Functional Prediction of Biological Profile During Eutrophication in Marine Environment

In the marine environment, coastal nutrient pollution and algal blooms are increasing in many coral reefs and surface waters around the world, leading to higher concentrations of dissolved organic carbon (DOC), nitrogen (N), phosphate (P), and sulfur (S) compounds. The adaptation of the marine microbiota to this stress involves evolutionary processes through mutations that can provide selective phenotypes. The aim of this in silico analysis is to elucidate the potential candidate hub proteins, biological processes, and key metabolic pathways involved in the pathogenicity of bacterioplankton during excess of nutrients. The analysis was carried out on the model organism Escherichia coli K-12, by adopting an analysis pipeline consisting of a set of packages from the Cystoscape platform. The results obtained show that the metabolism of carbon and sugars generally are the 2 driving mechanisms for the expression of virulence factors.

The Vulnerability of the Developing Brain: Analysis of Highly Expressed Genes in Infant C57BL/6 Mouse Hippocampus in Relation to Phenotypic Annotation Derived From Mutational Studies

The hippocampus has been shown to have a major role in learning and memory, but also to participate in the regulation of emotions. However, its specific role(s) in memory is still unclear. Hippocampal damage or dysfunction mainly results in memory issues, especially in the declarative memory but, in animal studies, has also shown to lead to hyperactivity and difficulty in inhibiting responses previously taught. The brain structure is affected in neuropathological disorders, such as Alzheimer’s, epilepsy, and schizophrenia, and also by depression and stress. The hippocampus structure is far from mature at birth and undergoes substantial development throughout infant and juvenile life. The aim of this study was to survey genes highly expressed throughout the postnatal period in mouse hippocampus and which have also been linked to an abnormal phenotype through mutational studies to achieve a greater understanding about hippocampal functions during postnatal development. Publicly available gene expression data from C57BL/6 mouse hippocampus was analyzed; from a total of 5 time points (at postnatal day 1, 10, 15, 21, and 30), 547 genes highly expressed in all of these time points were selected for analysis. Highly expressed genes are considered to be of potential biological importance and appear to be multifunctional, and hence any dysfunction in such a gene will most likely have a large impact on the development of abilities during the postnatal and juvenile period. Phenotypic annotation data downloaded from Mouse Genomic Informatics database were analyzed for these genes, and the results showed that many of them are important for proper embryo development and infant survival, proper growth, and increase in body size, as well as for voluntary movement functions, motor coordination, and balance. The results also indicated an association with seizures that have primarily been characterized by uncontrolled motor activity and the development of proper grooming abilities. The complete list of genes and their phenotypic annotation data have been compiled in a file for easy access.

Structure and Function of Major SARS-CoV-2 and SARS-CoV Proteins

SARS-CoV-2 virus, the causative agent of COVID-19 pandemic, has a genomic organization consisting of 16 nonstructural proteins (nsps), 4 structural proteins, and 9 accessory proteins. Relative of SARS-CoV-2, SARS-CoV, has genomic organization, which is very similar. In this article, the function and structure of the proteins of SARS-CoV-2 and SARS-CoV are described in great detail. The nsps are expressed as a single or two polyproteins, which are then cleaved into individual proteins using two proteases of the virus, a chymotrypsin-like protease and a papain-like protease. The released proteins serve as centers of virus replication and transcription. Some of these nsps modulate the host’s translation and immune systems, while others help the virus evade the host immune system. Some of the nsps help form replication-transcription complex at double-membrane vesicles. Others, including one RNA-dependent RNA polymerase and one exonuclease, help in the polymerization of newly synthesized RNA of the virus and help minimize the mutation rate by proofreading. After synthesis of the viral RNA, it gets capped. The capping consists of adding GMP and a methylation mark, called cap 0 and additionally adding a methyl group to the terminal ribose called cap1. Capping is accomplished with the help of a helicase, which also helps remove a phosphate, two methyltransferases, and a scaffolding factor. Among the structural proteins, S protein forms the receptor of the virus, which latches on the angiotensin-converting enzyme 2 receptor of the host and N protein binds and protects the genomic RNA of the virus. The accessory proteins found in these viruses are small proteins with immune modulatory roles. Besides functions of these proteins, solved X-ray and cryogenic electron microscopy structures related to the function of the proteins along with comparisons to other coronavirus homologs have been described in the article. Finally, the rate of mutation of SARS-CoV-2 residues of the proteome during the 2020 pandemic has been described. Some proteins are mutated more often than other proteins, but the significance of these mutation rates is not fully understood.

The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

In Silico Identification and Functional Characterization of Conserved miRNAs in the Genome of Cryptosporidium parvum

Cryptosporidium parvum, a predominant causal agent of a fatal zoonotic protozoan diarrhoeal disease called cryptosporidiosis, bears a worldwide public health concern for childhood mortality and poses a key threat to the dairy and water industries. MicroRNAs (miRNAs), small but powerful posttranscriptional gene silencing RNA molecules, regulate a variety of molecular, biological, and cellular processes in animals and plants. As to the present date, there is a paucity of information regarding miRNAs of C. parvum; hence, this study was used to identify miRNAs in the organism using a comprehensible expressed sequence tag–based homology search approach consisting of a series of computational screening process from the identification of putative miRNA candidates to the functional annotation of the important gene targets in C. parvum. The results revealed a conserved miRNA that targeted 487 genes in the model organism ( Drosophila melanogaster) and 85 genes in C. parvum, of which 11 genes had direct involvements in several crucial virulence factors such as environmental oocyst protection, excystation, locomotion, adhesion, invasion, stress protection, intracellular growth, and survival. Besides, 20 genes showed their association with various major pathways dedicated for the ribosomal biosynthesis, DNA repair, transportation, protein production, gene expression, cell cycle, cell proliferation, development, immune response, differentiation, and nutrient metabolism of the organism in the host. Thus, this study provides a strong evidence of great impact of identified miRNA on the biology, virulence, and pathogenesis of C. parvum. Furthermore, the study suggests that the detected miRNA could be a potential epigenomic tool for controlling the protozoon through silencing those virulent and pathway-related target genes.

In Silico Exploration of Phytoconstituents From Phyllanthus emblica and Aegle marmelos as Potential Therapeutics Against SARS-CoV-2 RdRp

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide has increased the importance of computational tools to design a drug or vaccine in reduced time with minimum risk. Earlier studies have emphasized the important role of RNA-dependent RNA polymerase (RdRp) in SARS-CoV-2 replication as a potential drug target. In our study, comprehensive computational approaches were applied to identify potential compounds targeting RdRp of SARS-CoV-2. To study the binding affinity and stability of the phytocompounds from Phyllanthus emblica and Aegel marmelos within the defined binding site of SARS-CoV-2 RdRp, they were subjected to molecular docking, 100 ns molecular dynamics (MD) simulation followed by post-simulation analysis. Furthermore, to assess the importance of features involved in the strong binding affinity, molecular field-based similarity analysis was performed. Based on comparative molecular docking and simulation studies of the selected phytocompounds with SARS-CoV-2 RdRp revealed that EBDGp possesses a stronger binding affinity (−23.32 kcal/mol) and stability than other phytocompounds and reference compound, Remdesivir (−19.36 kcal/mol). Molecular field-based similarity profiling has supported our study in the validation of the importance of the presence of hydroxyl groups in EBDGp, involved in increasing its binding affinity toward SARS-CoV-2 RdRp. Molecular docking and dynamic simulation results confirmed that EBDGp has better inhibitory potential than Remdesivir and can be an effective novel drug for SARS-CoV-2 RdRp. Furthermore, binding free energy calculations confirmed the higher stability of the SARS-CoV-2 RdRp-EBDGp complex. These results suggest that the EBDGp compound may emerge as a promising drug against SARS-CoV-2 and hence requires further experimental validation.

Prediction of Anti-COVID 19 Therapeutic Power of Medicinal Moroccan Plants Using Molecular Docking

The emerging pathogen SARS-CoV2 causing coronavirus disease 2019 (COVID-19) is a global public health challenge. To the present day, COVID-19 had affected more than 40 million people worldwide. The exploration and the development of new bioactive compounds with cost-effective and specific anti-COVID 19 therapeutic power is the prime focus of the current medical research. Thus, the exploitation of the molecular docking technique has become essential in the discovery and development of new drugs, to better understand drug-target interactions in their original environment. This work consists of studying the binding affinity and the type of interactions, through molecular docking, between 54 compounds from Moroccan medicinal plants, dextran sulfate and heparin (compounds not derived from medicinal plants), and 3CLpro-SARS-CoV-2, ACE2, and the post fusion core of 2019-nCoV S2 subunit. The PDB files of the target proteins and prepared herbal compounds (ligands) were subjected for docking to AutoDock Vina using UCSF Chimera, which provides a list of potential complexes based on the criteria of form complementarity of the natural compound with their binding affinities. The results of molecular docking revealed that Taxol, Rutin, Genkwanine, and Luteolin-glucoside have a high affinity with ACE2 and 3CLpro. Therefore, these natural compounds can have 2 effects at once, inhibiting 3CLpro and preventing recognition between the virus and ACE2. These compounds may have a potential therapeutic effect against SARS-CoV2, and therefore natural anti-COVID-19 compounds.

Significance of African Diets in Biotherapeutic Modulation of the Gut Microbiome

Diet plays an essential role in human development and growth, contributing to health and well-being. The socio-economic values, cultural perspectives, and dietary formulation in sub-Saharan Africa can influence gut health and disease prevention. The vast microbial ecosystems in the human gut frequently interrelate to maintain a healthy, well-coordinated cellular and humoral immune signalling to prevent metabolic dysfunction, pathogen dominance, and induction of systemic diseases. The diverse indigenous diets could differentially act as biotherapeutics to modulate microbial abundance and population characteristics. Such modulation could prevent stunted growth, malnutrition, induction of bowel diseases, attenuated immune responses, and mortality, particularly among infants. Understanding the associations between specific indigenous African diets and the predictability of the dynamics of gut bacteria genera promises potential biotherapeutics towards improving the prevention, control, and treatment of microbiome-associated diseases such as cancer, inflammatory bowel disease, obesity, type 2 diabetes, and cardiovascular disease. The dietary influence of many African diets (especially grain-base such as millet, maize, brown rice, sorghum, soya, and tapioca) promotes gut lining integrity, immune tolerance towards the microbiota, and its associated immune and inflammatory responses. A fibre-rich diet is a promising biotherapeutic candidate that could effectively modulate inflammatory mediators’ expression associated with immune cell migration, lymphoid tissue maturation, and signalling pathways. It could also modulate the stimulation of cytokines and chemokines involved in ensuring balance for long-term microbiome programming. The interplay between host and gut microbial digestion is complex; microbes using and competing for dietary and endogenous proteins are often attributable to variances in the comparative abundances of Enterobacteriaceae taxa. Many auto-inducers could initiate the process of quorum sensing and mammalian epinephrine host cell signalling system. It could also downregulate inflammatory signals with microbiota tumour taxa that could trigger colorectal cancer initiation, metabolic type 2 diabetes, and inflammatory bowel diseases. The exploitation of essential biotherapeutic molecules derived from fibre-rich indigenous diet promises food substances for the downregulation of inflammatory signalling that could be harmful to gut microbiota ecological balance and improved immune response modulation.

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

Reconstructing Draft Genomes Using Genome Resolved Metagenomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India

Rabindra Sarovar lake is an artificial freshwater lake in the arsenic infested eastern region of India. In this study, using the genome resolved metagenomics approach; we have deciphered the taxonomic diversity as well as the functional insights of the gene pools specific to this region. Initially, a total of 113 Metagenome Assembled Genomes (MAGs) were recovered from the two predominant seasons, that is, rainy (n = 50) and winter (n = 63). After bin refinement and de-replication, 27 MAGs (18 from Winter season and 9 from Rainy season) were reconstructed. These MAGs were either of high-quality (n = 10) or of medium quality (n = 17) that was determined based on genome completeness and contamination. These 27 MAGs spanning across 6 bacterial phyla and the most predominant ones were Proteobacteria, Bacteroidetes, and Cyanobacteria regardless of the season. Functional annotation across the MAGs suggested the existence of all known types of arsenic resistance and metabolism genes. Besides, important secondary metabolites such as zoocin_A, prochlorosin, and microcin were also abundantly present in these genomes. The metagenomic study of this lake provides the first insights into the microbiome composition and functional classification of the gene pools in two predominant seasons. The presence of arsenic metabolism and resistance genes in the recovered genomes is a sign of adaptation of the microbes to the arsenic contamination in this region. The presence of secondary metabolite genes in the lake microbiome has several implications including the potential use of these for the pharmaceutical industry.