Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202

Structure based de novo design of IspD inhibitors as anti-tubercular agents

Tuberculosis is one of the leading contagious diseases, caused by Mycobacterium tuberculosis. Despite improvements in anti-tubercular agents, it remains one of the most prevalent infectious diseases worldwide, responsible for a total of 1.6 million deaths annually. The emergence of multidrug resistant strains highlighted the need of discovering novel drug targets for the development of anti-tubercular agents. 2-C-methyl-D-erythritol-4-phosphate cytidyltransferase (IspD) is an enzyme involved in MEP pathway for isoprenoid biosynthesis, which is considered an attractive target for the discovery of novel antibiotics for its essentiality in bacteria and absence in mammals. In the present study, we have employed structure based drug design approach to develop novel and potent inhibitors for IspD receptor. To explore binding affinity and hydrogen bond interaction between the ligand and active site of IspD receptor, docking studies were performed. ADMET and synthetic accessibility filters were used to screen designed molecules. Finally, ten compounds were selected and subsequently submitted for the synthesis and in vitro studies as IspD inhibitors

Recovering complete and draft population genomes from metagenome datasets.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution