PatchMAN docking: Modeling peptide-protein interactions in the context of the receptor surface

Peptide docking can be perceived as a subproblem of protein-protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation between the two partners, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about the peptide sequence to generate a representative conformer ensemble, which can then be rigid body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely that the protein surface defines the peptide bound conformation.We present PatchMAN (Patch-Motif AligNments), a novel peptide docking approach which uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a non-redundant set of protein-peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide-protein complexes in 62% / 81% within 2.5Å / 5Å RMSD, with corresponding sampling in 81% / 100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the conformation of the bound peptide is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide-protein association, and to the design of new peptide binders.

Computational Study of Scorpion Venom (Lychas Mucronatus) Activity as Antimicrobial Peptides (AMPs) to the SARS-CoV-2 Main Protease for the Future Coronavirus Disease (COVID-19) Inhibitors

The 2019 coronavirus pandemic disease (COVID-19) is still declared a global pandemic by the World Health Organization (WHO). Therefore, an effort that is considered effective in finding therapeutic agents is needed to prevent the spread of COVID-19 infection. One of the steps that can be chosen is by utilizing antimicrobial peptides (AMPs) from animal venom by targeting the specific receptor of SARS-CoV-2, namely the main protease (Mpro). Through this research, a computational approach will be conducted to predict antiviral activity, including protein-peptide docking using PatchDock algorithm, to identify, evaluate, and explore the affinity and molecular interactions of four types of antimicrobial peptides (AMPs), such as Mucroporin, Mucroporin-M1, Mucroporin-S1, and Mucroporin-S2 derived from scorpion venom (Lychas mucronatus) against main protease (Mpro) SARS-CoV-2. These results were then confirmed using protein-peptide interaction dynamics simulations for 50 ns using Gromacs 2016 to observe the molecular stability to the binding site of SARS-CoV-2 Mpro. Based on protein-peptide docking simulations, it was proven that the Mucroporin S-1 peptides have a good affinity against the active site area of SARS-CoV-2 Mpro, with an ACE score of −779.56 kJ/mol. Interestingly, Mucroporin-S1 was able to maintain the stability of its interactions based on the results of RMSD, RMSF, and MM/PBSA binding free energy calculations. The results of the computational approach predict that the Mucroporin-S1 peptide is expected to be useful for further research in the development of new antiviral-based AMPs for the COVID-19 infectious disease.

YAPP-CD: Yet another protein-peptide complex database

Protein-peptide interactions are of great interest to the research community not only because they serve as mediators in many protein-protein interactions but also because of the increasing demand for peptide-based pharmaceutical products. Protein-peptide docking is a major tool for studying protein-peptide interactions, and several docking methods are currently available. Among various protein-peptide docking algorithms, template-based approaches, which utilize known protein-peptide complexes or templates to predict a new one, have been shown to yield more reliable results than template-free methods in recent comparative research. To obtain reliable results with a template-based docking method, the template database must be comprehensive enough; that is, there must be similar templates of protein-peptide complexes to the protein and peptide being investigated. Thus, the template database must be updated to leverage recent advances in structural biology. However, the template database distributed with GalaxyPepDock, one of the most widely used peptide docking programs, is outdated, limiting the prediction quality of the method. Here, we present an up-to-date protein-peptide complex database called YAPP-CD, which can be directly plugged into the GalaxyPepDock binary package to improve GalaxyPepDock's prediction quality by drawing on recent discoveries in structural biology. Experimental results show that YAPP-CD significantly improves GalaxyPepDock's prediction quality, e.g., the average Ligand/Interface RMSD of a benchmark set is reduced from 7.60 A/3.62 A to 3.47 A/1.71 A.

In silico studies on milk derived peptides as potential inhibitors against SARS CoV-2 spike protein receptor binding domain

COVID-19 (Corona Virus Infected Diseases-19) is caused by a strain of coronavirus called SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2). There’s no permanent diagnosis available till date to combat the disease. The viral infection in humans is initiated by binding of RBD (receptor binding domain) of spike protein to human angiotensin-converting enzyme 2 (hACE2) receptor protein. In this computational study, milk-derived peptides are screened against Receptor Binding Domain (RBD) of spike protein of the virus. Milk is considered as one of the most nutrient-rich liquid foods having several antibacterial and antiviral activities. Milk derived peptides including Casein and Whey derived peptides are known to have profound anti-viral and immunomodulatory activities. After extensive literature search, some peptides having anti-viral activities against different viruses, are shortlisted for this study and their three-dimensional structures are modelled for protein-peptide docking against SARS-CoV-2 spike protein RBD. After performing protein-peptide docking and protein-protein docking using different servers such as HPEPDOCK, FIREDOCK, HADDOCK, HDOCK, it has been observed that in presence of the peptides, the interaction between spike RBD and hACE2 has been reasonably decreased, which implies that milk-derived peptides can be potential peptide-inhibitors against the RBD of the virus along with other medications. Further studies on milk-derived peptides should be performed to develop peptide drugs based on milk-derived peptides.

Covalent Flexible Peptide Docking in Rosetta

Electrophilic peptides that form an irreversible covalent bond with their target have great potential for binding targets that have been previously considered undruggable. However, the discovery of such peptides remains...

Divide-and-Link Peptide Docking: A Fragment-Based Peptide Docking Protocol

Protein-peptide interaction is crucial for various important cellular regulations, and also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained...

Identifikasi Peptida Bioaktif dari Protein Kedelai sebagai Inhibitor Enzim α-glukosidase untuk Kandidat Antidiabetes

Diabetes mellitus is one of the endocrine metabolic disorders that has caused morbidity and mortality worldwide. Α-glucosidase inhibitor which plays an important role in carbohydrate metabolism is needed to avoid postprandial hyperglycemia. A bioactive peptide derived from soy protein was chosen as an alternative treatment for diabetes because of its therapeutic potential. Several bioactive peptides have been shown to inhibit the α-glucosidase enzyme, such as the bioactive peptide LLPLPVLK, SWLRL, and WLRL. This study aims to identify and evaluate molecular interactions that occur between bioactive peptide molecules and α-glucosidase enzyme macromolecules using protein-peptide docking methods through in silico. Bioactive peptide sequencing was first modeled using the PEP-FOLD software. The best conformation was chosen for an interaction study of the α-glucosidase enzyme macromolecule using HPEPDock software. Further exploration was carried out on the molecular interactions formed using BIOVIA Discovery Studio 2020 software. Based on the results of molecular docking, the WLRL bioactive peptide has the best affinity against the α-glucosidase enzyme, with a binding free energy value of −748.12 kJ/mol. Therefore, the bioactive peptide is predicted to be a suitable candidate for the α-glucosidase enzyme inhibitor.

Functionalized osteoarthritis targeting peptides for MRI, lubricant and regenerative medicine

Despite the efforts made for osteoarthritis (OA) treatment, the results are limited and can be improved by enhancing the OA homing strategy. Here, we used a phage display system to identify OA-targeting peptides, and combined them with magnetic resonance imaging detection reagents to expand their application to early OA diagnosis in rat and swine models. OA-targeting peptides showed better static and kinetic friction characteristics than scrambled peptides, when conjugated with hyaluronic acid for rheological lubrication studies using human OA specimens. Furthermore, mesenchymal stem cells, through CD44 binding to hyaluronic acid conjugated with OA-targeting peptides, showed better capacity for OA homing and repair than those conjugated with scrambled peptides. Protein–peptide docking revealed WXPXW as the consensus binding motifs that bind to collagen XII, a protein exclusively expressed in human and animal OA models. These results suggest the potential of OA-targeting peptides to promote diagnosis, treatment, and regenerative medicine for OA.