on U1-SCRTX-lg1a. \u003cstrong\u003e(C) \u003c/strong\u003eSurface hydrophobicity\u003cstrong\u003e \u003c/strong\u003eto the interaction between the receptor and ligand.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3043813/v1/458923af4e43dd2a85551d44.png"},{"id":38416468,"identity":"db7077b8-09b4-49f8-9b2b-5b06cfa2805a","added_by":"auto","created_at":"2023-06-12 16:58:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2124868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3043813/v1/e1dbc048-e951-4a8a-bc62-10f1316a03ca.pdf"},{"id":38416466,"identity":"e35e3740-ce58-4abb-a6c1-a9d46833116f","added_by":"auto","created_at":"2023-06-12 16:58:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":61518,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3043813/v1/d5691a4db8b89e9053744b5c.xlsx"},{"id":38416245,"identity":"765151b0-9436-4218-93ad-d9587f208a85","added_by":"auto","created_at":"2023-06-12 16:50:39","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17303,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3043813/v1/d699358d9bd0483218bb2356.xlsx"},{"id":38416467,"identity":"d96a487c-94ce-47ba-b55b-3d0972b3e204","added_by":"auto","created_at":"2023-06-12 16:58:39","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11846,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3043813/v1/eca724c88f37e44e9ab8f624.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIn silico prospection of receptors associated with the biological activity of U1-SCTRX-lg1a: an antimicrobial peptide isolated from the venom of \u003cem\u003eLoxosceles gaucho\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eLoxosceles sp.\u003c/em\u003e (Araneae, Sicariidae) belongs to the subphylum Chelicerata, one of the three evolutionary lineages of arthropods. Systematically, within this subphylum, the class Arachnida comprises most of the Chelicerata, including forms such as spiders, scorpions, mites, and ticks (Ruppert \u0026amp; Barnes, 1996).\u003c/p\u003e \u003cp\u003eBrown spiders are a group of spiders that produce venoms with human clinical manifestations. This venom has been studied for at least 60 years in different research groups worldwide. The extraction and characterization of this venom was motivated by several cases related to loxoscelism, injury caused by spider bites (Chaves-Moreira et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThree main toxin families are present in spider venom: phospholipases-D, astacin-like metalloproteases, and inhibitor cystine knot (ICK) peptides. Additionally, serine proteases, serpins, hyaluronidases, venom allergens, and translationally controlled tumor protein (TCTP) are also present. These toxins have essential biological properties that enable them to interact with a range of distinct molecular targets. Therefore, this toxin can be a source of bioactive molecules for use in the pharmaceutical industry (Chaves-Moreira et al., 2019).\u003c/p\u003e \u003cp\u003eThe emergence of new multiresistant microorganisms is increasing each year, which can increase the risks of mortality and morbidity, consequently overloading public health systems and causing financial losses for countries stricken. Otherwise, the low efficiency of traditional medicines and treatments against these organisms requires research for the development of new bioactive molecules to control diseases caused by microorganisms (FERRI, Maurizio et al., 2013).\u003c/p\u003e \u003cp\u003eU1-UCRTX-lg1a (VGTDFSGNDDISDVQK) is an anionic peptide derived from phospholipase-D isolated from spider \u003cem\u003eL. gaucho\u003c/em\u003e venom. This peptide showed antibacterial activity against \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eEnterobacter cloacae\u003c/em\u003e presented minimal inhibitory concentrations between 2 and 5 \u0026micro;M. In this way, activity was only observed in gram-negative bacteria. Furthermore, in an experiment with human erythrocytes, hemolytic activity was not observed with U1-SCRTX-lg1a at within the inhibitory concentrations. (Segura. P and Junior. P, 2018). However, your active mechanism and molecular targets are not well elucidated.\u003c/p\u003e \u003cp\u003eVirtual ligand screening and molecular docking are computational methods to identify protein targets and interactions. Among them, those methods can be used for the initial development of new drugs; likewise, simulations have been performed using many parameters at the same time since they are faster and more efficient than \u003cem\u003ein vitro\u003c/em\u003e experiments (GLAAB, Enrico \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this process, there are steps: protein and ligand preparation, creation of molecular models, molecular docking, analysis, and visualization of the results (Seif. EJM et al., 2023). In contrast, computational biology requires a high processing rate; however, this barrier has been superseded due to computational advancement and the emergence of online servers to perform analysis (Andrusier \u0026amp; Duhovny, 2002).\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to use free bioinformatics tools to perform virtual screening and identify potential receptors associated with the antimicrobial activity of the U1-SCRTX-lg1a peptide and to further describe those ligand-receptor interactions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePeptide characterization and minimum free energy\u003c/h2\u003e \u003cp\u003eThe Heliquest server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://heliquest.ipmc.cnrs.fr/\u003c/span\u003e\u003cspan address=\"https://heliquest.ipmc.cnrs.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Gautier et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) was used to determine the physicochemical parameters (net charge, hydrophobicity moment and molecular weight) of U1-SCRTX-lg1peptide (VGTDFSGNDDISDVQK). Theoretical pI, instability index, and grand average of hydropathicity (GRAVY) were obtained by the Expasy Server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/cgi-bin/protparam/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/cgi-bin/protparam/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Gasteiger E. et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Pepcalc server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pepcalc.com/\u003c/span\u003e\u003cspan address=\"https://pepcalc.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e To build the peptide tridimensional structure, free energy mini was used by the server I-TASSER (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zhanggroup.org/I-TASSER/\u003c/span\u003e\u003cspan address=\"https://zhanggroup.org/I-TASSER/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Zheng W et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe state of minimum free energy was obtained using UCSF Chimera software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cgl.ucsf.edu/chimera/\u003c/span\u003e\u003cspan address=\"https://www.cgl.ucsf.edu/chimera/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Pettersen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The following settings were used: steepest descendent steps (100000); steepest descendent steps size \u0026Aring; (0.02); conjugate gradient steps (10), conjugate gradient steps size \u0026Aring; (0.02), update interval (10), fixed atoms (none) after H, H-B, charges, and SR (amber force field ff14SB).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSearch for potential Gram-negative targets\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo identify potential receptors for U1-SCRTX-lg1a, the PharmMapper server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lilab-ecust.cn/pharmmapper/\u003c/span\u003e\u003cspan address=\"http://www.lilab-ecust.cn/pharmmapper/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was used (Wang et al., 2017). The peptide structure was submitted using the following parameters: generate conformers (yes); maximum generated conformations (300); full/complete pharmacophore mapping; all targets selected (v2010, 7302), and number of reserved matched targets (300). Those results were ranked by normalized fit score. All targets were classified based on origin and catalytic activity (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e in the microorganism. It selected a total of forty-nine proteins derived from Gram-negative bacteria without mutations in their models, and those structures were provided by the Protein Data Bank (PDB) server (\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\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTarget and peptide molecular docking\u003c/h2\u003e \u003cp\u003eMolecular docking between U1-SCRTX-lg1a and the selected targets was performed by PatchDock software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfo3d.cs.tau.ac.il/PatchDock/\u003c/span\u003e\u003cspan address=\"https://bioinfo3d.cs.tau.ac.il/PatchDock/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Schneidman-Duhovny et al., 2005). The PatchDock parameters were set to clustering RMSD (4.0) and complex type (default). The docking results were ranked based on the major score value to determine the optimal binding of U1-SCRTX-lg1a. AREA, ACE (effective atomic contact energies), and transformation values were also collected. The docking results were classified by the major score value. In addition, the better target found in PatchDock was also performed by AutoDock vina (TROTT, Oleg; OLSON, Arthur J, 2010) to examine the affinity of the C-terminus and N-terminus segments and native ligand.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of ligand and receptor interactions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe U1-SCRTX-lg1a and receptor interaction was analyzed using UCSF Chimera software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cgl.ucsf.edu/chimera/\u003c/span\u003e\u003cspan address=\"https://www.cgl.ucsf.edu/chimera/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Pettersen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The find bond tool was used with relaxed constraint binding (2 \u0026Aring; and 20 degrees) to examine the interaction. In this study, only hydrogen bonds with distances less than 4 \u0026Aring; between heavy atoms were considered. Furthermore, information on residue electron donors and acceptors was collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eComparison of predicted ligand sites\u003c/h2\u003e \u003cp\u003eReceptor ligand sites were predicted using the PDBsum server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e For this, the UniProt code (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and the FASTA sequence of better scores found in molecular docking analysis were used. The results were compared to the ligand site obtained by molecular docking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMolecular dynamics simulation\u003c/h2\u003e \u003cp\u003eMolecular dynamics simulations were performed between PDB\u003csub\u003eid\u003c/sub\u003e:1wxi as a receptor and U1-SCRTX-lg1a as a ligand using Chimera 1.6 software. The simulations utilized the TIP3BOX solvent model, a 1:1 ratio, and 100 steps from steepest descent with a size of 0.02 \u0026Aring; to steepest descent and conjugate gradient steps with a size of 0.02 \u0026Aring;. Intermodal and intramodal hydrogen bonds were identified with constraints relaxed at 2 angstroms and 20 degrees.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003ePeptide Characterization and Minimum Free Energy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe physicochemical analysis using Heliquest, Expasy, and Pepcal servers showed that the U1-SCTRX-lg1a peptide has a molecular weight of 1695.7 g/mol, a net charge of -3, and a hydrophobic index/hydrophobic moment of 0.083/0.189. Energy minimization of the peptide structure resulted in a free energy of -1502.51 kJ/mol (figure 1). The peptide contains four acidic, one aromatic, one alkaline, three aliphatic, and five polar amino acids. Aspartate has a negatively charged R group, phenylalanine has an aromatic side chain with relatively hydrophobic characteristics, lysine is positively charged and hydrophilic, and valine and isoleucine tend to group together inside proteins, stabilizing the protein structure through hydrophobic interactions. Serine, threonine, asparagine, and glutamine are more water-soluble amino acids that contain functional groups capable of forming hydrogen bonds (NELSON, David L.; COX, Michael M., 2022).\u003c/p\u003e\n\u003cp\u003eThe peptide presented a theoretical mass of approximately 1695.75 g/mol and is relatively small. The model obtained from the I-TASSER server showed a confidence score (C-score) of -0.90, which was used to estimate the quality of the predicted models. C-scores range from [-5,2], where a higher C-score indicates a more confident model (Jianyi Yang et al., 2015). The best model was used for target search and molecular docking.\u003c/p\u003e\n\u003cp\u003ePhysicochemical\u0026nbsp;properties such\u0026nbsp;as charge, hydrophobicity and residue number are usually found in membranolytic antimicrobial peptides, and membrane injury is the main activation mechanism due to potential differences between membranes and peptides\u0026nbsp;(BENFIELD, A. H. and HENRIQUES, S. T., 2020).\u003c/p\u003e\n\u003cp\u003eIn contrast, hemolytic activity was not observed with U1-SCRTX-lg1a at inhibitory concentrations. Therefore, this peptide uses nonmembranolytic activity mechanisms to inhibit bacterial growth. Thus, antimicrobial activity occurs by interacting with intracellular targets, promoting homeostasis impairment and cell death. This mechanism is particularly found in noncharged or low-charged peptides, such as Doderlin (DA SILVA, Bruna S. et al, 2023), Rondonin (RICILUCA, K. C. T. et al, 2012) and Crotamine (DAL MAS, Caroline et al, 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Gram-negative target identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first step of bioprospecting receptors for U1-SCRTX-lg1a was determined by the PharmMapper Server. This is an online tool that uses pharmacophore mapping techniques to identify potential drug targets that can be used for virtual screening. The tool has a database of more than 7,000 proteins built based on protein information and pharmacophore models. When inserting a molecule, the tool generates a ranked list of proteins based on the similarity of their pharmacophores. This enables the identification of possible drug targets and the development of new compounds with therapeutic potential. (LIU, Xiaofeng et al., 2010).\u003c/p\u003e\n\u003cp\u003eThe PharmMapper search resulted in 300 general targets (table S1), among which\u0026nbsp;49 originated\u0026nbsp;from Gram-negative targets (table S2); however, in this\u0026nbsp;research, only\u0026nbsp;10 had better scores based on the PatchDock results (table 1). The PharmMapper results\u0026nbsp;were\u0026nbsp;normalized to fit the score between 8.216 and 9.998. The better receptors found using this method were peptidyl-dipeptidase dcp [PDB\u003csub\u003eid\u003c/sub\u003e: 1y79], phosphoenolpyruvate carboxylase [PDB\u003csub\u003eid\u003c/sub\u003e: 1jqn] and thermoresistant gluconokinase [PDB\u003csub\u003eid\u003c/sub\u003e: 1ko8], all of which originated from \u003cem\u003eEscherichia coli.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe\u0026nbsp;\u003c/em\u003eU1-SCTRX-lg1a peptide exhibited antimicrobial activity against \u003cem\u003eEscherichia coli\u003c/em\u003e strains SBS636 and D31 with a minimum inhibitory concentration of 4.6 \u0026mu;M (Segura. P and Junior. P, 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e The 10 targets originating from Gram-negative microorganisms resulting from PharmMapper based on molecular docking. The rank of targets was ordered by major normalized fit score.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e\u003cstrong\u003eID PDB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormalized Fit Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCatalytic activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrigin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1y79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e9.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003ePeptidyl-dipeptidase dcp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003eHydrolysis of unblocked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1jqn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e9.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003ePhosphoenolpyruvate carboxylase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003eoxaloacetate + phosphate = hydrogencarbonate + phosphoenolpyruvate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003cem\u003e\u0026nbsp;K12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1ko8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e9.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003eThermoresistant gluconokinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003eATP + D-gluconate = 6-phospho-D-gluconate + ADP + H\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003cem\u003e\u0026nbsp;K12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1m2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e9.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003eMetallo-beta-lactamase type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003ea beta-lactam + H2O = a substituted beta-amino acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eElizabethkingia meningoseptica\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1hmu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e9.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003eChondroitinase-AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003eEliminative degradation of polysaccharides containing 1,4-beta-D-hexosamine\u0026nbsp;and 1,3-beta-D-glucuronosyl linkages to disaccharides containing 4-deoxy-beta-D-gluc-4-enuronosyl groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003ePedobacter heparinus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1wxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e8812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-dependent\u0026nbsp;NAD\u003csup\u003e\u0026nbsp;\u003c/sup\u003esynthetase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003eATP + deamido-NAD\u003csup\u003e+\u003c/sup\u003e + NH4\u003csup\u003e+\u003c/sup\u003e = AMP + diphosphate + H\u003csup\u003e+\u003c/sup\u003e + NAD\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003cem\u003e\u0026nbsp;K12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1k4m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e8.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003eNicotinate-nucleotide adenylyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003eATP + H\u003csup\u003e+\u003c/sup\u003e + nicotinate beta-D-ribonucleotide = deamido-NAD\u003csup\u003e+\u003c/sup\u003e + diphosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003cem\u003e\u0026nbsp;K12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1geg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e8.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003eDiacetyl reductase [(S)-acetoin forming]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003e(S)-acetoin + NAD\u003csup\u003e+\u003c/sup\u003e = diacetyl + H\u003csup\u003e+\u003c/sup\u003e + NADH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.873720136518772%\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.703071672354948%\"\u003e\n \u003cp\u003e1kp2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.870307167235495%\"\u003e\n \u003cp\u003e829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\"\u003e\n \u003cp\u003eArgininosuccinate synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.69283276450512%\"\u003e\n \u003cp\u003eATP + L-aspartate + L-citrulline = 2-(N(omega)-L-arginino) succinate + AMP + diphosphate + H\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003cem\u003e\u0026nbsp;K12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eID PDB\u003c/strong\u003e - Identification code in protein data bank, \u003cstrong\u003ePM Rank\u0026nbsp;\u003c/strong\u003e- Rank in PharmMapper server,\u003cstrong\u003e\u0026nbsp;Normalized Fit Score\u003c/strong\u003e - value obtained by ratio of fit score and number of features,\u003cstrong\u003e\u0026nbsp;Catalytic activity\u0026nbsp;\u003c/strong\u003e- obtained from uniprot.org \u003cstrong\u003eOrigin\u0026nbsp;\u003c/strong\u003e- Species from the target was isolated\u003cstrong\u003e\u0026nbsp;None\u0026nbsp;\u003c/strong\u003e- catalytic activity not found.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTarget and peptide molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatchDock is software that utilizes a docking technique based on complementarity principles to model the molecular docking between two proteins. This allows for the simulation of protein docking in different contexts. Molecular docking is a technique used to study the interaction between molecules, especially between a protein and a ligand, with the aim of predicting their binding affinity (Schneidman-Duhovny, Dina et al, 2005).\u003c/p\u003e\n\u003cp\u003eMolecular docking was performed by PatchDock for all 49 targets originating from Gram-negative\u0026nbsp;organisms\u0026nbsp;(table S3); however, in this\u0026nbsp;work, only\u0026nbsp;10 targets with better docking scores were identified (table 2).\u003c/p\u003e\n\u003cp\u003eThe docking results indicated score (10702 to 6066), area (1498.70 to 728.40), and ACE (417.90 to -152.8). The ranking of receptors observed in PatchDock differs from that in the PharmMapper search. Among all proteins studied, the highest dock score was found for peptidyl-dipeptidase dcp [PDB\u003csub\u003eid\u003c/sub\u003e: 1y79], nicotinate-nucleotide adenylyltransferase [PDB\u003csub\u003eid\u003c/sub\u003e: 1k4m], phosphoenolpyruvate carboxylase [PDB\u003csub\u003eid\u003c/sub\u003e: 1jqn], nitrogenase molybdenum-iron protein alpha chain [PDB\u003csub\u003eid\u003c/sub\u003e: 1h1l], metallo-beta-lactamase type 2 [PDB\u003csub\u003eid\u003c/sub\u003e: 1m2x], and NH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esynthetase [PDB\u003csub\u003eid\u003c/sub\u003e: 1wxi] (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Results of rigid docking obtained from PatchDock. U1-SCRTX-lg1a was used as the ligand, and the receptor target obtained by PharmMapper was used. The table is arranged according to the major scores.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"532\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e\u003cstrong\u003eID PDB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96992481203007%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.225563909774436%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kj/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1y79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96992481203007%\"\u003e\n \u003cp\u003ePeptidyl-dipeptidase dcp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.225563909774436%\" colspan=\"2\"\u003e\n \u003cp\u003e10702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1296.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e91.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1k4m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96992481203007%\"\u003e\n \u003cp\u003eNicotinate-nucleotide adenylyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.225563909774436%\" colspan=\"2\"\u003e\n \u003cp\u003e10126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1256.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e417.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1jqn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96992481203007%\"\u003e\n \u003cp\u003ePhosphoenolpyruvate carboxylase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.225563909774436%\" colspan=\"2\"\u003e\n \u003cp\u003e9974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1292.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e212.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1h1l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96992481203007%\"\u003e\n \u003cp\u003eNitrogenase molybdenum-iron protein alpha chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.225563909774436%\" colspan=\"2\"\u003e\n \u003cp\u003e9924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1446.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e265.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1m2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96992481203007%\"\u003e\n \u003cp\u003eMetallo-beta-lactamase type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.225563909774436%\" colspan=\"2\"\u003e\n \u003cp\u003e9748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1285.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e357.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1wxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.661654135338345%\" colspan=\"2\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esynthetase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.533834586466165%\"\u003e\n \u003cp\u003e9742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1223.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e38.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1ko8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.661654135338345%\" colspan=\"2\"\u003e\n \u003cp\u003eThermoresistant gluconokinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.533834586466165%\"\u003e\n \u003cp\u003e9556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1317.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e77.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1geg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.661654135338345%\" colspan=\"2\"\u003e\n \u003cp\u003eAcetoin(diacetyl) reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.533834586466165%\"\u003e\n \u003cp\u003e9534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1188.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e218.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1kp2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.661654135338345%\" colspan=\"2\"\u003e\n \u003cp\u003eArgininosuccinate synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.533834586466165%\"\u003e\n \u003cp\u003e9358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1488.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e367.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.406015037593985%\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.090225563909774%\"\u003e\n \u003cp\u003e1hmu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.661654135338345%\" colspan=\"2\"\u003e\n \u003cp\u003eChondroitinase-AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.533834586466165%\"\u003e\n \u003cp\u003e9340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.030075187969924%\"\u003e\n \u003cp\u003e1185.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.278195488721805%\"\u003e\n \u003cp\u003e214.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eID PDB\u003c/strong\u003e (Identification code in protein data bank), \u003cstrong\u003ePM Rank\u0026nbsp;\u003c/strong\u003e(Rank in PharmMapper server), \u003cstrong\u003eScore\u0026nbsp;\u003c/strong\u003e(Geometric shape complementarity score), \u003cstrong\u003eArea (\u003c/strong\u003eApproximate interface area of the complex), \u003cstrong\u003eACE (\u003c/strong\u003eAtomic effective contact energy).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of ligand and receptor interactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceptor-ligand interaction analysis plays a significant role in all biological processes, and computational tools are used to simulate these biological phenomena, such as three-dimensional structural modeling and molecular docking between ligands and receptors. Rigid docking is also a crucial tool in computational drug design, which enables efficient identification of connections between rigid molecules without any initial restrictions on position or orientation. The use of this approach enables a faster and more efficient exploration of optimal solutions, significantly reducing the computation time required to identify the correct binding configurations (Andrusier \u0026amp; Duhovny, 2002).\u003c/p\u003e\n\u003cp\u003eTo better understand the docking analysis, we studied only stronger hydrogen bonds (\u0026le; 4 \u0026Aring;) between U1-SCRTX-lg1a and the receptor. The H bonds of six highest docking scores are summarized in table 3. These results showed that peptide as a donor or acceptor electrons in hydrogen bond interactions.\u003c/p\u003e\n\u003cp\u003eRigid docking using PatchDock produced the following interactions: peptidyl-dipeptidase Dcp from \u003cem\u003eEscherichia coli\u003c/em\u003e with 7 bonds and phosphoenolpyruvate carboxylase from \u003cem\u003eEscherichia coli K12\u003c/em\u003e with 8 bonds. The NH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esynthetase from \u003cem\u003eEscherichia coli K12\u003c/em\u003e showed the highest number of hydrogen bonds (10 bonds) (figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e:\u0026nbsp;Hydrogen bonds and distance (\u0026le; 4 \u0026Aring;) for the six highest scores receptors and U1-SCRTX-lg1a obtained by a docking result by PatchDock.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDB ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.925465838509318%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKg/mol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.788819875776397%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDonor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.546583850931675%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcceptor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-A distance (\u0026Acirc;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1y79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003ePeptidyl-dipeptidase dcp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.925465838509318%\"\u003e\n \u003cp\u003e-75558.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.788819875776397%\"\u003e\n \u003cp\u003eARG 141.A NH1\u003c/p\u003e\n \u003cp\u003eARG 141.A NH2\u003c/p\u003e\n \u003cp\u003eARG 268.A NH2\u003c/p\u003e\n \u003cp\u003eASN 428.1A ND2\u003c/p\u003e\n \u003cp\u003eASN 491.A ND2\u003c/p\u003e\n \u003cp\u003eASN 8.L ND2\u003c/p\u003e\n \u003cp\u003eASP 9.L N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.546583850931675%\"\u003e\n \u003cp\u003eSER\u0026nbsp;6.LOG\u003c/p\u003e\n \u003cp\u003eASP 9.L OD1\u003c/p\u003e\n \u003cp\u003eGLN 15.L O\u003c/p\u003e\n \u003cp\u003eASP 4.L OD1\u003c/p\u003e\n \u003cp\u003eASP 4.L O\u003c/p\u003e\n \u003cp\u003eTHR 69.A OG1\u003c/p\u003e\n \u003cp\u003eTYR 106.A OH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e\u0026nbsp;2.008\u003c/p\u003e\n \u003cp\u003e3.966\u003c/p\u003e\n \u003cp\u003e3.814\u003c/p\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003cp\u003e3.633\u003c/p\u003e\n \u003cp\u003e3.941\u003c/p\u003e\n \u003cp\u003e3.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1k4m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003eNicotinate-nucleotide adenylyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.925465838509318%\"\u003e\n \u003cp\u003e-55867.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.788819875776397%\"\u003e\n \u003cp\u003ePHE 5.L N\u003c/p\u003e\n \u003cp\u003eLEU 69.A N\u003c/p\u003e\n \u003cp\u003eLYS 21.B NZ\u003c/p\u003e\n \u003cp\u003eLYS 67.B NZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.546583850931675%\"\u003e\n \u003cp\u003eASP 66.C O\u003c/p\u003e\n \u003cp\u003eGLN 15.L OE1\u003c/p\u003e\n \u003cp\u003eASP 10.L OD1\u003c/p\u003e\n \u003cp\u003eSER 6.L OG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e3.908\u003c/p\u003e\n \u003cp\u003e3.932\u003c/p\u003e\n \u003cp\u003e3.424\u003c/p\u003e\n \u003cp\u003e3.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1jqn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003ePhosphoenolpyruvate carboxylase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.925465838509318%\"\u003e\n \u003cp\u003e-91386.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.788819875776397%\"\u003e\n \u003cp\u003eASN 8.L N\u003c/p\u003e\n \u003cp\u003eASP 9.L N\u003c/p\u003e\n \u003cp\u003eGLN 15.L NE2\u003c/p\u003e\n \u003cp\u003eLYS 16.L NZ\u003c/p\u003e\n \u003cp\u003eARG 244.A\u003c/p\u003e\n \u003cp\u003eARG 244.A\u003c/p\u003e\n \u003cp\u003eLYS 384.A\u003c/p\u003e\n \u003cp\u003eARG 392.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.546583850931675%\"\u003e\n \u003cp\u003eGLN 532.A\u003c/p\u003e\n \u003cp\u003eGLN 532.A\u003c/p\u003e\n \u003cp\u003eALA 470.A\u003c/p\u003e\n \u003cp\u003eGLU 574.A\u003c/p\u003e\n \u003cp\u003eVAL 14.L\u003c/p\u003e\n \u003cp\u003eVAL 14.L\u003c/p\u003e\n \u003cp\u003eASP 4.L\u003c/p\u003e\n \u003cp\u003eILE 11.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003cp\u003e2.303\u003c/p\u003e\n \u003cp\u003e2.047\u003c/p\u003e\n \u003cp\u003e3.506\u003c/p\u003e\n \u003cp\u003e3.167\u003c/p\u003e\n \u003cp\u003e2.523\u003c/p\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003cp\u003e3.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1h1l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003eNitrogenase molybdenum-iron protein alpha chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.925465838509318%\"\u003e\n \u003cp\u003e-201573.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.788819875776397%\"\u003e\n \u003cp\u003eVAL 1.L N\u003c/p\u003e\n \u003cp\u003eLYS 361.B NZ\u003c/p\u003e\n \u003cp\u003eLYS 6.D NZ\u003c/p\u003e\n \u003cp\u003eASN 8.D ND2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.546583850931675%\"\u003e\n \u003cp\u003eSER 9.D OG\u003c/p\u003e\n \u003cp\u003eVAL 1.L O\u003c/p\u003e\n \u003cp\u003eASP 4.L O\u003c/p\u003e\n \u003cp\u003eASP 10.L OD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e3.673\u003c/p\u003e\n \u003cp\u003e2.483\u003c/p\u003e\n \u003cp\u003e3.789\u003c/p\u003e\n \u003cp\u003e3.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1m2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003eMetallo-beta-lactamase type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.925465838509318%\"\u003e\n \u003cp\u003e-66967.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.788819875776397%\"\u003e\n \u003cp\u003eLYS 16.L NZ\u003c/p\u003e\n \u003cp\u003eLYS 16.L NZ\u003c/p\u003e\n \u003cp\u003eLYS 43.B NZ\u003c/p\u003e\n \u003cp\u003eLYS 43.B NZ\u003c/p\u003e\n \u003cp\u003eLYS 66.B NZ\u003c/p\u003e\n \u003cp\u003eLYS 43.D NZ\u003c/p\u003e\n \u003cp\u003eLYS 66.D N\u003c/p\u003e\n \u003cp\u003eSER 225.D OG.A\u003c/p\u003e\n \u003cp\u003eTRP 277.D N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.546583850931675%\"\u003e\n \u003cp\u003eLYS 66.B O\u003c/p\u003e\n \u003cp\u003eASP 227.B OD2\u003c/p\u003e\n \u003cp\u003eSER 6.L OG\u003c/p\u003e\n \u003cp\u003eASP 10.L OD1\u003c/p\u003e\n \u003cp\u003eSER 12.L OG\u003c/p\u003e\n \u003cp\u003eILE 11.L O\u003c/p\u003e\n \u003cp\u003ePHE 5.L O\u003c/p\u003e\n \u003cp\u003eVAL 1.L O\u003c/p\u003e\n \u003cp\u003eGLN 15.L OE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e3.493\u003c/p\u003e\n \u003cp\u003e3.724\u003c/p\u003e\n \u003cp\u003e2.290\u003c/p\u003e\n \u003cp\u003e1.414\u003c/p\u003e\n \u003cp\u003e3.311\u003c/p\u003e\n \u003cp\u003e3.921\u003c/p\u003e\n \u003cp\u003e2.528\u003c/p\u003e\n \u003cp\u003e3.830\u003c/p\u003e\n \u003cp\u003e3.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1wxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esynthetase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.925465838509318%\"\u003e\n \u003cp\u003e-29952.81\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.788819875776397%\"\u003e\n \u003cp\u003eSER 6.L N\u003c/p\u003e\n \u003cp\u003eGLY 7.L N\u003c/p\u003e\n \u003cp\u003eSER 48.A OG\u003c/p\u003e\n \u003cp\u003eGLN 51.A N\u003c/p\u003e\n \u003cp\u003eASN 136.A ND2\u003c/p\u003e\n \u003cp\u003eARG 142.A NH2\u003c/p\u003e\n \u003cp\u003eTHR 160.A OG1\u003c/p\u003e\n \u003cp\u003eTHR 172.A N\u003c/p\u003e\n \u003cp\u003eASP 176.A N\u003c/p\u003e\n \u003cp\u003eASP 223.A N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.546583850931675%\"\u003e\n \u003cp\u003eASP 223.A OD2\u003c/p\u003e\n \u003cp\u003eASP 223.A OD2\u003c/p\u003e\n \u003cp\u003eASP 9.L O\u003c/p\u003e\n \u003cp\u003eASP 9.L OD1\u003c/p\u003e\n \u003cp\u003eVAL 1.L O\u003c/p\u003e\n \u003cp\u003eGLN 15.L OE1\u003c/p\u003e\n \u003cp\u003eASP 13.L OD1\u003c/p\u003e\n \u003cp\u003eVAL 14.L O\u003c/p\u003e\n \u003cp\u003eVAL 14.L O\u003c/p\u003e\n \u003cp\u003eASP 10.L OD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.956521739130435%\"\u003e\n \u003cp\u003e2.890\u003c/p\u003e\n \u003cp\u003e3.796\u003c/p\u003e\n \u003cp\u003e3.331\u003c/p\u003e\n \u003cp\u003e3.961\u003c/p\u003e\n \u003cp\u003e2.571\u003c/p\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003cp\u003e2.679\u003c/p\u003e\n \u003cp\u003e3.869\u003c/p\u003e\n \u003cp\u003e3.998\u003c/p\u003e\n \u003cp\u003e1.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDonor\u003c/strong\u003e (electron donor residue), \u003cstrong\u003eAcceptor\u003c/strong\u003e (electron acceptor residue),\u003cstrong\u003e\u0026nbsp;D-A distance\u0026nbsp;\u003c/strong\u003e(distance between heavy atoms of electron acceptor and donor),\u003cstrong\u003e\u0026nbsp;THR 3. L N\u0026nbsp;\u003c/strong\u003e(Residue name/ Number/ Chain ID/ Shared electron atom), L (Ligand ID).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLigand binding site analysis and molecular dynamic simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison between predicted binding sites from the PDBsum and those observed using Chimera 1.6 software revealed that the U1-SCRTX-lg1a interaction may be colocalized or closely related to residues of ligand sites of each receptor found in this work.\u003c/p\u003e\n\u003cp\u003eAmong the models with the six receptors with the highest dock scores,\u0026nbsp;NH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esynthetase\u0026nbsp;showed the most satisfactory peptide interaction. It revealed 4 closely and two colocalized residues of the active site. Although peptidyl-dipeptidase dcp and phosphoenolpyruvate carboxylase both presented only one close binding\u0026nbsp;site, the other 3\u0026nbsp;receptors did not\u0026nbsp;present\u0026nbsp;interactions with active\u0026nbsp;sites\u0026nbsp;(table 4).\u003c/p\u003e\n\u003cp\u003eTo better elucidate these interactions, molecular dynamics simulation was performed only between the peptide and NH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esynthetase receptors. This analysis resulted in an energy value for the protein complex of -214,890.21 kJ/mol, whereas with rigid docking, this value was -29.952.81. This result suggests that after the molecular dynamics simulation, the complex exhibits a more favorable energy value compared to the previous state (fig 3).\u003c/p\u003e\n\u003cp\u003eNAD\u003csup\u003e+\u003c/sup\u003e synthetase [1xwi] from \u003cem\u003eE. coli\u003c/em\u003e catalyzes ATP-dependent starch-NAD amidation to form NAD\u003csup\u003e+\u003c/sup\u003e. NAD plays roles in processes as diverse as calcium mobilization, DNA repair, and posttranslational modification of proteins in eukaryotes. (JAUCH, Ralf et al. 2005).\u003c/p\u003e\n\u003cp\u003eMolecular docking showed that U1-SCTRX-lg1a binds to SER\u003csub\u003e48\u003c/sub\u003e and THR\u003csub\u003e160\u003c/sub\u003e of the chain, occupying an important active site for this protein. Therefore, this interaction suggests that U1-SCTRX-lg1a may compete with the ligand site of this receptor and can reduce its enzymatic activity, consequently modifying intracellular functions such as nucleic acid metabolism and altering homeostasis, resulting in Gram-negative bacterial death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Study of receptor binding location prediction using the PDBsum server compared to PatchDock docking analysis and molecular dynamics simulation. The \u003cstrong\u003ebold\u003c/strong\u003e residue is shared among binding sites, and the \u003cu\u003eunderlined\u003c/u\u003e residues are those near the binding amino acids.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.54954954954955%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.82882882882883%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDB ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSites with interactions involving ligands (PDBsum)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand binding site residues (PatchDock)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.54954954954955%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.82882882882883%\"\u003e\n \u003cp\u003e1y79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e426(a), 469(a), 473(a), \u003cu\u003e498(a)\u003c/u\u003e, 593(a), 594(a) 601(a), 607(a), 611(a), 614(a), 700(a), 702(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e69(a), 106(a),141(a), 268(a), 428(a), \u003cu\u003e491(a)\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003eNONE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.54954954954955%\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.82882882882883%\"\u003e\n \u003cp\u003e1k4m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e11(a), 12(a), 40(a), 45(a), 46(a) 85(a), 107(a), 109(a), 110(a), 118(a), 134(a), 177(a), 179(a), 181(a), 182(a), 185(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e69(a), 21(b), 67(b), 66(c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003eNONE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.54954954954955%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.82882882882883%\"\u003e\n \u003cp\u003e1jqn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e\u003cu\u003e396(a),\u003c/u\u003e 506(a), 543(a), 587(a), 699(a), 773(a), 832(a), 881(a), 901(a), 902(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e244(a), 384(a), \u003cu\u003e392(a)\u003c/u\u003e, 470(a), 532(a), 574(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003eNONE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.54954954954955%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.82882882882883%\"\u003e\n \u003cp\u003e1h1l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e61(a), 87(a), 95(a), 153(a), 190(a), 194(a), 273(a), 355(a), 440(a), 1479(a), 1480(a), 68(b), 93(b), 106 (b), 107(b), 151(b), 349(d), 353(d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e361(b), 6(d), 8(d), 9(d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003eNONE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.54954954954955%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.82882882882883%\"\u003e\n \u003cp\u003e1m2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e116(a), 118(a), 119(a), 120(a), 167(a), 196(a), 237(a), 221(a), 263(a), 285(a), 288(a), 811(a), 901(a), 902(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e43(b), 66(b), 227(b), 43(d), 66(d), 225(d), 277(d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003eNONE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.54954954954955%\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.82882882882883%\"\u003e\n \u003cp\u003e1wxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e46(a), \u003cstrong\u003e48(a)\u003c/strong\u003e, \u003cu\u003e52(a)\u003c/u\u003e, \u003cu\u003e53(a)\u003c/u\u003e, 82(a), 88(a), \u003cu\u003e140(a)\u003c/u\u003e, \u003cu\u003e173(a)\u003c/u\u003e, \u003cstrong\u003e160(a)\u003c/strong\u003e, 165(a), 189(a), 400(a), 500(a), 600(a), 700(a),\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e223(a), \u003cstrong\u003e48(a)\u003c/strong\u003e, \u003cu\u003e51(a)\u003c/u\u003e, \u003cu\u003e136(a),\u003c/u\u003e 142(a), \u003cstrong\u003e160(a)\u003c/strong\u003e, \u003cu\u003e172(a)\u003c/u\u003e, \u003cu\u003e176(a)\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.207207207207208%\"\u003e\n \u003cp\u003e\u003cstrong\u003e48(a)\u003c/strong\u003e, \u003cu\u003e51(a)\u003c/u\u003e, \u003cu\u003e90(a)\u003c/u\u003e, \u003cu\u003e142(a)\u003c/u\u003e, \u003cu\u003e146(a)\u003c/u\u003e, \u003cu\u003e172(a)\u003c/u\u003e, 223(a), 225(a), 226(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eID PDB\u003c/strong\u003e (Identification code in protein data bank), \u003cstrong\u003ePM Rank\u0026nbsp;\u003c/strong\u003e(Rank in PharmMapper server), \u003cstrong\u003e86(a)\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;(Residue number/ Chain ID).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutoDock Vina analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe application of AutoDock Vina allowed a more detailed analysis of the protein-ligand interactions, providing valuable information about the binding affinity and the main amino acid residues involved in the interaction. A higher score represents a more favorable interaction between molecules (TROTT, Oleg and OLSON, Arthur J. 2010).\u003c/p\u003e\n\u003cp\u003eThe models of the ligands indicated in the literature were obtained for comparison between their docking and the two lateral portions of the U1-SCRTX-lg1 toxin derived from \u003cem\u003eLoxosceles gaucho\u003c/em\u003e venom.\u003c/p\u003e\n\u003cp\u003eThe results suggest that the C-terminus of the U1-SCRTX-lg1a peptide exhibits a more favorable interaction with the 1wxi receptor than the N-terminus. Furthermore, the results showed that the binding between the\u0026nbsp;NH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esynthetase\u0026nbsp;receptor and the diphosphate ligand (O7P24), which is maintained through stacking interactions involving\u0026nbsp;ARG\u003csub\u003e142\u003c/sub\u003e, ILE\u003csub\u003e47\u003c/sub\u003e and SER\u003csub\u003e48\u003c/sub\u003e with the adenine ring of the molecule (JAUCH, Ralf et al. 2005), was the one with the highest score. This connection was found in the literature, in docking with PatchDock, in molecular dynamics simulation and most favorably in AutoDock Vina.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e Interaction results between 1wxi and U1-SCRTX-lg1a using AutoDock Vina sorted from lowest score to highest score.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"350\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.759312320916905%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.306590257879655%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.93409742120344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSD l.b. / u.b.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.759312320916905%\"\u003e\n \u003cp\u003e-7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.306590257879655%\"\u003e\n \u003cp\u003eU1-SCRTX-lg1a - N term (VGTDFSGNDD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.93409742120344%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.759312320916905%\"\u003e\n \u003cp\u003e-7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.306590257879655%\"\u003e\n \u003cp\u003eAdenosine\u0026nbsp;Monophosphate\u0026nbsp;(C10H14N5O7P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.93409742120344%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.759312320916905%\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.306590257879655%\"\u003e\n \u003cp\u003eU1-SCRTX-lg1a - C term (GNDDISDVQK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.93409742120344%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.759312320916905%\"\u003e\n \u003cp\u003e-4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.306590257879655%\"\u003e\n \u003cp\u003eDiphosphate\u0026nbsp;(O7P24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.93409742120344%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e - measure used in the molecular docking process to assess the quality of the interaction between two molecules, \u003cstrong\u003eLigand -\u0026nbsp;\u003c/strong\u003eLigand name\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e- \u003cstrong\u003eRMSD l.b. / u.b.\u0026nbsp;\u003c/strong\u003e(Root Mean Square Deviation), range of RMSD values that are considered acceptable for assessing process accuracy. The l.b. (lower bound) refers to the lower bound of the range, while the u.b. (upper bound) refers to the upper bound.\u003c/p\u003e\n\u003cp\u003eFurthermore, in crystalline by JAUCH, Ralf et al, 2005, the ribose portions of the adenosine monophosphate nucleotides that engage in hydrogen bonding interactions with the hydroxyl side chain of the THR\u003csub\u003e160\u003c/sub\u003e group did not show greater affinity compared to the C-terminal portion of the peptide derived from spider venom in Autodock vina. This connection was found in the PatchDock results and in the literature, but it was broken after the molecular dynamics simulation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe adoption of different bioinformatic tools was successful in prospecting potential receptors associated with the antimicrobial activity of U1-SCRTX-lg1a. It was used by PharmMapper to search receptors, PatchDock and AutoDock vina to mensurate interactions and UCSF chimera to molecular dynamics.\u003c/p\u003e \u003cp\u003eAt the end of this study, 6 potential receptors originating from Gram-negative organisms were found. The NH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u003c/sup\u003e synthetase presented the best result, which was associated with NAD\u003csup\u003e+\u003c/sup\u003e production, an important precursor in several cellular pathways from \u003cem\u003eEscherichia coli\u003c/em\u003e K12. Therefore, the U1-SCRTX-lg1a interaction may disrupt the normal function of this enzyme, generating intracellular alteration and growth impairment, corroborating bacterial in vitro experiments.\u003c/p\u003e \u003cp\u003eFinally, this study opens new ways to perform in vitro experiments to validate in silico results, as well as to design analogs to improve the biological activity of this peptide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData and Software Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe physicochemical properties were determined via the Heliquest server https://heliquest.ipmc.cnrs.fr/. Potential receptors were screened through PharmMapper available at http://www.lilab-ecust.cn/pharmmapper/. Sequence and files of receptors were downloaded from the website protein data bank https://www.rcsb.org/. A molecular docking method was used: PatchDock https://bioinfo3d.cs.tau.ac.il/PatchDock/ and https://vina.scripps.edu/. Ligand and receptor interactions and molecular presentation were built by the free software UCSF chimera (version 1.16) https://www.cgl.ucsf.edu/chimera/. For the prediction of ligand sites, the PDBsum server was used http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/. All files used in this study are available in https://github.com/loxoscelesgaucho/loxosceles-in-silico.git.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the team of the Protein Chemistry Laboratory at the Laboratory from Applied Toxinology (LETA - Butantan Institute, Brazil) for the constant support and encouragement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received financial support from the Research Support Foundation of the State of São Paulo (FAPESP/CeTICS), grant number 2013/07467-1, National Council for Scientific and Technological Development (CNPq) process 472744/2012-7 and from Higher Education Personnel Improvement Coordination (CAPES) process number 88887.663437/2022-00.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eBANERJEE, Soojay et al. 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Springer, Berlin, Heidelberg, 2002. p.\u0026nbsp;185\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFERRI, Maurizio et al. Antimicrobial resistance: a global emerging threat to public health systems. Critical reviews in food science and nutrition, v. 57, n. 13, p.\u0026nbsp;2857\u0026ndash;2876, 2017. Frontiers in microbiology, v. 4, p.\u0026nbsp;353, 2013\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGasteiger E., Hoogland C., Gattiker A., Duvaud S., Wilkins M.R., Appel R.D., Bairoch A.;\u003cem\u003eProtein Identification and Analysis Tools on the Expasy Server;\u003c/em\u003e (In) John M. Walker (ed): The Proteomics Protocols Handbook, Humana Press (2005). pp.\u0026nbsp;571\u0026ndash;607.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGautier,R. et al. 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Structure, v. 10, n. 1, p. 93\u0026ndash;103, 2002.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"a8189063-4b29-47a8-943f-1b562ad2a5fe","identifier":"10.13039/501100001807","name":"Fundação de Amparo à Pesquisa do Estado de São Paulo","awardNumber":"2013/07467-1","order_by":0},{"identity":"9a13c07c-1025-459d-9564-cac26a30cee0","identifier":"10.13039/501100002322","name":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","awardNumber":"88887.663437/2022-00.","order_by":1},{"identity":"ca91454c-12e0-4833-8240-506591707b74","identifier":"10.13039/501100003593","name":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","awardNumber":"472744/2012-7","order_by":2}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Instituto Butantan","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":"Computational Biology, Molecular Docking, Intracellular Targets, Spider Venom, Bioprospecting.","lastPublishedDoi":"10.21203/rs.3.rs-3043813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3043813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The emergence of antibiotic-resistant pathogens generates impairment to human health. U1-SCTRX-lg1a is a peptide isolated from a phospholipase D extracted from the spider venom of \u003cem\u003eLoxosceles gaucho\u003c/em\u003e with antimicrobial activity against Gram-negative bacteria (between 1.15 μM to 4.6 μM). The aim of this study was to suggest potential receptors associated with the antimicrobial activity of U1-SCTRX-lg1a using \u003cem\u003ein silico\u003c/em\u003e bioinformatics tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The search for potential targets of U1-SCRTX-lg1a was performed using the PharmMapper server. Molecular docking between U1-SCRTX-lg1a and the receptor was performed using PatchDock software. The prediction of ligand sites for each receptor was conducted using the PDBSum server. Chimera 1.6 software was used to perform molecular dynamics simulations only for the best dock score receptor. In addition, U1-SCRTX-lg1a and native ligand interactions were compared using AutoDock Vina software. Finally, predicted interactions were compared with the ligand site previously described in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults and discussion:\u003c/strong\u003e The bioprospecting of U1-SCRTX-lg1a resulted in the identification of forty-nine intracellular proteins originating from Gram-negative microorganisms. Among these, NH\u003csub\u003e3\u003c/sub\u003e-dependent NAD\u003csup\u003e+\u003c/sup\u003e synthetase showed the highest dock score. This result suggests that the peptide derived from brown spider venom may interact with residues SER48 and THR160. In addition, the C-terminus has greater affinity for the receptor than the N-terminus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The \u003cem\u003ein silico\u003c/em\u003e bioprospecting of receptors suggests that U1-SCRTX-lg1a may interfere with NAD\u003csup\u003e+ \u003c/sup\u003eproduction in \u003cem\u003eEscherichia coli\u003c/em\u003e, a Gram-negative bacterium, altering the homeostasis of the microorganism and impairing growth.\u003c/p\u003e","manuscriptTitle":"In silico prospection of receptors associated with the biological activity of U1-SCTRX-lg1a: an antimicrobial peptide isolated from the venom of Loxosceles gaucho","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-12 16:50:35","doi":"10.21203/rs.3.rs-3043813/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":"d3c6b3ef-c569-4096-9fef-cb53be4bba2b","owner":[],"postedDate":"June 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":22269502,"name":"Animal Science"},{"id":22269503,"name":"General Microbiology"},{"id":22269504,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2023-07-08T05:29:17+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-12 16:50:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3043813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3043813","identity":"rs-3043813","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}