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Odorant-binding proteins (OBPs) in insects bind to volatile chemical cues that are important in regulating insect behavior. It is hypothesized that OBPs bind with specificity to certain volatiles and may help in transport and delivery to odorant receptors (ORs), and may help in buffering the olfactory response and aid the insect in various behaviors. Honeybees are eusocial insects that perceive olfactory cues and strongly rely on them to perform complex olfactory behaviors. Here, we have identified and annotated odorant-binding proteins and few chemosensory proteins from the genome of the dwarf honey bee, Apis florea, using an exhaustive homology-based bioinformatic pipeline and analyzed the evolutionary relationships between the OBP subfamilies. Our study confirms that the Minus-C subfamily in honey bees has diverged from the Classic subfamily of odorant-binding proteins.

Odorant binding proteins (OBPs) in insects bind to volatile chemical cue and help in their binding to odorant receptors. The odor coding hypothesis states that OBPs may bind with specificity to certain volatiles and aid the insect in various behaviours. Honeybees are eusocial insects with complex behaviour that requires olfactory inputs. Here, we have identified and annotated odorant binding proteins from the genome of the dwarf honey bee, Apis florea using an exhaustive homology-based bioinformatic pipeline and analyzed the evolutionary relationships between the OBP subfamilies. Our study suggests that Minus-C subfamily may have diverged from the Classic subfamily of odorant binding proteins in insects.

With the advent of Next Generation Sequencing, many population specific whole genome sequences published thus far, predominantly represent individuals of European ancestry. While sequencing efforts of underrepresented communities in genomes datasets, like the Yoruba West-African, Han Chinese, Tibetan, South Korean, Egyptian and Japanese have recently added to the public genomic repositories, a comprehensive understanding of human genomic diversity and discovery of trait-associated variants necessitates the need for additional population specific analysis. In this context, the genomics of the population from the Indian sub-continent, given its genetic heterogeneity needs further elucidation.In this context, the endogamous Zoroastrian-Parsi community of India, offer an exceptional insight into a homogenous population that has culturally, socially, and genetically remained intact, for 13 centuries amidst the genomic, social and cultural Indian landscape, consequent to their migration from the ancient Persian plateau.Notwithstanding longevity as a trait, this endangered community is highly susceptible to cancers, rare genetic disorders, and display a documented high incidence of neurodegenerative and autoimmune conditions. The community as a matter of cultural practice abstains from smoking.Here, we describe the assembly and annotation of the genome of an adult female, Zoroastrian-Parsi individual sequenced at a high depth of 173X using a combination of short Illumina reads (160X) and long nanopore reads (13X). Using a combination of hybrid assemblers, we created a new, population-specific human reference genome, The Zoroastrian-Parsi Genome Reference Female, AGENOME-ZPGRF, contains 2,778,216,114 nucleotides as compared to 3,096,649,726 in GRCh38 constituting 93.235% of the total genomic fraction. Annotation identified 20833 genomic features, of which 14996 are almost identical to their counterparts on GRCh38 while 5837 genomic features were covered in partial. AGENOME-ZPGRF contained 5,426,310 variants of which the majority were SNP’s (4,291,601) and 960,867 SNPs were AGENOME-ZPGRF specific personal variants not listed in dbSNP.We present, AGENOME-ZPGRF as a whole reference for any genetic studies involving Zoroastrian-Parsi individuals extending their application to identify disease relevant prognostic biomarkers and variants in global population genomics studies.

Reversible epigenetic changes within the loci of genes that regulate critical cell processes have recently emerged as important biomarkers of disease pathology. It is then natural to consider the consequences for population health risk of such epigenetic changes during the aging process. Specifically, the interplay between dynamic methylation changes that accompany aging and mutations that accrue in an individual’s genome over time need further investigation. The current study investigated the role of dynamic methylation acting together with gene variants in an individual over time to gain insight into the evolving epigenome–genome interplay that affects biochemical pathways controlling physiological processes during aging.We completed a whole-genome methylation and variant analysis in a non-smoking Zoroastrian-Parsi individual, collecting two samples, 12 years apart (at 53 and 65 years respectively) (ZPMetG-Hv2a-1A (old, t0), ZPMetG-Hv2a-1B (recent, t0+12)) and analysing them using a GridION Nanopore sequencer at 13X genome coverage overall. We further identified the single nucleotide variants (SNVs) and indels in known CpG islands by employing the Genome Analysis Tool Kit (GATK) and MuTect2 variant-caller pipeline with the GRCh37 (patch 13) human genome as reference.We found 5258 disease-relevant genes that had been differentially methylated in this individual over 12 years. Employing the GATK pipeline, we found 24,948 genes, corresponding to 4,58,148 variants, specific to ZPMetG-Hv2a-1B, indicating the presence of variants that had accrued over time. A fraction of the gene variants (242/24948) occurred within the CpG regions that were differentially methylated, with 67/247 exactly coincident with a CpG site. Our analysis yielded a critical cluster of 10 genes that were each significantly methylated and had variants at the CpG site or the ±4 bp CpG region window. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment network analysis as well as Reactome and STRING analysis of gene-specific variants indicated an impact on biological processes regulating the immune system, disease networks implicated in cancer and neurodegenerative diseases, and transcriptional control of processes regulating cellular senescence and longevity. Additional analysis of mutational signatures indicated a majority of C>T transitions followed by T>C transitions in the more recent sample, ZPMetG-Hv2a-1B.Our current study provides additional insight into the aging methylome over time and the interplay between different methylation and gene variants in the etiology of disease.

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.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and has wide protein applications. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. In order to perform such analyses, it requires intense computational power and it becomes impossible to cover the entire chemical space of small molecules. It has been aided by a shift towards using Machine Learning-based methodologies that aids in binding prediction using regression. Recent developments using deep learning has enabled us to make sense of massive amounts of complex datasets. Herein, 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 that find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies towards a diverse set of ligands. The models were trained and validated using a detailed methodology for feature extraction. We have also tested DEELIG on protein complexes relevant to the current public health scenario. Our approach to network construction and training on protein-ligand dataset prepared in-house has provided significantly better results than previously existing methods in the field.

The olfactory system is capable of detecting and distinguishing thousands of environmental odorants that play a key role in reproduction, social behaviours including pheromones influenced classical events. Membrane secretary odorant binding proteins (OBPs) are soluble lipocalins, localized in the nasal membrane of mammals. They bind and carry odorants within the nasal epithelium to putative olfactory transmembrane receptors (ORs). While the existence of OBPs and their significant functions are very well known in insects and laboratory mammals, there is little information about the species-specific OBPs in buffaloes. In fact, the OBP of nasal epithelium has not yet been exploited to develop a suitable technique to detect estrus which is being reported as a difficult task in buffalo. In the present study, using molecular biology and protein engineering approaches, we have cloned six novel OBP isoforms from buffalo nasal epithelium (bnOBPs). Furthermore, 3D model was developed and molecular-docking, dynamics experiments were performed by In-silico approach. In particular, we found four residues (Phe104, Phe134, Phe69 and Asn118) from OBP1a, which had strong binding affinities towards two sex pheromones, specifically oleic acid and p-cresol. We expressed this protein in Escherichia coli to examine its involvement in the sex pheromone perception from female buffalo urine and validated through fluorescence quenching studies. Interestingly, fluorescence binding experiments also showed similar strong binding affinities of OBP1a to oleic acid and p-cresol. By using structural data, the binding specificity is also verified by site-directed mutagenesis of the four residues followed by in-vitro binding assays. Our results enable to better understand the functions of different nasal epithelium OBPs in buffaloes. They also lead to improved understanding of the interaction between olfactory proteins and odorants to develop highly selective biosensing devices for non-invasive detection of estrus in buffaloes.