Functional map of SARS-CoV-2 3CL protease reveals tolerant and immutable sites.

The SARS-CoV-2 3CL protease (3CLpro) is an attractive therapeutic target, as it is essential to the virus and highly conserved among coronaviruses. However, our current understanding of its tolerance to mutations is limited. Here, we develop a yeast-based deep mutational scanning approach to systematically profile the activity of all possible single mutants of the 3CLpro and validate a subset of our results within authentic viruses. We reveal that the 3CLpro is highly malleable and is capable of tolerating mutations throughout the protein. Yet, we also identify specific residues that appear immutable, suggesting that these may be targets for future 3CLpro inhibitors. Finally, we utilize our screening as a basis to identify E166V as a resistance-conferring mutation against the clinically used 3CLpro inhibitor, nirmatrelvir. Collectively, the functional map presented herein may serve as a guide to better understand the biological properties of the 3CLpro and for drug development against coronaviruses.

The Functional Landscape of SARS-CoV-2 3CL Protease.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) as the etiologic agent of COVID-19 (coronavirus disease 2019) has drastically altered life globally. Numerous efforts have been placed on the development of therapeutics to treat SARS-CoV-2 infection. One particular target is the 3CL protease (3CL pro ), which holds promise as it is essential to the virus and highly conserved among coronaviruses, suggesting that it may be possible to find broad inhibitors that treat not just SARS-CoV-2 but other coronavirus infections as well. While the 3CL protease has been studied by many groups for SARS-CoV-2 and other coronaviruses, our understanding of its tolerance to mutations is limited, knowledge which is particularly important as 3CL protease inhibitors become utilized clinically. Here, we develop a yeast-based deep mutational scanning approach to systematically profile the activity of all possible single mutants of the SARS-CoV-2 3CL pro , and validate our results both in yeast and in authentic viruses. We reveal that the 3CL pro is highly malleable and is capable of tolerating mutations throughout the protein, including within the substrate binding pocket. Yet, we also identify specific residues that appear immutable for function of the protease, suggesting that these interactions may be novel targets for the design of future 3CL pro inhibitors. Finally, we utilize our screening results as a basis to identify E166V as a resistance-conferring mutation against the therapeutic 3CL pro inhibitor, nirmatrelvir, in clinical use. Collectively, the functional map presented herein may serve as a guide for further understanding of the biological properties of the 3CL protease and for drug development for current and future coronavirus pandemics.

CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework.

Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).

IAMPE: NMR-Assisted Computational Prediction of Antimicrobial Peptides.

Antimicrobial peptides (AMPs) are at the focus of attention due to their therapeutic importance and developing computational tools for the identification of efficient antibiotics from the primary structure. Here, we utilized the 13CNMR spectral of amino acids and clustered them into various groups. These clusters were used to build feature vectors for the AMP sequences based on the composition, transition, and distribution of cluster members. These features, along with the physicochemical properties of AMPs were exploited to learn computational models to predict active AMPs solely from their sequences. Naïve Bayes (NB), k-nearest neighbors (KNN), support-vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to build the classification system using the collected AMP datasets from the CAMP, LAMP, ADAM, and AntiBP databases. Our results were validated and compared with the CAMP and ADAM prediction systems and indicated that the synergistic combination of the 13CNMR features with the physicochemical descriptors enables the proposed ensemble mechanism to improve the prediction performance of active AMP sequences. Our web-based AMP prediction platform, IAMPE, is available at http://cbb1.ut.ac.ir/.