YM155 for COVID-19

Abstract The papain-like protease of SARS-CoV-2 (PLpro2) is integral to viral polyprotein cleavage and the modulation of host immune responses, positioning it as a critical target for antiviral drug development. Here, we elucidate the molecular mechanisms governing the noncovalent inhibition of PLpro2 through a comprehensive computational approach, including molecular docking, extensive molecular dynamics (MD) simulations, binding free energy calculations (MM/GBSA and SIE), principal component and free energy landscape (PCA/FEL) analyses, and protein–ligand interaction fingerprinting (ProLIF). We assessed a structurally diverse set of noncovalent inhibitors for their capacity to induce conformational rearrangements and stabilize key structural motifs of PLpro2, with particular emphasis on the BL2 loop. Notably, XR3 and A19 exhibited superior experimental and predicted binding affinities, which can be attributed to favorable contacts with essential residues Tyr268 and Gln269, the attenuation of loop dynamics, and the stabilization of energetically favorable conformational states. By contrast, less potent inhibitors were associated with increased conformational heterogeneity, fragmented free energy landscapes, and diminished interactions with critical loop residues. Therefore, our integrative analysis delineates the structural and energetic determinants underpinning noncovalent PLpro2 inhibition, underscoring the central roles of loop immobilization and π-stacking interactions in the rational design of next-generation PLpro2 inhibitors.

Papain-like protease (PLpro), a crucial functional domain of the SARS-CoV-2 non-structural protein 3 (nsp3), plays a dual role in both hydrolyzing viral polyprotein precursors and modulating host immune responses. These critical functions position PLpro as a key target in the ongoing development of antiviral therapies for SARS-CoV-2. This review analyzes more than 100 PLpro-ligand co-crystal structures and summarizes the major binding modes between these ligands and PLpro. Most of these ligands bind to sites analogous to those targeted by the classical non-covalent inhibitor GRL0617, primarily involving the P3 and P4 subsites and the BL2 loop. Based on these structural insights, optimized inhibitors have expanded targeting beyond the canonical binding site to auxiliary regions such as the BL2 groove and the Val70 site, and in some cases toward the catalytic Cys111 buried within a narrow pocket. Certain ligands identified through various screening approaches bind to non-canonical or allosteric regions, such as the S1 and S2 sites or the zinc-finger domain, engaging PLpro through distinct interaction modes and thereby offering additional opportunities for PLpro inhibitor design. The review also discusses potential strategies for future PLpro inhibitor development informed by recent structural advances. Taken together, these structural and functional insights support ongoing efforts in the structure-guided design and optimization of PLpro inhibitors.

The coronavirus disease 2019 (COVID-19) is a severe worldwide pandemic. Due to the emergence of various SARS-CoV-2 variants and the presence of only one Food and Drug Administration (FDA) approved anti-COVID-19 drug (remdesivir), the disease remains a mindboggling global public health problem. Developing anti-COVID-19 drug candidates that are effective against SARS-CoV-2 and its various variants is a pressing need that should be satisfied. This systematic review assesses the existing literature that used in silico models during the discovery procedure of anti-COVID-19 drugs. Cochrane Library, Science Direct, Google Scholar, and PubMed were used to conduct a literature search to find the relevant articles utilizing the search terms “In silico model,” “COVID-19,” “Anti-COVID-19 drug,” “Drug discovery,” “Computational drug designing,” and “Computer-aided drug design.” Studies published in English between 2019 and December 2022 were included in the systematic review. From the 1120 articles retrieved from the databases and reference lists, only 33 were included in the review after the removal of duplicates, screening, and eligibility assessment. Most of the articles are studies that use SARS-CoV-2 proteins as drug targets. Both ligand-based and structure-based methods were utilized to obtain lead anti-COVID-19 drug candidates. Sixteen articles also assessed absorption, distribution, metabolism, excretion, toxicity (ADMET), and drug-likeness properties. Confirmation of the inhibitory ability of the candidate leads by in vivo or in vitro assays was reported in only five articles. Virtual screening, molecular docking (MD), and molecular dynamics simulation (MDS) emerged as the most commonly utilized in silico models for anti-COVID-19 drug discovery.