Exploiting AGPase genes and encoded proteins to prioritize development of optimum engineered strains in microalgae towards sustainable biofuel production.

Although ADP glucose pyrophosphorylase (AGPase), with two large subunits (ls) and two small subunits (ss), is a promising knockout target for increasing the neutral lipid content, the details regarding the sequence-structure features and their distribution within metabolic system in microalgae is rather limited. Against this backdrop, a comprehensive genome-wide comparative analysis on 14 sequenced microalgal genomes was performed. For the first time the heterotetrameric structure of the enzyme and the interaction of the catalytic unit with the substrate was also studied. Novel findings of the present study includes: (i) at the DNA level, the genes controlling the ss are more conserved than those controlling the ls; the variation in both the gene groups is mainly due to exon number, exon length and exon phase distribution; (ii) at protein level, the ss genes are more conserved relative to those for ls; (III) three putative key consensus sequences 'LGGGAGTRLYPLTKNRAKPAV', 'WFQGTADAV' and 'ASMGIYVFRKD' were ubiquitously conserved in all the AGPases; (iv) molecular dynamics investigations revealed that the modeled AGPase heterotetrameric structure, from oleaginous algae Chlamydomonas reinharditii, was completely stable in real time environment; (v) The binding interfaces of catalytic unit, ssAGPase, from C. reinharditii with α-D-glucose 1-phosphate (αGP) was also analyzed. The results of the present study have provided system-based insights into the structure-function of the genes and encoded proteins, which provided clues for exploitation of variability in these genes that, could be further utilized to design site-specific mutagenic experiments for engineering of microalgal strains towards sustainable development of biofuel.

Comprehensive sequence and structure analysis of algal lipid catabolic enzyme Triacylglycerol lipase: an in silico study to vitalize the development of optimum engineered strains with high lipid productivity.

Microalgae as an alternative renewable resource for biofuel production have captured much significance. Nonetheless, its economic viability is a field of major concern for researchers. Unraveling the lipid catabolic pathway and gaining insights into the sequence-structural features of its primary functioning enzyme, Triacylglycerol lipase, will impart valuable information to target microalgae for augmented lipid content. In the present study, a genome-wide comparative study on putative Triacylglycerol lipase (TAGL) enzyme from algal species belonging to varied phylogenetic lineages was performed. The comprehensive sequence analysis revealed that TAGL comprises of three distinct conserved domains, such as, Patatin, Class III Lipase, and Abhydro_lipase, and also confirmed the ubiquitous presence of GXSXG motif in the sequences analyzed. In the absence of a crystal structure of algal TAGL till date, we developed the first 3D model of patatin domain of TAGL from an oleaginous microalga, Phaedactylum tricornutum, employing homology modeling, docking and molecular dynamic simulations methods. The domain-substrate complex having the low-ranking docking score revealed the binding of palmitic acid to the TAGL patatin domain surface with strong hydrogen bond interactions. The simulation results implied that the substrate-complexed patatin domain and the free enzyme adopted a more stable conformation after 40 ns. This is the first ever attempt to provide in-silico insights into the structural and dynamical insights on catalytic mechanism of the TAGL patatin domain. Subsequently, these findings aided our understanding on their structural stability, folding mechanism and protein-substrate interactions, which could be further utilized to design site-specific mutagenic experiments for engineering microalgal strains.Communicated by Ramaswamy H. Sarma.

Next-Generation Bioinformatics Approaches and Resources for Coronavirus Vaccine Discovery and Development-A Perspective Review.

COVID-19 is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To fight this pandemic, which has caused a massive death toll around the globe, researchers are putting efforts into developing an effective vaccine against the pathogen. As genome sequencing projects for several coronavirus strains have been completed, a detailed investigation of the functions of the proteins and their 3D structures has gained increasing attention. These high throughput data are a valuable resource for accelerating the emerging field of immuno-informatics, which is primarily aimed toward the identification of potential antigenic epitopes in viral proteins that can be targeted for the development of a vaccine construct eliciting a high immune response. Bioinformatics platforms and various computational tools and databases are also essential for the identification of promising vaccine targets making the best use of genomic resources, for further experimental validation. The present review focuses on the various stages of the vaccine development process and the vaccines available for COVID-19. Additionally, recent advances in genomic platforms and publicly available bioinformatics resources in coronavirus vaccine discovery together with related immunoinformatics databases and advances in technology are discussed.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery.

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 November 25, 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.

DBCOVP: A database of coronavirus virulent glycoproteins.

Since the emergence of SARS-CoV-1 (2002), novel coronaviruses have emerged periodically like the MERS- CoV (2012) and now, the SARS-CoV-2 outbreak which has posed a global threat to public health. Although, this is the third zoonotic coronavirus breakout within the last two decades, there are only a few platforms that provide information about coronavirus genomes. None of them is specific for the virulence glycoproteins and complete sequence-structural features of these virulence factors across the betacoronavirus family including SARS-CoV-2 strains are lacking. Against this backdrop, we present DBCOVP (http://covp.immt.res.in/), the first manually-curated, web-based resource to provide extensive information on the complete repertoire of structural virulent glycoproteins from coronavirus genomes belonging to betacoronavirus genera. The database provides various sequence-structural properties in which users can browse and analyze information in different ways. Furthermore, many conserved T-cell and B-cell epitopes predicted for each protein are present that may perform a significant role in eliciting the humoral and cellular immune response. The tertiary structure of the epitopes together with the docked epitope-HLA binding-complex is made available to facilitate further analysis. DBCOVP presents an easy-to-use interface with in-built tools for similarity search, cross-genome comparison, phylogenetic, and multiple sequence alignment. DBCOVP will certainly be an important resource for experimental biologists engaged in coronavirus research studies and will aid in vaccine development.

Designing an efficient multi-epitope vaccine displaying interactions with diverse HLA molecules for an efficient humoral and cellular immune response to prevent COVID-19 infection.

BACKGROUND: The novel SARS-CoV-2 coronavirus, the causative agent of the ongoing pandemic COVID-19 disease continues to infect people globally and has infected millions of humans worldwide. However, no effective vaccine against this virus exists. METHOD: Using Immunoinformatics, epitopic sequences from multiple glycoproteins that play crucial role in pathogenesis were identified. Particularly, epitopes were mapped from conserved receptor-binding domain of spike protein which have been experimentally validated in SARS-CoV-1 as a promising target for vaccine development. RESULTS: A multi-epitopic vaccine construct comprising of B-cell, CTL, HTL epitopes was developed along with fusion of adjuvant and linkers. The epitopes identified herein are reported for the first time and were predicted to be highly antigenic, stable, nonallergen, nontoxic and displayed conservation across several SARS-CoV-2 isolates from different countries. Additionally, the epitopes associated with maximum HLA alleles and population coverage analysis shows the proposed epitopes would be a relevant representative of large proportion of the world population. A reliable three-dimensional structure of the vaccine construct was developed. Consequently, docking and molecular-dynamics simulation ensured the stable interaction between vaccine and innate-immune receptor.

dEMBF v2.0: An Updated Database of Enzymes for Microalgal Biofuel Feedstock.

In light of increasing algal genomics data and knowledge of biosynthetic pathways responsible for biofuel production, an integrated resource for easy access to all information is essential to improve our understanding of algal lipid metabolism. Against this backdrop, dEMBF v2.0, a significantly updated and improved version of our database of microalgae lipid biosynthetic enzymes for biofuel production, has been developed. dEMBF v2.0 now contains a comprehensive annotation of 2018 sequences encoding 35 enzymes, an increase of over 7-fold as compared with the first version. Other improved features include an increase in species coverage to 32 algal genomes, analysis of additional metabolic pathways, expanded annotation thoroughly detailing sequence and structural features, including enzyme-ligand interactions, and integration of supporting experimental evidence to demonstrate the role of enzymes in increasing lipid content. Along with a complete redesign of the interface, the updated database provides several inbuilt tools and user-friendly functionalities for more interactive and dynamic visualization of data.