Rilpivirine inhibits SARS-CoV-2 protein targets: A potential multi-target drug

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction

Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Current DTI methods predominantly focus on extracting semantic features from drug and protein sequences or utilizing structural information, often neglecting the integration of both. This gap hinders the achievement of a comprehensive representation of drug and protein molecules. To address this, we propose SS-DTI, a novel end-to-end deep learning approach that integrates both semantic and structural information. Our method features a multi-scale semantic feature extraction block to capture local and global information from sequences and employs Graph Convolutional Networks (GCNs) to learn structural features. Evaluations on four benchmark datasets demonstrate that SS-DTI outperforms state-of-the-art methods, showcasing its superior predictive performance. Our code is available at https://github.com/RobinChun/SS-DTI.

Identification of steroidal cardenolides from Calotropis procera as novel HIV-1 PR inhibitors: A molecular docking & molecular dynamics simulation study

Background & objectives Despite advancements in antiretroviral therapy, drug-resistant strains of HIV (human immunodeficiency virus) remain a global health concern. Natural compounds from medicinal plants offer a promising avenue for developing new HIV-1 PR (protease) inhibitors. This study aimed to explore the potential of compounds derived from Calotropis procera, a medicinal plant, as inhibitors of HIV-1 PR. Methods This in silico study utilized natural compound information and the crystal structure of HIV-1 PR. Molecular docking of 17 steroidal cardenolides from Calotropis procera against HIV-1 PR was performed using AutoDock 4.2 to identify compounds with higher antiviral potential. A dynamic simulation study was performed to provide insights into the stability, binding dynamics, and potential efficacy of the top potential antiviral compound as an HIV-1 therapeutic. Results We found that all tested cardenolides had higher binding affinities than Amprenavir, indicating their potential as potent HIV-1 PR inhibitors. Voruscharin and uscharidin displayed the strongest interactions, forming hydrogen bonds and hydrophobic interactions with HIV-1 PR. Voruscharin showed improved stability with lower RMSD (Root Mean Square Deviation) values and reduced fluctuations in binding site residues but increased flexibility in certain regions. The radius of gyration analysis confirmed a stable binding pose between HIV-1 PR and Voruscharin. Interpretation & conclusions These findings suggest that Calotropis procera could potentially be a source of compounds for developing novel HIV-1 PR inhibitors, contributing to the efforts to combat HIV. Further studies and clinical trials are needed to evaluate the safety and efficacy of these compounds as potential drug candidates for the treatment of HIV-1 infection.

VIPERA: Viral Intra-Patient Evolution Reporting and Analysis

Viral mutations within patients nurture the adaptive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intra-patient SARS-CoV-2 serial samples. Herein, we describe Viral Intra-Patient Evolution Reporting and Analysis (VIPERA), a new software that integrates the evaluation of the intra-patient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection. We have validated it using positive and negative control datasets and have successfully applied it to a new case, which revealed population dynamics and evidence of adaptive evolution. VIPERA is available under a free software license at https://github.com/PathoGenOmics-Lab/VIPERA.

Monoclonal neutralizing antibodies against SARS-COV-2 S protein

Novel coronavirus pneumonia, also known as coronavirus disease 2019 (COVID-19), is caused by sub-severe acute respiratory syndrome type 2 coronavirus (SARS-CoV-2) infection. The spike (S) protein of SARS-CoV-2 binds to angiotensin-converting enzyme 2 (ACE2) receptors widely expressed on the surface of human cells leading to life-threatening respiratory infections. A serious hazard to human health is posed by the lack of particular treatment medications for this virus infection. We advocate the creation of high-affinity antibodies using the receptor binding domain (RBD) of S protein as a specific antigenic epitope to develop a drug that can precisely target therapy COVID-19 because SARS-CoV-2 infection of the host cells is dependent on S protein binding to ACE2. Finally, we obtained high-affinity antibodies 14F4HL and 14E3HL that have high affinity with RBD and well-drug-forming properties, suitable for further humanization studies. Thus, monoclonal antibodies that neutralize the S protein were identified in our study, which may provide new insights for the development of COVID-19 therapeutic drugs.

Main and papain-like proteases as prospective targets for pharmacological treatment of coronavirus SARS-CoV-2

The pandemic caused by the coronavirus SARS-CoV-2 led to a global crisis in the world healthcare system. Despite some progress in the creation of antiviral vaccines and mass vaccination of the population, the number of patients continues to grow because of the spread of new SARS-CoV-2 mutations. There is an urgent need for direct-acting drugs capable of suppressing or stopping the main mechanisms of reproduction of the coronavirus SARS-CoV-2. Several studies have shown that the successful replication of the virus in the cell requires proteolytic cleavage of the protein structures of the virus. Two proteases are crucial in replicating SARS-CoV-2 and other coronaviruses: the main protease (Mpro) and the papain-like protease (PLpro). In this review, we summarize the essential viral proteins of SARS-CoV-2 required for its viral life cycle as targets for chemotherapy of coronavirus infection and provide a critical summary of the development of drugs against COVID-19 from the drug repurposing strategy up to the molecular design of novel covalent and non-covalent agents capable of inhibiting virus replication. We overview the main antiviral strategy and the choice of SARS-CoV-2 Mpro and PLpro proteases as promising targets for pharmacological impact on the coronavirus life cycle.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Synergistic interactions of repurposed drugs that inhibit Nsp1, a major virulence factor for COVID-19

Nsp1 is one of the first proteins expressed from the SARS-CoV-2 genome and is a major virulence factor for COVID-19. A rapid multiplexed assay for detecting the action of Nsp1 was developed in cultured lung cells. The assay is based on the acute cytopathic effects induced by Nsp1. Virtual screening was used to stratify compounds that interact with two functional Nsp1 sites: the RNA-binding groove and C-terminal helix-loop-helix region. Experimental screening focused on compounds that could be readily repurposed to treat COVID-19. Multiple synergistic combinations of compounds that significantly inhibited Nsp1 action were identified. Among the most promising combinations are Ponatinib, Rilpivirine, and Montelukast, which together, reversed the toxic effects of Nsp1 to the same extent as null mutations in the Nsp1 gene.