Identification of Triazolopyrimidinyl Scaffold SARS-CoV-2 Papain-Like Protease (PLpro) Inhibitor.

The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its companion disease, COVID-19, has reminded us of the importance of basic coronaviral research. In this study, a comprehensive approach using molecular docking, in vitro assays, and molecular dynamics simulations was applied to identify potential inhibitors for SARS-CoV-2 papain-like protease (PLpro), a key and underexplored viral enzyme target. A focused protease inhibitor library was initially created and molecular docking was performed using CmDock software (v0.2.0), resulting in the selection of hit compounds for in vitro testing on the isolated enzyme. Among them, compound 372 exhibited promising inhibitory properties against PLpro, with an IC50 value of 82 ± 34 µM. The compound also displayed a new triazolopyrimidinyl scaffold not yet represented within protease inhibitors. Molecular dynamics simulations demonstrated the favorable binding properties of compound 372. Structural analysis highlighted its key interactions with PLpro, and we stress its potential for further optimization. Moreover, besides compound 372 as a candidate for PLpro inhibitor development, this study elaborates on the PLpro binding site dynamics and provides a valuable contribution for further efforts in pan-coronaviral PLpro inhibitor development.

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening.

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME).

High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Fichert, Ghose, Mozzicconacci, Muegge, Egan, Murcko, Veber, Ro3, Ro4, and Ro5) to facilitate their open access use and compared them. Then, we implemented these filters in the open platform Konstanz Information Miner (KNIME) as a freely accessible and simple workflow compatible with small or large compound databases for the benefit of the readers and for the help in the early drug design steps.

Molecular Dynamics Simulations Reveal Interactions of an IgG1 Antibody With Selected Fc Receptors.

In a survey of novel interactions between an IgG1 antibody and different Fcγ receptors (FcγR), molecular dynamics simulations were performed of interactions of monoclonal antibody involved complexes with FcγRs. Free energy simulations were also performed of isolated wild-type and substituted Fc regions bound to FcγRs with the aim of assessing their relative binding affinities. Two different free energy calculation methods, Molecular Mechanical/Generalized Born Molecular Volume (MM/GBMV) and Bennett Acceptance Ratio (BAR), were used to evaluate the known effector substitution G236A that is known to selectively increase antibody dependent cellular phagocytosis. The obtained results for the MM/GBMV binding affinity between different FcγRs are in good agreement with previous experiments, and those obtained using the BAR method for the complete antibody and the Fc-FcγR simulations show increased affinity across all FcγRs when binding to the substituted antibody. The FcγRIIa, a key determinant of antibody agonistic efficacy, shows a 10-fold increase in binding affinity, which is also consistent with the published experimental results. Novel interactions between the Fab region of the antibody and the FcγRs were discovered with this in silico approach, and provide insights into the antibody-FcγR binding mechanism and show promise for future improvements of therapeutic antibodies for preclinical studies of biological drugs.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design.

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.