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Acinetobacter baumannii, a prominent pathogen responsible for chronic infections in the blood, urinary tract, and lungs, has a high mortality due to its virulence and limited preventive methods. The present study aims to characterize the pilus assembly protein of A. baumannii to offer leads for epitope-based vaccine development. FilF is the putative pilus assembly protein that reportedly plays a supreme character in the virulence of this WHO-listed ESKAPE bacterium. Implementing various bioinformatics tools, led to the recognition of many antigenic B and T cell epitopes. Most promising B and T-cell epitopes were selected based on their binding efficiency with commonly occurring MHC alleles. Finally, we stepped down to fourteen protective antigenic peptides. These epitopes were also revealed to be non-allergenic and non-toxic. As a result, a vaccine chimera was created by linking these epitopes with appropriate linkers and adjuvant such as β-defensins. Furthermore, homology modeling and validation were carried out, with the modeled structure being employed for molecular docking with the immunological receptor (TLR-4) found on lymphocyte cells. As a result of the molecular dynamics simulation, the interaction between human TLR-4 and the multi-epitope vaccine sequence was stable. Finally, in silico cloning and immune simulation were carried out to see the efficacy of the construct vaccine. This is the first study targeting the pilus assembly protein from A. baumannii to identify novel epitopes that hold potential for further experimental design of multi-peptide vaccine construct against the pathogen.

Microalgae are potential feedstocks for the commercial production of carotenoids, however, the metabolic pathways for carotenoid biosynthesis across algal lineage are largely unexplored. This work is the first to provide a comprehensive survey of genes and enzymes associated with the less studied methylerythritol 4-phosphate/1-deoxy-D-xylulose 5-phosphate pathway as well as the carotenoid biosynthetic pathway in microalgae through bioinformatics and comparative genomics approach. Candidate genes/enzymes were subsequently analyzed across 22 microalgae species of lineages Chlorophyta, Rhodophyta, Heterokonta, Haptophyta, Cryptophyta, and known Arabidopsis homologs in order to study the evolutional divergence in terms of sequence-structure properties. A total of 403 enzymes playing a vital role in carotene, lutein, zeaxanthin, violaxanthin, canthaxanthin, and astaxanthin were unraveled. Of these, 85 were hypothetical proteins whose biological roles are not yet experimentally characterized. Putative functions to these hypothetical proteins were successfully assigned through a comprehensive investigation of the protein family, motifs, intrinsic physicochemical features, subcellular localization, pathway analysis, etc. Furthermore, these enzymes were categorized into major classes as per the conserved domain and gene ontology. Functional signature sequences were also identified which were observed conserved across microalgal genomes. Additionally, the structural modeling and active site architecture of three vital enzymes, DXR, PSY, and ZDS catalyzing the vital rate-limiting steps in Dunaliella salina were achieved. The enzymes were confirmed to be stereochemically reliable and stable as revealed during molecular dynamics simulation of 100 ns. The detailed functional information about individual vital enzymes will certainly help to design genetically modified algal strains with enhanced carotenoid contents.

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the 25th of November 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease., Graphical abstract Image 1, Highlights • This review presents a comprehensive and up-to-date overview of newly developed computational resources including tools and web servers focused on coronaviruses. • We have categorized these popular tools and databases depending on the utility and application such as for coronavirus detection, comparative genomics analysis, vaccine and drug discovery, molecular docking, tools for SARS-CoV-2 detection, and other applications in coronaviruses study. • The features of each of the tools have been discussed along with their application in recent coronavirus research studies.

Parkinson's disease (PD) is characterized by progressive death of dopamine producing neurons in the substantia nigra pars compacta of the mid brain. Dysfunction of an E3 ligase protein, Parkin, encoded by PARK2 gene, results in accumulation of misfolded proteins in brain cells which lead to the onset of PD. Parkin is a multi-domain protein consisting of N-terminal ubiquitin-like domain (Ubl) followed by RING0, RING1, In Between Ring (IBR) domain and RING2 domain which is present at the C-terminal end of Parkin protein. Ubl domain is the smallest domain of Parkin and is involved in the binding of Parkin with E2 protein molecule required for proper Ubiquitination and functioning of proteins in the brain. Mutations in the Parkin protein are known to be associated with protein dysfunction leading to PD. This study aims to decipher the characteristics and effects of the different mutations in the Ubl domain by an in-silico analysis. The mutations were collected from PDmutDB and COSMIC databases. The pathogenic impacts of amino-acid mutations on Ubl structure and function were analysed by using various computational tools. Due to lack of proper full-chain structure of the Ubl domain, a homology model of the domain was reconstructed using Discovery Studio 2.5 (DS 2.5) software suite. We found that the mutations A31D, A46P, C59F, A46T, E28K, E49K, R42P, R42S, and Q63K were the most deleterious ones which might be associated with the onset of PD. In order to study the dynamic behaviour of the Parkin Ubl domain in cellular environment, molecular dynamics (MD) simulations were carried out using the wild-type and mutant Ubl domains. Our analyses could predict the cellular dynamics of the mutations and therefore might help in predicting the hitherto unknown molecular mechanism of the disease onset and designing precision medicine for the treatment of PD.