The papain-like protease (PLpro) is vital for the replication of coronaviruses (CoVs), as well as for escaping innate-immune responses of the host. Hence, it has emerged as an attractive antiviral drug-target. In this study, computational approaches were employed, mainly the structure-based virtual screening coupled with all-atom molecular dynamics (MD) simulations to computationally identify specific inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PLpro, which can be further developed as potential pan-PLpro based broad-spectrum antiviral drugs. The sequence, structure, and functional con-serveness of most deadly human CoVs PLpro were explored, and it was revealed that functionally important catalytic triad residues are well conserved among SARS-CoV, SARS-CoV-2, and middle east respiratory syndrome coronavirus (MERS-CoV). The subsequent screening of a focused protease in-hibitors database composed of ～7,000 compounds resulted in the identification of three candidate compounds, ADM_13083841, LMG_15521745, and SYN_15517940. These three compounds established conserved interactions which were further explored through MD simulations, free energy calculations, and residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the active residues of SARS-CoV-2 PLpro, and showed consistent interaction profile with SARS-CoV PLpro and MERS-CoV PLpro as well. Conclusively, the re-ported SARS-CoV-2 PLpro specific compounds could serve as seeds for developing potent pan-PLpro based broad-spectrum antiviral drugs against deadly human coronaviruses. Moreover, the presented infor-mation related to binding site residual energy contribution could lead to further optimization of these compounds.

Recently emerged SARS-CoV-2 caused a major outbreak of coronavirus disease 2019 (COVID-19) and instigated a widespread fear, threatening global health safety. To date, no licensed antiviral drugs or vaccines are available against COVID-19 although several clinical trials are under way to test possible therapies. During this urgent situation, computational drug discovery methods provide an alternative to tiresome high-throughput screening, particularly in the hit-to-lead-optimization stage. Identification of small molecules that specifically target viral replication apparatus has indicated the highest potential towards antiviral drug discovery. In this work, we present potential compounds that specifically target SARS-CoV-2 vital proteins, including the main protease, Nsp12 RNA polymerase and Nsp13 helicase. An integrative virtual screening and molecular dynamics simulations approach has facilitated the identifi-cation of potential binding modes and favourable molecular interaction profile of corresponding com-pounds. Moreover, the identification of structurally important binding site residues in conserved motifs located inside the active site highlights relative importance of ligand binding based on residual energy decomposition analysis. Although the current study lacks experimental validation, the structural infor-mation obtained from this computational study has paved way for the design of targeted inhibitors to combat COVID-19 outbreak.

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity.Although noncanonical residues can always be used,employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening.Towards this goal,the dataset comprising all possible di-,tri-,and tetra-peptide com-binations of the canonical residues has been previously reported.However,with increasing computa-tional power,the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues.Here,we provide both the com-plete and prefiltered libraries of all di-,tri-,tetra-,and penta-peptide sequences from 20 canonical amino acids and their homodetic(N-to-C-terminal)cyclic homologues.The FASTA,simplified molecular-input line-entry system(SMILES),and structure-data file(SDF)-three dimension(3D)libraries can be readily used for screening against protein targets.We also provide a simple method and tool for conducting identity-based filtering.Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns.As a case study,the developed library was screened against severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)main protease to identify po-tential small peptide inhibitors.