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

Abstract

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.

D‐Peptide and D‐Protein Technology: Recent Advances, Challenges, and Opportunities**

“…In addition, the use of MIPD to identify competitive antagonists is limited by the arbitrary selection of off‐target binders. Efforts have been directed to computation‐based approaches with the goal to replace the tedious synthesis with in silico studies, including the docking of mirror‐image helices derived from the PDB; [88] screening of D‐tri/tetra peptides against the target active site; [89] virtual affinity maturation based on existing heterochiral structures [90] . However, existing in silico methods suffer from a lack of library diversity and polypeptide binder size, limiting the best example to a binding affinity of 20 μM [89] …”

D-Peptide and D-Protein technology: Recent advances, challenges, and opportunities

“…In addition, the use of MIPD to identify competitive antagonists is limited by the arbitrary selection of off-target binders. Efforts have been directed to computation-based approaches with the goal to replace the tedious synthesis with in silico studies, including the docking of mirror-image helices derived from the PDB; [80] screening of D-tri/tetra peptides against the target active site; [81] virtual affinity maturation based on existing heterochiral structures. [82] However, existing in silico methods suffer from a lack of library diversity and polypeptide binder size, limiting the best example to a binding affinity of 20 μM.…”

“…[82] However, existing in silico methods suffer from a lack of library diversity and polypeptide binder size, limiting the best example to a binding affinity of 20 μM. [81]…”

“…[28] In addition, discovery of D-peptide binders could be achieved via computational-based approaches. [80][81]139] Together, while there are challenges remained to be solved, D-polypeptide research remains to have strong potentials that can generate explosive impacts on numerous research topics.…”

Discovery of all-D-peptide inhibitors of SARS CoV 2 3C-like protease

“…[26][27][28] In the case of SARS-CoV-2, three studies describe the identification of Dpeptide inhibitors against SARS-CoV-2 Spike protein and 3CL pro . [29][30][31] Of particular interest are all-D-peptides that are composed of D-amino acid residues. However, many peptidebased drug development strategies, like phage display selection, end up with L-peptides.…”