Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery.

Absolute binding free energies play a crucial role in drug development, particularly as part of the lead discovery process. In recent work, we showed how in silico predictions directly could support drug development by ranking and recommending favorable ideas over unfavorable ones. Here, we demonstrate a Python workflow that enables the calculation of ABFEs with minimal manual input effort, such as the receptor PDB and ligand SDF files, and outputs a .tsv file containing the ranked ligands and their corresponding binding free energies. The implementation uses Snakemake to structure and control the execution of tasks, allowing for dynamic control of parameters and execution patterns. We provide an example of a benchmark system that demonstrates the effectiveness of the automated workflow.

Drug repurposing and sequence analysis in S-glycoprotein variants reveals critical signature patterns and destabilization of receptor-binding domain in omicron variant.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus since its emergence in 2019 has yielded several new viral variants with varied infectivity, disease severity, and antigenicity. Although most mutations are expected to be relatively neutral, mutations at the Spike region of the genome have shown to have a major impact on the viral transmission and infection in humans. Therefore, it is crucial to survey the structures of spike protein across the global virus population to contextualize the rate of therapeutic success against these variants. In this study, high-frequency mutational variants from different geographic regions were pooled in order to study the structural evolution of the spike protein through drug docking and MD simulations. We investigated the mutational burden in the spike subregions and have observed that the different variants harbour unique signature patterns in the spike subregions, with certain domains being highly prone to mutations. Further, the MD simulations and docking study revealed that different variants show differential stability when docked for the same set of drug targets. This work sheds light on the mutational burden and the stability landscape of the spike protein across the variants from different geographical regions.Communicated by Ramaswamy H. Sarma.

A comprehensive SARS-CoV-2 genomic analysis identifies potential targets for drug repurposing.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is a novel human coronavirus strain (HCoV) was initially reported in December 2019 in Wuhan City, China. This acute infection caused pneumonia-like symptoms and other respiratory tract illness. Its higher transmission and infection rate has successfully enabled it to have a global spread over a matter of small time. One of the major concerns involving the SARS-COV-2 is the mutation rate, which enhances the virus evolution and genome variability, thereby making the design of therapeutics difficult. In this study, we identified the most common haplotypes from the haplotype network. The conserved genes and population level variants were analysed. Non-Structural Protein 10 (NSP10), Nucleoprotein, Papain-like protease (Plpro or NSP3) and 3-Chymotrypsin like protease (3CLpro or NSP5), which were conserved at the highest threshold, were used as drug targets for molecular dynamics simulations. Darifenacin, Nebivolol, Bictegravir, Alvimopan and Irbesartan are among the potential drugs, which are suggested for further pre-clinical and clinical trials. This particular study provides a comprehensive targeting of the conserved genes. We also identified the mutation frequencies across the viral genome.

Analysing non-synonymous mutations in XDR and MDR tuberculosis drugs.

Tuberculosis is a bacterial disease caused by Mycobacterium tuberculosis. It is known to be the second-largest cause of death and models a severe risk to public health throughout the world. Though it affects people of almost every age, individuals with weakened immune systems, (e.g., HIV infection) are more likely to get infected. The present study deals with analyzing non-synonymous mutations in anti-tuberculosis drugs, which may have a significant role in causing XDR and MDR tuberculosis drug resistance. Continued use of tuberculosis drugs, discontinuation of medicines and various other factors can promote drug resistance in the host's body. To understand the actual cause of resistance, we have identified some patterns of mutations which might be responsible for a change in the structure of the protein, ultimately causing drug resistance. Here, we aim to present some of the unique mutation patterns in the genes associated with the marketed drugs that might have a deleterious effect. In this study, we have used molecular docking approach for understanding the ligand binding affinity of the mutated drugs. The results are further validated by molecular dynamics studies.