ABSTRACT
Targeting RNA methyltransferases with small molecules as inhibitors or tool compounds is an emerging field of interest in epitranscriptomics and medicinal chemistry. For two challenging RNA methyltransferases that introduce the 5-methylcytosine (m5C) modification in different tRNAs, namely DNMT2 and NSUN6, an ultra-large commercially available chemical space was virtually screened by physicochemical property filtering, molecular docking, and clustering to identify new ligands for those enzymes. Novel chemotypes binding to DNMT2 and NSUN6 with affinities down to KD,app = 37 µM and KD,app = 12 µM, respectively, were identified using a microscale thermophoresis (MST) binding assay. These compounds represent the first molecules with a distinct structure from the cofactor SAM and have the potential to be developed into activity-based probes for these enzymes. Additionally, the challenges and strategies of chemical space docking screens with special emphasis on library focusing and diversification are discussed.
Subject(s)
Methyltransferases , RNA , Molecular Docking Simulation , RNA, Transfer/chemistry , DNA (Cytosine-5-)-Methyltransferases , tRNA MethyltransferasesABSTRACT
The transmembrane serine protease 2 (TMPRSS2) primes the SARS-CoV-2 Spike (S) protein for host cell entry and represents a promising target for COVID-19 therapy. Here we describe the in silico development and in vitro characterization of peptidomimetic TMPRSS2 inhibitors. Molecular docking studies identified peptidomimetic binders of the TMPRSS2 catalytic site, which were synthesized and coupled to an electrophilic serine trap. The compounds inhibit TMPRSS2 while demonstrating good off-target selectivity against selected coagulation proteases. Lead candidates are stable in blood serum and plasma for at least ten days. Finally, we show that selected peptidomimetics inhibit SARS-CoV-2 Spike-driven pseudovirus entry and authentic SARS-CoV-2 infection with comparable efficacy as camostat mesylate. The peptidomimetic TMPRSS2 inhibitors also prevent entry of recent SARS-CoV-2 variants of concern Delta and Omicron BA.1. In sum, our study reports antivirally active and stable TMPRSS2 inhibitors with prospects for further preclinical and clinical development as antiviral agents against SARS-CoV-2 and other TMPRSS2-dependent viruses.
Subject(s)
COVID-19 Drug Treatment , Peptidomimetics , Cell Culture Techniques , Humans , Molecular Docking Simulation , Peptidomimetics/pharmacology , SARS-CoV-2 , Serine Endopeptidases/geneticsABSTRACT
Due to its fast international spread and substantial mortality, the coronavirus disease COVID-19 evolved to a global threat. Since there is currently no causative drug against this viral infection available, science is striving for new drugs and other approaches to treat the new disease. Studies have shown that the cell entry of coronaviruses into host cells takes place through the binding of the viral spike (S) protein to cell receptors. Priming of the S protein occurs via hydrolysis by different host proteases. The inhibition of these proteases could impair the processing of the S protein, thereby affecting the interaction with the host-cell receptors and preventing virus cell entry. Hence, inhibition of these proteases could be a promising strategy for treatment against SARSCoV- 2. In this review, we discuss the current state of the art of developing inhibitors against the entry proteases furin, the transmembrane serine protease type-II (TMPRSS2), trypsin, and cathepsin L.
Subject(s)
COVID-19 Drug Treatment , SARS-CoV-2 , Humans , Serine Proteases , Spike Glycoprotein, Coronavirus/metabolism , Virus InternalizationABSTRACT
Inhibition of coronavirus (CoV)-encoded papain-like cysteine proteases (PLpro ) represents an attractive strategy to treat infections by these important human pathogens. Herein we report on structure-activity relationships (SAR) of the noncovalent active-site directed inhibitor (R)-5-amino-2-methyl-N-(1-(naphthalen-1-yl)ethyl) benzamide (2 b), which is known to bind into the S3 and S4 pockets of the SARS-CoV PLpro . Moreover, we report the discovery of isoindolines as a new class of potent PLpro inhibitors. The studies also provide a deeper understanding of the binding modes of this inhibitor class. Importantly, the inhibitors were also confirmed to inhibit SARS-CoV-2 replication in cell culture suggesting that, due to the high structural similarities of the target proteases, inhibitors identified against SARS-CoV PLpro are valuable starting points for the development of new pan-coronaviral inhibitors.
Subject(s)
Antiviral Agents/pharmacology , Benzamides/pharmacology , Coronavirus 3C Proteases/metabolism , Cysteine Proteinase Inhibitors/pharmacology , Isoindoles/pharmacology , SARS-CoV-2/drug effects , Animals , Antiviral Agents/chemical synthesis , Antiviral Agents/metabolism , Benzamides/chemical synthesis , Benzamides/metabolism , Catalytic Domain , Chlorocebus aethiops , Coronavirus 3C Proteases/chemistry , Crystallography, X-Ray , Cysteine Proteinase Inhibitors/chemical synthesis , Cysteine Proteinase Inhibitors/metabolism , Isoindoles/chemical synthesis , Isoindoles/metabolism , Molecular Docking Simulation , Molecular Structure , Protein Binding , Structure-Activity Relationship , Vero Cells , Virus Replication/drug effectsABSTRACT
The analysis of B-factor profiles from X-ray protein structures can be utilized for structure-based drug design since protein mobility changes have been associated with the quality of protein-ligand interactions. With the BANΔIT (B'-factor analysis and ΔB' interpretation toolkit), we have developed a JavaScript-based browser application that provides a graphical user interface for the normalization and analysis of B'-factor profiles. To emphasize the usability for rational drug design applications, we have analyzed a selection of crystallographic protein-ligand complexes and have given exemplary conclusions for further drug optimization including the development of a B'-factor-supported pharmacophore model for SARS CoV-2 main protease inhibitors. BANΔIT is available online at https://bandit.uni-mainz.de. The source code can be downloaded from https://github.com/FBarthels/BANDIT.