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Targeting the class A Beta lactamases Omega loop is an ideal way to combat drug resistance because of its significant role in the catalytic activity and deacylation process inhibition. Therefore, the molecular docking approach with computerized peptide-based in silico screening has been applied for the identification of inhibitors of TEM-type bLs. Among the subjected 105 peptides, Chrombacin (-47.8 KJ/mol), Gassericin A (-35.7 KJ/mol), Duramycin (-34.1 KJ/mol), Brevinin-1DYa (-34.0 KJ/mol), Amoebapore A (-31.2 KJ/mol), Mundticin ATO6 (-29.0 KJ/mol), Lactocyclicin Q (-26.3 KJ/mol), Cinnamycin (-25.9 KJ/mol showed highest binding energy. Among the peptides that showed the highest docking score Elafin, Cinnamycin, Duramycin interacted with Lys 73 of the a domain of catalytic residues of TEM-1 Beta lactamases, whereas Taromycin A, Gassericin A interacted with Lys 234 of the b domain, depicting a strong inhibition and also exhibited desirable physicochemical properties. Hence further in vitro examination of these cyclic peptides against the resistant strains is warranted to help design further novel inhibitors based on their scaffolds and also for the development of an effective drug combination regime. [ABSTRACT FROM AUTHOR]

Targeting the class A Beta lactamases Omega loop is an ideal way to combat drug resistance because of its significant role in the catalytic activity and deacylation process inhibition. Therefore, the molecular docking approach with computerized peptide-based in silico screening has been applied for the identification of inhibitors of TEM-type βLs. Among the subjected 105 peptides, Chrombacin (-47.8 KJ/mol), Gassericin A (-35.7 KJ/mol), Duramycin (-34.1 KJ/mol), Brevinin-1DYa (-34.0 KJ/mol), Amoebapore A (-31.2 KJ/mol), Mundticin ATO6 (-29.0 KJ/mol), Lactocyclicin Q (-26.3 KJ/mol), Cinnamycin (-25.9 KJ/mol showed highest binding energy. Among the peptides that showed the highest docking score Elafin, Cinnamycin, Duramycin interacted with Lys 73 of the α domain of catalytic residues of TEM-1 Beta lactamases, whereas Taromycin A, Gassericin A interacted with Lys 234 of the β domain, depicting a strong inhibition and also exhibited desirable physicochemical properties. Hence further in vitro examination of these cyclic peptides against the resistant strains is warranted to help design further novel inhibitors based on their scaffolds and also for the development of an effective drug combination regime.

The antimicrobial peptides (AMPs) have been regarded as the next generation antibiotics. This study aimed to explore the AMP inhibitors of β-lactamase enzyme employing computational biology methods. Protein-peptide docking of Acinetobacter baumannii OXA-24 class D β-lactamase with AMPs (melittin and RP-1) were performed using HawkDock webserver. The docked complexes were subjected to energy property analysis through MM/GBSA, and binding affinity (ΔG (kcal/mol)) and stability (dissociation constant, KD (M)) prediction using PRODIGY. Both the AMPs, melittin and RP-1, were well docked with A. baumannii OXA-24. The top ranked OXA-24-melittin and OXA-24-RP-1 complexes were detected on the basis of the HawkDock scores (-2974.08 and -2825.83, respectively), thereafter by rescoring with MM/GBSA-based binding free energy (BFE) of -33.85 and -29.29 kcal/mol, respectively. The PRODIGY-based respective BFE (-8.0 and -10.0 kcal/mol) and KD (1.4 × 10-6 and 5 × 10-8 M) of the complexes revealed excellent protein-peptide binding affinity and complex stability. The iMODS-based molecular dynamic simulation authenticated the stability and molecular motion flexibility of the protein-peptide complexes. The quantum mechanics and molecular mechanics energy estimated using density-functional theory with CP2K software, for the electrostatic interaction of OXA-24-RP-1 (-218862.95 kcal/mol), was more favourable than the H-bonded interaction of OXA-24-melittin (-200620.21 kcal/mol), nevertheless both the peptides were found effective to inhibit OXA-24. Both the AMPs had toxicity profiles within the acceptable limits as predicted through pkCSM. Current findings indicate the potential supplement or replacement of conventionally used antibiotics with melittin and RP-1 to treating or averting A. baumannii infection by escaping the β-lactamases-mediated resistance.

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein. [ABSTRACT FROM AUTHOR]

Designing peptides for protein–protein interaction inhibition is of significant interest in computer‐aided drug design. Such inhibitory peptides could mimic and compete with the binding of the partner protein to the inhibition target. Experimental peptide design is a laborious, time consuming, and expensive multi‐step process. Therefore, in silico peptide design can be beneficial for achieving this task. We present a novel algorithm, Pep–Whisperer, which aims to design inhibitory peptides for protein–protein interaction. The desirable peptides would have a relatively high predicted binding affinity to the target protein in a given protein–protein complex. The algorithm outputs linear peptides which are based on an initial template. The template could either be a peptide which is retrieved from the interaction site, or a patch of nonconsecutive amino acids from the protein–protein interface which is completed to a linear peptide by short polyalanine linkers. In addition, the algorithm takes into consideration the conservation of the amino acids in the ligand‐protein binding site by using evolutionary information for choosing the preferred amino acids in each position of the designed peptide. Our algorithm was able to design peptides with high predicted binding affinity to the target protein. The method is fully automated and available as a web server at http://bioinfo3d.cs.tau.ac.il/PepWhisperer/. [ABSTRACT FROM AUTHOR]

Protein-protein interactions with high specificity and low affinity are functionally important but are not comprehensively understood because they are difficult to identify. Particularly intriguing are the dynamic and specific interactions between folded protein domains and short unstructured peptides known as short linear motifs. Such domain-motif interactions (DMIs) are often difficult to identify and study because affinities are modest to weak. Here we describe "electrophoretic crosslinking shift assay" (ECSA), a simple in vitro approach that detects transient, low affinity interactions by covalently crosslinking a prey protein and a fluorescently labeled bait. We demonstrate this technique on the well characterized DMI between MAP kinases and unstructured D-motif peptide ligands. We show that ECSA detects sequence-specific micromolar interactions using less than a microgram of input prey protein per reaction, making it ideal for verifying candidate low-affinity DMIs of components that purify with low yield. We propose ECSA as an intermediate step in SLiM characterization that bridges the gap between high throughput techniques such as phage display and more resource-intensive biophysical and structural analysis., Competing Interests: Conflict of interest The authors declare no conflict of interest with the contents of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Protein–protein interactions (PPIs) play crucial roles in many cellular processes and their deregulation often leads to cellular dysfunctions. One promising way to modulate PPIs is to use peptide derivatives that bind their protein target with high affinity and high specificity. Peptide modulators are often designed using secondary structure mimics. However, fragment-based design is an alternative emergent approach in the PPI field. Most of the reported computational fragment-based libraries targeting PPIs are composed of small molecules or already approved drugs, but, according to our knowledge, no amino acid based library has been reported yet. In this context, we developed a novel fragment-based approach called Des3PI (design of peptides targeting protein–protein interactions) with a library composed of natural amino acids. All the amino acids are docked into the target surface using Autodock Vina. The resulting binding modes are geometrically clustered, and, in each cluster, the most recurrent amino acids are identified and form the hotspots that will compose the designed peptide. This approach was applied on Ras and Mcl-1 proteins, as well as on A β protofibril. For each target, at least five peptides generated by Des3PI were tested in silico: the peptides were first blindly docked on their target, and then, the stability of the successfully docked complexes was verified using 200 ns MD simulations. Des3PI shows very encouraging results by yielding at least 3 peptides for each protein target that succeeded in passing the two-step assessment. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a major neurodegenerative illness whose prevalence is increasing worldwide but the molecular mechanism remains unclear. There is some scientific evidence that the molecular complexity of Alzheimer's pathophysiology is associated with the formation of extracellular amyloid-beta plaques in the brain. A novel cross- phenotype association analysis of imaging genetics reported a brain atrophy susceptibility gene, namely FAM222A and the protein Aggregatin encoded by FAM222A interacts with amyloid-beta (Aβ)-peptide (1-42) through its N-terminal Aβ binding domain and facilitates Aβ aggregation. The function of Aggregatin protein is unknown, and its three-dimensional structure has not been analyzed experimentally yet. Our goal was to investigate the interaction of Aggregatin with Aβ in detail by in silico analysis, including the 3D structure prediction analysis of Aggregatin protein by homology modeling. Our analysis verified the interaction of the C-terminal domain of model protein with the N-terminal domain of Aβ. This is the first attempt to demonstrate the interaction of Aggregatin with the Aβ. These results confirmed in vitro and in vivo study reports claiming FAM222A helping to ease the aggregating of the Aβ-peptide.

Most recently, monkeypox virus (MPXV) has emanated as a global public health threat. Unavailability of effective medicament against MPXV escalates demand for new therapeutic agent. In this study, in silico strategies were conducted to identify novel drug against the A36R protein of MPXV. The A36R protein of MPXV is responsible for the viral migration, adhesion, and vesicle trafficking to the host cell. To block the A36R protein, 4893 potential antiviral peptides (AVPs) were retrieved from DRAMP and SATPdb databases. Finally, 57 sequences were screened based on peptide filtering criteria, which were then modeled. Likewise, 31 monkeypox virus A36R protein sequences were collected from NCBI protein database to find consensus sequence and to predict 3D protein model. The refined and validated models of the A36R protein and AVP peptides were used to predict receptor-ligand interactions using DINC 2 server. Three peptides that showed best interactions were SATPdb10193, SATPdb21850, and SATPdb26811 with binding energies -6.10, -6.10, and -6.30 kcal/mol, respectively. Small molecules from drug databases were also used to perform virtual screening against the A36R protein. Among databases, Enamine-HTSC showed strong affinity with docking scores ranging from -8.8 to 9.8 kcal/mol. Interaction of target protein A36R with the top 3 peptides and the most probable drug (Z55287118) examined by molecular dynamic (MD) simulation. Trajectory analyses (RMSD, RMSF, SASA, and Rg) confirmed the stable nature of protein-ligand and protein-peptide complexes. This work suggests that identified top AVPs and small molecules might interfere with the function of the A36R protein of MPXV. [ABSTRACT FROM AUTHOR]

Matrix metalloproteinases-1 is recognized as a potential target of tumors and cancer for their role in breaking down collagen and extracellular matrix proteins. Our study screened 51 peptides against MMP-1 to find potent inhibitors for preventing breast cancer metastasis. Since cyclic peptides have higher selectivity and stability than linear peptides, we have focused our study on peptides with a cyclic structure as an MMP-1 inhibitor. We used an in-silico approach to design cyclic peptides and illustrated their activity as specific non-zinc binding inhibitors of MMP-1. Peptide cyclization, induced-fit docking (IFD), molecular dynamics simulation, and binding free energy (MM/GBSA) calculation were applied to evaluate the activity of peptides to inhibit MMP-1. Among the 51 peptides, we selected four peptides based on their docking score, binding affinity, and interacting residues. Complex 35, 36, 37, and 51 achieved highest docking scores of −215.37, −215.71, −195.56, and −208.08 kcal/mol, respectively. From the molecular dynamic simulation, we found three complexes (35, 36, and 37) with a stable structural conformation in terms of APO protein for 500 ns? Complexes 35, 36, 37 and 51 exhibited an average RMSD value of 2.45 ± 0.19, 3.39 ± 0.39, 2.03 ± 0.47, 2.60 ± 0.30 Å, respectively. The MM/GBSA free energy analysis of protein-peptide complex, complex 35, 36, and 37 show the greater MM/GBSA result consisting of the average value of −54.61 ± 0.45, −66.57 ± 0.38, and −43.57 ± 0.45 kJ/mol. These findings can facilitate the rational design and modifications of selective peptide inhibitors targeting the catalytic domain of MMP-1.

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein.

Abstract Background Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. Results Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. Conclusions Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.

Abstract Background Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. Result Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. Conclusion The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/).

Protein–protein interactions of Interleukin-17 (IL17) play vital role in the autoimmune and inflammatory diseases, such as rheumatoid arthritis, multiple sclerosis, and psoriasis. Potent therapeutics for these diseases could be developed by blocking or modulating these interactions through biologics, peptide inhibitors and small molecule inhibitors. Unlike biologics, peptide inhibitors are cost effective and can be orally available. Peptide inhibitors do not require a binding groove as that of small molecules either. Therefore, crystal structure of IL17A in complex with a high affinity peptide inhibitor (HAP) (1-IHVTIPADLWDWIN-14) is investigated with an aim to find hot spots that could improve its potency. An in silico mutagenesis strategy was implemented using FoldX PSSM to scan for positions tolerant to amino acid substitution. Three positions T4, A7, and N14 showed improved stability when mutated with 'F/M/Y', 'P' and 'F/M/Y', respectively. A set of 31 mutant peptides are designed through combinations of these tolerant mutations using Build Model application of FoldX. Binding affinity and interactions of 31 peptides are assessed through protein–peptide docking and binding free energy calculations. Two peptides namely, P1 ("1-IHVTIPPDLWDWIY-14") and P2 ("1-IHVMIPPDLWDWIF-14") showed better binding affinity to IL17A dimerization site compared to HAP. Interactions of P1, P2 and HAP are also analyzed through 100 ns molecular dynamics simulations using GROMACS v5.0. The results revealed that the P2 peptide likely to offer better potency compared to HAP and P1. Therefore, the P2 peptide can be synthesized to develop oral therapies for autoimmune and inflammatory diseases with further experimental evaluations. [ABSTRACT FROM AUTHOR]

Hypertension is declared as the major risk factor of cardiovascular diseases and stroke, and the leading cause of premature deaths. ACE is a zinc dependent dipeptidyl peptidase and plays key role in controlling blood pressure via renin angiotensin system (RAS), and hence serves as the promising target for antihypertension drugs. Many food derived antihypertensive peptides have been identified recently. However, their ACE inhibitory activity, interactions and stability are not fully evaluated. Our work focused on combination of modern bioinformatics techniques for efficient evaluation of potent ACE inhibitory food peptides and understanding of interactions between ACE and inhibitory peptides. We reported novel antihypertensive peptide IQDVPS, LQPGS, VIP from date, salmon and soybean proteins respectively. Food proteins were digested in-silico to release peptides. Molecular docking studies revealed high binding affinities and interactions with ACE active site. MD simulations and Alanine Scanning were carried out to study the stability of these ACE-peptide complexes in cell like environment. The results showed that the suggested peptides competitively inhibit ACE by tightly binding to its active site, meanwhile maintaining the structural stability of the complex. ACE-LQPGS (Salmon) was found to have best binding with least structural fluctuations. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a major neurodegenerative illness whose prevalence is increasing worldwide but the molecular mechanism remains unclear. There is some scientific evidence that the molecular complexity of Alzheimer's pathophysiology is associated with the formation of extracellular amyloid-beta plaques in the brain. A novel cross-phenotype association analysis of imaging genetics reported a brain atrophy susceptibility gene, namely FAM222A and the protein Aggregatin encoded by FAM222A interacts with amyloid-beta (Aß)-peptide (1-42) through its N-terminal Aß binding domain and facilitates Aß aggregation. The function of Aggregatin protein is unknown, and its three-dimensional structure has not been analyzed experimentally yet. Our goal was to investigate the interaction of Aggregatin with Aß in detail by in silico analysis, including the 3D structure prediction analysis of Aggregatin protein by homology modeling. Our analysis verified the interaction of the C-terminal domain of model protein with the N-terminal domain of Aß. This is the first attempt to demonstrate the interaction of Aggregatin with the Aß. These results confirmed in vitro and in vivo study reports claiming FAM222A helping to ease the aggregating of the Aß-peptide. [ABSTRACT FROM AUTHOR]

Many antimicrobial peptides (AMPs) have multiple antimicrobial immunity effects. One such class of peptides is temporins. Temporins are the smallest (AMPs) found in nature and are highly active against gram-positive bacteria. Nowadays, there was a rapid increase in the availability of the 3D structure of proteins in PDB (protein data bank). The conserved residues and 3D structural conformations of temporins (AMPs) were still unknown. The present study explores the sequence analysis, alpha-helical structural conformations of temporins. The sequence of temporins was deracinated from APD3 database, the three-dimensional structure was constructed by homology modeling studies. The sequence analysis results show that the conserved residues among the peptide sequences, the maximum of the sequences are 70% alike to each other. The secondary structure prediction results revealed that 99% of temporin (AMPs) exhibited in alpha-helical form. The 3D structure speculated using RAMPAGE exposes the alpha-helical conformation in all temporins (AMPs). The phylogenetic analysis reveals the evolutionary relationships of temporins (AMPs), which are branched into seven clusters. As a result, we identified a list of potential temporin AMPs which docked to the antiviral protein (MERS-CoV), it shows good protein-peptide binding. This computational approach may serve as a good model for the rationale design of temporin based antibiotics. [ABSTRACT FROM AUTHOR]

We participated in CARPI rounds 38‐45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein‐protein, protein‐peptide, and protein‐oligosaccharide interactions. Both template‐based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo‐oligomer protein, GalaxyPepDock for protein‐peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock‐ab‐initio for protein‐peptide complex, GalaxyDock2 and Galaxy7TM for protein‐oligosaccharide complex) have been tested. Template‐based methods depend heavily on the availability of proper templates and template‐target similarity, and template‐target difference is responsible for inaccuracy of template‐based models. Inaccurate template‐based models could be improved by our structure refinement and loop modeling methods based on physics‐based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics‐based methods for such problems is still to come. [ABSTRACT FROM AUTHOR]

Computational structural prediction of macromolecular interactions is a fundamental tool toward the global understanding of cellular processes. The Critical Assessment of PRediction of Interactions (CAPRI) community‐wide experiment provides excellent opportunities for blind testing computational docking methods and includes original targets, thus widening the range of docking applications. Our participation in CAPRI rounds 38 to 45 enabled us to expand the way we include evolutionary information in structural predictions beyond our standard free docking InterEvDock pipeline. InterEvDock integrates a coarse‐grained potential that accounts for interface coevolution based on joint multiple sequence alignments of two protein partners (co‐alignments). However, even though such co‐alignments could be built for none of the CAPRI targets in rounds 38 to 45, including host‐pathogen and protein‐oligosaccharide complexes and a redesigned interface, we identified multiple strategies that can be used to incorporate evolutionary constraints, which helped us to identify the most likely macromolecular binding modes. These strategies include template‐based modeling where only local adjustments should be applied when query‐template sequence identity is above 30% and larger perturbations are needed below this threshold; covariation‐based structure prediction for individual protein partners; and the identification of evolutionarily conserved and structurally recurrent anchoring interface motifs. Overall, we submitted correct predictions among the top 5 models for 12 out of 19 interface challenges, including four High‐ and five Medium‐quality predictions. Our top 20 models included correct predictions for three out of the five targets we missed in the top 5, including two targets for which misleading biological data led us to downgrade correct free docking models. [ABSTRACT FROM AUTHOR]

The cytoplasmic dynein light chain 1 (DYNLL1) is an important constituent of motor proteins complex. In human it is encoded by DYNLL1 gene. It is involved in cargo transport functions and interacts with many viral proteins with the help of short linear consensus motif sequence (K/R) XTQT. Viral proteins bind to DYNLL1 through its consensus short linear motif (SLiM) sequence to reach the target site in the cell and cause different infections in the host. It is still unknown if bacterial proteins also contain the same conserved SLiMs sequence through which they bind to this motor protein and cause infections. So, it is important to investigate the role of DYNLL1 in human bacterial infections. The interaction partner proteins of DYNLL1 against conserved viral motif sequences were predicted through PDBSum. Pairwise sequence alignment, between viral motif sequence and that of predicted proteins, was performed to identify conserved region in predicted interaction partners. Docking between the DYNLL1 and new pathogenic interaction partners was performed, by using PatchDock, to explore the protein-protein binding quality. Interactions of docked complexes were visualized by DimPlot. Three pathogenic bacterial proteins i.e., enterochelin esterase (3MGA), protective antigen (3J9C) and putative lipoprotein (4KT3) were selected as candidate interaction partners of DYNLL1. The putative lipoprotein (4KT3) showed low quality binding with DYNLL1. So, enterochelin esterase (3MGA) and protective antigen (3J9C) were speculated to be involved in human bacterial infections by using DYNLL1 to reach their target sites. [ABSTRACT FROM AUTHOR]

We present a nonredundant benchmark, coined PepPro, for testing peptide–protein docking algorithms. Currently, PepPro contains 89 nonredundant experimentally determined peptide–protein complex structures, with peptide sequence lengths ranging from 5 to 30 amino acids. The benchmark covers peptides with distinct secondary structures, including helix, partial helix, a mixture of helix and β‐sheet, β‐sheet formed through binding, β‐sheet formed through self‐folding, and coil. In addition, unbound proteins' structures are provided for 58 complexes and can be used for testing the ability of a docking algorithm handling the conformational changes of proteins during the binding process. PepPro should benefit the docking community for the development and improvement of peptide docking algorithms. The benchmark is available at http://zoulab.dalton.missouri.edu/PepPro%5fbenchmark. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds promise for drug development. However, experimental confirmation of the peculiarities of PPIs is challenging due to their dynamic and transient nature. As a complement to experimental technologies, multiple computational molecular docking (MD) methods have been developed to predict the structures of protein-protein complexes and their dynamics, still requiring further improvements in several issues. Here, we report an improved MD method, namely three-software docking (3SD), by employing three popular protein-peptide docking software (CABS-dock, HPEPDOCK, and HADDOCK) in combination to ensure constant quality for most targets. We validated our 3SD performance in known protein-peptide interactions (PpIs). We also enhanced MD performance in proteins having intrinsically disordered regions (IDRs) by applying the modified 3SD strategy, the three-software docking after removing random coiled IDR (3SD-RR), to the comparable crystal PpI structures. At the end, we applied 3SD-RR to the AlphaFold2-predicted receptors, yielding an efficient prediction of PpI pose with high relevance to the experimental data regardless of the presence of IDRs or the availability of receptor structures. Our study provides an improved solution to the challenges in studying PPIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. Protein-protein interactions (PPIs) are integral to life, and abnormal PPIs are associated with diseases such as cancer. Studying protein-peptide interactions (PpIs) is challenging due to their dynamic and transient nature. Here we developed improved docking methods (3SD and 3SD-RR) to predict the PpI poses, ensuring constant quality in most targets and also addressing issues like intrinsically disordered regions (IDRs) and artificial intelligence-predicted structures. Our study provides an improved solution to the challenges in studying PpIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. [ABSTRACT FROM AUTHOR]

Abstract Background Many protein–protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression). Methods In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at http://biocomp.chem.uw.edu.pl/CABSdock/ ). Based on the docking result, the protein–protein complex is reconstructed and refined. Results Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model. Conclusions The results show that the CABS-dock method may be useful as the primary docking tool in specific protein–protein docking cases similar to EphB4-EphrinB2 complex—that is, where a short linear motif fragment can be identified.

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results., Competing Interests: Declaration of competing interest No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

Background: Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. Results: Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. Conclusions: Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking. [ABSTRACT FROM AUTHOR]

Thrombosis represents a major cause of morbidity and mortality around the world. Peptides isolated from natural sources have been proven to have anticoagulant and antithrombotic properties. VITPOR AI, a 16-mer peptide, isolated from Porphyra yezoensis was reported to have anticoagulant property. In this study, the coagulation factor XIIa activity in the presence of VITPOR AI was determined. Molecular modelling was performed to investigate the interaction between peptide and FXIIa. The structure of the peptide was predicted using PEP-FOLD3 server and simulated by molecular dynamics (MD) using GROMACS package. Molecular docking was carried out using peptide-protein docking software, pepATTRACT and its stability was confirmed by MD simulations. The chromogenic substrate assay revealed that the peptide inhibited the amidolytic activity of FXIIa with IC 50 of 70.24 μM. The docking result showed peptide interactions through hydrogen bonds with Pro 96, Tyr 99, Glu 146, Gly 193 and Ser 195 of FXIIa. The MD simulation demonstrated that the peptide's binding with the FXIIa was stable as it did not move away from its binding region throughout the simulation period of 100 ns Moreover, MM/PBSA analysis also indicated a stable binding between the protein and peptide. These results suggest that the inhibition of the FXIIa activity might be due to binding of the peptide to oxyanion hole of the catalytic site. Thus, VITPOR AI could be explored as a potent anticoagulant which inhibits only intrinsic pathway of coagulation cascade but does not affect the extrinsic pathway. Image 1 • Anticoagulant peptide VITPOR AI inhibited the coagulation factor XIIa in a dose-dependent manner. • Docking results predicted that the peptide formed hydrogen bonds with Pro 96, Tyr 99, Glu 146, Gly 193 and Ser 195 of FXIIa. • Molecular dynamics (MD) simulations revealed the stability of the FXIIa-peptide docked complex. [ABSTRACT FROM AUTHOR]

Abstract Inhibins are heterodimeric glycoproteins that belong to transforming growth factor-β superfamily. The present study was aimed at structural elucidation of immunogenic inhibin chimeric peptide (ICP) using immunoinformatics tools, to develop anti-ICP antibodies and to understand the mechanism of interaction between HPG-axis and anti-ICP antibodies in adult female Clarias batrachus. Homology modelling predicted that the ICP adopts a turn and random coil, which was further confirmed by circular dichroism (CD). Competitive binding ELISA with ICP and different unrelated peptides confirmed that the developed anti-ICP antiserum is highly specific and critical for the direct interaction to the inhibin-α subunit. The anti-ICP antibodies increased the mRNA transcript levels of follicle stimulating hormone receptor (FSHr), luteinizing hormone receptor (LHr), activin receptor type I (Act RI) and activin receptor type II (Act RII). Further, the serum concentrations of follicle stimulating hormone (FSH), 17β-estradiol (E2) and progesterone increased significantly in anti-ICP antisera treated groups compared to control. Modelled inhibin-α and ICP exhibits better interaction with each other through two salt bridge formations. Also, ICP showed better interaction with betaglycan through positive and polar-based interactions. The results of the present study suggest that immunoneutralization of inhibin bioactivity, confirms the physiological role of inhibin as a component of ovarian feedback mechanism regulating FSH secretion through augmentation of gonadotropin and activin receptor signalling pathways that could affect the reproductive success in fishes. Highlights • Novel species-specific inhibin chimeric peptide was designed for C. batrachus. • Structural characterization of ICP was determined using bioinformatics approach. • Anti-inhibin antibody significantly up regulated the receptor expression and hormonal levels. [ABSTRACT FROM AUTHOR]

Background: Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. Result: Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. Conclusion: The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/). [ABSTRACT FROM AUTHOR]

The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.

Interaction of amyloid peptides (Aβ) with receptor for advanced glycation end products (RAGE) elicits an inflammatory response and augments Alzheimer's disease (AD) pathology. The present study was aimed to analyse the interactions of different forms of Aβ42 peptide with ligand binding domain of normal and G82S RAGE and their possible consequences in AD pathology. The structures of RAGE ectodomain (3CJJ), monomeric forms of Aβ42 - 1IYT (apolar) and 1Z0Q (polar) and fibrillar (2BEG) were obtained from PDB. The structure of G82 and S82 RAGE was generated using SWISS MODEL. SIFT and PolyPhen analysis was performed to predict the phenotypic and functional effect of the amino acid substitution. The G82 and S82 variant structures were simulated in GROMACS and the 10 lowest energy structures were docked with different forms of Aβ42 using CLUSPRO in antibody mode. The lowest energy docked structure was further simulated for 5 ns. The structures corresponding to 0-5 ns were taken and the amino acid interactions were generated using PDBSUM. SIFT analysis indicated that G82S SNP had a tolerating effect on the structure of protein but polyphen predicted a probable damaging effect. Highest binding score was obtained with 2BEG docked with both G82 RAGE (-375.84 ± 7.425 Kcal/mol) and G82S variant (-391.09 ± 13.391 Kcal/mol) indicating that the fibrillar form showed better interaction. Compared to G82 RAGE, the S82 variant showed better interaction to all three forms of Aβ42. The results of study indicate that RAGE interacted better with fibrillar form of Aβ42 peptide and G82S mutation enhanced the binding affinity of RAGE towards amyloid peptides leading to enhanced inflammatory response.

Peptides are biologically active, small molecules with high specificity in its mode of action that can be effective at nanomolar concentrations. Peptide-based antiviral medicines have already been licensed and used to treat human immunodeficiency virus (HIV), influenza virus and hepatitis C virus. So far, no peptide drug has been approved for antiviral treatment against Dengue virus, and many are under clinical trials. Therefore, developing a reasonable peptide against the Dengue virus Envelope protein structure will be a successful strategy for treating Dengue. Hence, we investigated protein-protein docking interactions between 215 peptides retrieved from the AVP database against the envelope protein using Cluspro and HADDOCK followed by the evaluation of their allegenicity, toxicity and physicochemical characteristics investigation. Further validation of the protein-peptide complexes was performed with Molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann surface area (MMPBSA) analysis on the hit inhibitors. This study revealed that Indolicidin (-75.026 ± 1.54 KJ/mol) and Human Neutrophil peptide-1 (-71.6551 ± 2.112 KJ/mol) shows higher negative ΔG binding implicating their relative stabilization in the protein-peptide interactions during 100 ns of dynamic simulations. Also, both the peptides exhibited desirable physicochemical properties and were nonallergenic. Hence, we further aim to test these peptides by in vitro and in vivo studies to confirm their efficacy against Dengue virus.Communicated by Ramaswamy H. Sarma.

Background: Many protein-protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression).Methods: In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at http://biocomp.chem.uw.edu.pl/CABSdock/ ). Based on the docking result, the protein-protein complex is reconstructed and refined.Results: Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model.Conclusions: The results show that the CABS-dock method may be useful as the primary docking tool in specific protein-protein docking cases similar to EphB4-EphrinB2 complex-that is, where a short linear motif fragment can be identified. [ABSTRACT FROM AUTHOR]

ABSTRACT Computational protein-protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real-life cases and help accelerate methodology development. For CAPRI Rounds 28-35, we used an automatic docking pipeline integrating the coarse-grained co-evolution-based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co-evolved interfaces. Together with Zdock/Frodock for rigid-body docking, SOAP-PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein-peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1st by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co-evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378-390. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290⁻1310 of GluN2B (GluN2B-CT1290-1310) remain elusive and the interaction between GluN2B-CT1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT1290-1310 was predicted by computational simulation methods including protein⁻peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT1290⁻1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein⁻peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT1290-1310 and DAPK1 interface.

ABSTRACT Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Protein–peptide interactions play essential functional roles in living organisms and their structural characterization is a hot subject of current experimental and theoretical research. Computational modeling of the structure of protein–peptide interactions is usually divided into two stages: prediction of the binding site at a protein receptor surface, and then docking (and modeling) the peptide structure into the known binding site. This paper presents a comprehensive CABS-dock method for the simultaneous search of binding sites and flexible protein–peptide docking, available as a user’s friendly web server. We present example CABS-dock results obtained in the default CABS-dock mode and using its advanced options that enable the user to increase the range of flexibility for chosen receptor fragments or to exclude user-selected binding modes from docking search. Furthermore, we demonstrate a strategy to improve CABS-dock performance by assessing the quality of models with classical molecular dynamics. Finally, we discuss the promising extensions and applications of the CABS-dock method and provide a tutorial appendix for the convenient analysis and visualization of CABS-dock results. The CABS-dock web server is freely available at http://biocomp.chem.uw.edu.pl/CABSdock/ . [ABSTRACT FROM AUTHOR]

There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner. [ABSTRACT FROM AUTHOR]

Interaction of amyloid peptides (Aβ) with receptor for advanced glycation end products (RAGE) elicits an inflammatory response and augments Alzheimer's disease (AD) pathology. The present study was aimed to analyse the interactions of different forms of Aβ42 peptide with ligand binding domain of normal and G82S RAGE and their possible consequences in AD pathology. The structures of RAGE ectodomain (3CJJ), monomeric forms of Aβ42 – 1IYT (apolar) and 1Z0Q (polar) and fibrillar (2BEG) were obtained from PDB. The structure of G82 and S82 RAGE was generated using SWISS MODEL. SIFT and PolyPhen analysis was performed to predict the phenotypic and functional effect of the amino acid substitution. The G82 and S82 variant structures were simulated in GROMACS and the 10 lowest energy structures were docked with different forms of Aβ42 using CLUSPRO in antibody mode. The lowest energy docked structure was further simulated for 5 ns. The structures corresponding to 0-5 ns were taken and the amino acid interactions were generated using PDBSUM. SIFT analysis indicated that G82S SNP had a tolerating effect on the structure of protein but polyphen predicted a probable damaging effect. Highest binding score was obtained with 2BEG docked with both G82 RAGE (–375.84 ± 7.425 Kcal/mol) and G82S variant (–391.09 ± 13.391 Kcal/mol) indicating that the fibrillar form showed better interaction. Compared to G82 RAGE, the S82 variant showed better interaction to all three forms of Aβ42. The results of study indicate that RAGE interacted better with fibrillar form of Aβ42 peptide and G82S mutation enhanced the binding affinity of RAGE towards amyloid peptides leading to enhanced inflammatory response. [ABSTRACT FROM AUTHOR]

Molecular docking programs play a crucial role in drug design and development. In recent years, much attention has been devoted to the protein-peptide docking problem in which docking of a flexible peptide with a given protein is sought. In this work, we present a docking algorithm which is based on the use of a filling function method for continuous global optimization. In particular, the protein-peptide docking position is found by minimizing the conformational potential free energy function based on a new approximate mathematical model. The resulting global optimization problem presents some difficulties, since it is a large-scale one and the objective function is non-convex, so that it has many local minima. To solve the problem, we adopt a global optimization method based on the use of a filling function to escape from local solutions. Moreover, in order to obtain more accurate results, we search the correct docking position by performing a two-phase optimization process. In particular, in a first step, only the carbon $$\mathrm{C}_\alpha $$ atoms of the protein and peptide are considered, thus obtaining an approximate docking solution. Then, the energy function is completed by considering all the peptide and protein atoms so that, starting from the solution of the first phase, the new minimization process gives a more accurate result. We present numerical results on a set of benchmark docking pairs and their comparison with those obtained by the known software package PacthDock for molecular docking. [ABSTRACT FROM AUTHOR]