Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes the papain-like protease (PLpro). The protein not only plays an essential role in viral replication but also cleaves ubiquitin and ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from host proteins, making it an important target for developing new antiviral drugs. In this study, we searched for novel, noncovalent potential PLpro inhibitors by employing a multistep in silico screening of a 15 million compound library. The selectivity of the best-scored compounds was evaluated by checking their binding affinity to the human ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), which, as a deubiquitylating enzyme, exhibits structural and functional similarities to the PLpro. As a result, we identified 387 potential, selective PLpro inhibitors, from which we retrieved the 20 best compounds according to their IC50 values toward PLpro estimated by a multiple linear regression model. The selected candidates display potential activity against the protein with IC50 values in the nanomolar range from approximately 159 to 505 nM and mostly adopt a similar binding mode to the known, noncovalent SARS-CoV-2 PLpro inhibitors. We further propose the six most promising compounds for future in vitro evaluation. The results for the top potential PLpro inhibitors are deposited in the database prepared to facilitate research on anti-SARS-CoV-2 drugs.

AlphaKnot is a server that measures entanglement in AlphaFold-solved protein models while considering pLDDT confidence values. AlphaKnot has two main functions: (i) providing researchers with a webserver for analyzing knotting in their own AlphaFold predictions and (ii) providing a database of knotting in AlphaFold predictions from the 21 proteomes for which models have been published prior to 2022. The knotting is defined in a probabilistic fashion. The knotting complexity of proteins is presented in the form of a matrix diagram which shows users the knot type for the entire polypeptide chain and for each of its subchains. The dominant knot types as well as the computed locations of the knot cores (i.e. minimal portions of protein backbones that form a given knot type) are shown for each protein structure. Based mainly on the pLDDT confidence values, entanglements are classified as Knots, Unsure, and Artifacts. The database portion of the server can be used, for example, to examine protein geometry and entanglement-function correlations, as a reference set for protein modeling, and for facilitating evolutional studies. The AlphaKnot server can be found at https://alphaknot.cent.uw.edu.pl/.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused both a health and economic crisis around the world. Its papain-like protease (PLpro) is one of the protein targets utilized in designing new drugs that would aid vaccines in the fight against the virus. Although there are already several potential candidates for a good inhibitor of this protein, the degree of variability of the protein itself is not taken into account. As an RNA virus, SARS-CoV-2 can mutate to a high degree, but PLpro variability has not been studied to date. Based on sequence data available in databases, we analyzed the mutational potential of this protein. We focused on the effect of observed mutations on inhibitors’ binding mode and their efficacy as well as protein’s activity. Our analysis identifies five mutations that should be monitored and included in the drug design process: P247S, E263D-Y264H and T265A-Y268C.