Virtual screening of peptides with high affinity for SARS-CoV-2 main protease

Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides

Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

A Computer-Aided Approach for the Discovery of D-Peptides as Inhibitors of SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease, also known as 3-chymotrypsin-like protease (3CLpro), is a cysteine protease responsible for the cleavage of viral polyproteins pp1a and pp1ab, at least, at eleven conserved sites, which leads to the formation of mature nonstructural proteins essential for the replication of the virus. Due to its essential role, numerous studies have been conducted so far, which have confirmed 3CLpro as an attractive drug target to combat Covid-19 and have reported a vast number of inhibitors and their co-crystal structures. Despite all the ongoing efforts, D-peptides, which possess key advantages over L-peptides as therapeutic agents, have not been explored as potential drug candidates against 3CLpro. The current work fills this gap by reporting an in silico approach for the discovery of D-peptides capable of inhibiting 3CLpro that involves structure-based virtual screening (SBVS) of an in-house library of D-tripeptides and D-tetrapeptides into the protease active site and subsequent rescoring steps, including Molecular Mechanics Generalized-Born Surface Area (MM-GBSA) free energy calculations and molecular dynamics (MD) simulations. In vitro enzymatic assays conducted for the four top-scoring D-tetrapeptides at 20 μM showed that all of them caused 55–85% inhibition of 3CLpro activity, thus highlighting the suitability of the devised approach. Overall, our results present a promising computational strategy to identify D-peptides capable of inhibiting 3CLpro, with broader application in problems involving protein inhibition.

Biotherapeutic effect of cell-penetrating peptides against microbial agents: a review

Selective permeability of biological membranes represents a significant barrier to the delivery of therapeutic substances into both microorganisms and mammalian cells, restricting the access of drugs into intracellular pathogens. Cell-penetrating peptides usually 5-30 amino acids with the characteristic ability to penetrate biological membranes have emerged as promising antimicrobial agents for treating infections as well as an effective delivery modality for biological conjugates such as nucleic acids, drugs, vaccines, nanoparticles, and therapeutic antibodies. However, several factors such as antimicrobial resistance and poor drug delivery of the existing medications justify the urgent need for developing a new class of antimicrobials. Herein, we review cell-penetrating peptides (CPPs) used to treat microbial infections. Although these peptides are biologically active for infections, effective transduction into membranes and cargo transport, serum stability, and half-life must be improved for optimum functions and development of next-generation antimicrobial agents.

A cyclic peptide inhibitor of the SARS-CoV-2 main protease

This paper presents the design and study of a first-in-class cyclic peptide inhibitor against the SARS-CoV-2 main protease (M^pro). The cyclic peptide inhibitor is designed to mimic the conformation of a substrate at a C-terminal autolytic cleavage site of M^pro. The cyclic peptide contains a [4-(2-aminoethyl)phenyl]-acetic acid (AEPA) linker that is designed to enforce a conformation that mimics a peptide substrate of M^pro. In vitro evaluation of the cyclic peptide inhibitor reveals that the inhibitor exhibits modest activity against M^pro and does not appear to be cleaved by the enzyme. Conformational searching predicts that the cyclic peptide inhibitor is fairly rigid, adopting a favorable conformation for binding to the active site of M^pro. Computational docking to the SARS-CoV-2 M^pro suggests that the cyclic peptide inhibitor can bind the active site of M^pro in the predicted manner. Molecular dynamics simulations provide further insights into how the cyclic peptide inhibitor may bind the active site of M^pro. Although the activity of the cyclic peptide inhibitor is modest, its design and study lays the groundwork for the development of additional cyclic peptide inhibitors against M^pro with improved activities.