DOR - a Database of Olfactory Receptors - Integrated Repository for Sequence and Secondary Structural Information of Olfactory Receptors in Selected Eukaryotic Genomes

iORbase: A database for the prediction of the structures and functions of insect olfactory receptors

Insect olfactory receptors (iORs) with atypical 7-transmembrane domains, unlike Chordata olfactory receptors, are not in the GPCR protein family. iORs selectively bind to volatile ligands in the environment and affect essential insect behaviors. In this study, we constructed a new platform (iORbase, https://www.iorbase.com) for the structural and functional analysis of iORs based on a combined algorithm for gene annotation and protein structure prediction. Moreover, it provides the option to calculate the binding affinities and binding residues between iORs and pheromone molecules by virtual screening of docking. Furthermore, iORbase supports the automatic structural and functional prediction of user-submitted iORs or pheromones. iORbase contains the well-analyzed results of approximately 6 000 iORs and their 3D protein structures identified from 59 insect species and 2 077 insect pheromones from the literature, as well as approximately 12 million pairs of simulated interactions between functional iORs and pheromones. We also built 4 online modules, iORPDB, iInteraction, iModelTM, and iOdorTool to easily retrieve and visualize the 3D structures and interactions. iORbase can help greatly improve the experimental efficiency and success rate, identify new insecticide targets, or develop electronic nose technology. This study will shed light on the olfactory recognition mechanism and evolutionary characteristics from the perspectives of omics and macroevolution.

A two-stage computational approach to predict novel ligands for a chemosensory receptor

Olfactory receptor (OR) 1A2 is the member of largest superfamily of G protein-coupled receptors (GPCRs). OR1A2 is an ectopically expressed receptor with only 13 known ligands, implicated in reducing hepatocellular carcinoma progression, with enormous therapeutic potential. We have developed a two-stage screening approach to identify novel putative ligands of OR1A2. We first used a pharmacophore model based on atomic property field (APF) to virtually screen a library of 5942 human metabolites. We then carried out structure-based virtual screening (SBVS) for predicting the potential agonists, based on a 3D homology model of OR1A2. This model was developed using a biophysical approach for template selection, based on multiple parameters including hydrophobicity correspondence, applied to the complete set of available GPCR structures to pick the most appropriate template. Finally, the membrane-embedded 3D model was refined by molecular dynamics (MD) simulations in both the apo and holo forms. The refined model in the apo form was selected for SBVS. Four novel small molecules were identified as strong binders to this olfactory receptor on the basis of computed binding energies.

Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.

InsectOR-Webserver for sensitive identification of insect olfactory receptor genes from non-model genomes

Insect Olfactory Receptors (ORs) are diverse family of membrane protein receptors responsible for most of the insect olfactory perception and communication, and hence they are of utmost importance for developing repellents or pesticides. Accurate gene prediction of insect ORs from newly sequenced genomes is an important but challenging task. We have developed a dedicated webserver, 'insectOR', to predict and validate insect OR genes using multiple gene prediction algorithms, accompanied by relevant validations. It is possible to employ this server nearly automatically and perform rapid prediction of the OR gene loci from thousands of OR-protein-to-genome alignments, resolve gene boundaries for tandem OR genes and refine them further to provide more complete OR gene models. InsectOR outperformed the popular genome annotation pipelines (MAKER and NCBI eukaryotic genome annotation) in terms of overall sensitivity at base, exon and locus level, when tested on two distantly related insect genomes. It displayed more than 95% nucleotide level precision in both tests. Finally, given the same input data and parameters, InsectOR missed less than 2% gene loci, in contrast to 55% loci missed by MAKER for Drosophila melanogaster. The webserver is freely available on the web at http://caps.ncbs.res.in/insectOR/ and the basic package can be downloaded from https://github.com/sdk15/insectOR for local use. This tool will allow biologists to perform quick preliminary identification of insect olfactory receptor genes from newly sequenced genomes and also assist in their further detailed annotation. Its usage can also be extended to other divergent gene families.

Sequence-Based Prediction of Olfactory Receptor Responses

Computational prediction of how strongly an olfactory receptor (OR) responds to various odors can help in bridging the widening gap between the large number of receptors that have been sequenced and the small number of experiments measuring their responses. Previous efforts in this area have predicted the responses of a receptor to some odors, using the known responses of the same receptor to other odors. Here, we present a method to predict the responses of a receptor without any known responses by using available data about the responses of other conspecific receptors and their sequences. We applied this method to ORs in insects Drosophila melanogaster (both adult and larva) and Anopheles gambiae and to mouse and human ORs. We found the predictions to be in significant agreement with the experimental measurements. The method also provides clues about the response-determining positions within the receptor sequences.

Topology prediction of insect olfactory receptors

Olfactory receptors are important transmembrane proteins that enable organisms to perceive odours and react to them. Structural understanding of insect olfactory receptors is scarce. In this review, we discuss different transmembrane helix prediction methods, consensus methods, topology prediction methods which can enable topology prediction of these proteins. We discuss the current success rates by applying the algorithms on few G-protein coupled receptors of known structure and olfactory receptor sequences and outstanding challenges. Finally, we discuss the impact of topology prediction on biology and modeling of ORs.

U, Kulkarni R, Löfstedt C, Sowdhamini R, Olsson SB. A Functional Agonist of Insect Olfactory Receptors: Behavior, Physiology and Structure

Chemical signaling is ubiquitous and employs a variety of receptor types to detect the cacophony of molecules relevant for each living organism. Insects, our most diverse taxon, have evolved unique olfactory receptors with as little as 10% sequence identity between receptor types. We have identified a promiscuous volatile, 2-methyltetrahydro-3-furanone (coffee furanone), that elicits chemosensory and behavioral activity across multiple insect orders and receptors. In vivo and in vitro physiology showed that coffee furanone was detected by roughly 80% of the recorded neurons expressing the insect-specific olfactory receptor complex in the antenna of Drosophila melanogaster, at concentrations similar to other known, and less promiscuous, ligands. Neurons expressing specialized receptors, other chemoreceptor types, or mutants lacking the complex entirely did not respond to this compound. This indicates that coffee furanone is a promiscuous ligand for the insect olfactory receptor complex itself and did not induce non-specific cellular responses. In addition, we present homology modeling and docking studies with selected olfactory receptors that suggest conserved interaction regions for both coffee furanone and known ligands. Apart from its physiological activity, this known food additive elicits a behavioral response for several insects, including mosquitoes, flies, and cockroaches. A broad-scale behaviorally active molecule non-toxic to humans thus has significant implications for health and agriculture. Coffee furanone serves as a unique tool to unlock molecular, physiological, and behavioral relationships across this diverse receptor family and animal taxa.

Computational genome-wide survey of odorant receptors from two solitary bees Dufourea novaeangliae (Hymenoptera: Halictidae) and Habropoda laboriosa (Hymenoptera: Apidae)

Olfactory/odorant receptors (ORs) probably govern eusocial behaviour in honey bees through detection of cuticular hydrocarbons (CHCs) and queen mandibular gland pheromones (QMP). CHCs are involved in nest-mate recognition whereas QMP acts as sex pheromone for drones and as retinue pheromone for female workers. Further studies on the effect of eusociality on the evolution of ORs are hindered by the non-availability of comprehensive OR sets of solitary species. We report complete OR repertoires from two solitary bees Dufourea novaeangliae (112 ORs) and Habropoda laboriosa (151 ORs). We classify these ORs into 34 phylogenetic clades/subfamilies. Differences in the OR sets of solitary and eusocial bees are observed in individual subfamilies like subfamily 9-exon (putative CHC receptors) and L (contains putative QMP receptor group). A subfamily (H) including putative floral scent receptors is expanded in the generalist honey bees only, but not in the specialists. On the contrary, subfamily J is expanded in all bees irrespective of their degree of social complexity or food preferences. Finally, we show species-lineage specific and OR-subfamily specific differences in the putative cis-regulatory DNA motifs of the ORs from six hymenopteran species. Out of these, [A/G]CGCAAGCG[C/T] is a candidate master transcription factor binding site for multiple olfactory genes.

Olfactory coding from the periphery to higher brain centers in the Drosophila brain

BACKGROUND

Odor information is processed through multiple receptor-glomerular channels in the first order olfactory center, the antennal lobe (AL), then reformatted into higher brain centers and eventually perceived by the fly. To reveal the logic of olfaction, it is fundamental to map odor representations from the glomerular channels into higher brain centers.

RESULTS

We characterize odor response profiles of AL projection neurons (PNs) originating from 31 glomeruli using whole cell patch-clamp recordings in Drosophila melanogaster. We reveal that odor representation from olfactory sensory neurons to PNs is generally conserved, while transformation of odor tuning curves is glomerulus-dependent. Reconstructions of PNs reveal that attractive and aversive odors are represented in different clusters of glomeruli in the AL. These separate representations are preserved into higher brain centers, where attractive and aversive odors are segregated into two regions in the lateral horn and partly separated in the mushroom body calyx.

CONCLUSIONS

Our study reveals spatial representation of odor valence coding from the AL to higher brain centers. These results provide a global picture of the olfactory circuit design underlying innate odor-guided behavior.

Structural elucidation of estrus urinary lipocalin protein (EULP) and evaluating binding affinity with pheromones using molecular docking and fluorescence study

Transportation of pheromones bound with carrier proteins belonging to lipocalin superfamily is known to prolong chemo-signal communication between individuals belonging to the same species. Members of lipocalin family (MLF) proteins have three structurally conserved motifs for delivery of hydrophobic molecules to the specific recognizer. However, computational analyses are critically required to validate and emphasize the sequence and structural annotation of MLF. This study focused to elucidate the evolution, structural documentation, stability and binding efficiency of estrus urinary lipocalin protein (EULP) with endogenous pheromones adopting in-silico and fluorescence study. The results revealed that: (i) EULP perhaps originated from fatty acid binding protein (FABP) revealed in evolutionary analysis; (ii) Dynamic simulation study shows that EULP is highly stable at below 0.45 Å of root mean square deviation (RMSD); (iii) Docking evaluation shows that EULP has higher binding energy with farnesol and 2-iso-butyl-3-methoxypyrazine (IBMP) than 2-naphthol; and (iv) Competitive binding and quenching assay revealed that purified EULP has good binding interaction with farnesol. Both, In-silico and experimental studies showed that EULP is an efficient binding partner to pheromones. The present study provides impetus to create a point mutation for increasing longevity of EULP to develop pheromone trap for rodent pest management.

Identification of Complete Repertoire of Apis florea Odorant Receptors Reveals Complex Orthologous Relationships with Apis mellifera

We developed a computational pipeline for homology based identification of the complete repertoire of olfactory receptor (OR) genes in the Asian honey bee species, Apis florea. Apis florea is phylogenetically the most basal honey bee species and also the most distant sister species to the Western honey bee Apis mellifera, for which all OR genes had been identified before. Using our pipeline, we identified 180 OR genes in A. florea, which is very similar to the number of ORs identified in A. mellifera (177 ORs). Many characteristics of the ORs including gene structure, synteny of tandemly repeated ORs and basic phylogenetic clustering are highly conserved. The composite phylogenetic tree of A. florea and A. mellifera ORs could be divided into 21 clades which are in harmony with the existing Hymenopteran tree. However, we found a few nonorthologous OR relationships between both species as well as independent pseudogenization of ORs suggesting separate evolutionary changes. Particularly, a subgroup of the OR gene clade XI, which had been hypothesized to code cuticular hydrocarbon receptors showed a high number of species-specific ORs. RNAseq analysis detected a total number of 145 OR transcripts in male and 162 in female antennae. Most of the OR genes were highly expressed on the female antennae. However, we detected five distinct male-biased OR genes, out of which three genes (AfOr11, AfOr18, AfOr170P) were shown to be male-biased in A. mellifera, too, thus corroborating a behavioral function in sex-pheromone communication.

MPDB: Molecular Pathways Brain Database

Molecular Pathways Brain Database (MPDB), is a novel database for molecular information of the brain pathways and is an initiative to provide an organized platform for researchers in the field of neuro-informatics. The database currently has information from 1850 molecules for three different sensory pathways namely olfactory transduction, photo transduction and long-term potentiation. The usefulness of the database is demonstrated by an analysis of the olfactory transduction pathway which helps understand their olfactory specifity and further indicates that some of the molecules have evolved independently among these organisms as per the need of time and function. The database is available for free at http://pranag.physics.iisc.ernet.in/mpdb/.

Computational Approaches for Decoding Select Odorant-Olfactory Receptor Interactions Using Mini-Virtual Screening

Olfactory receptors (ORs) belong to the class A G-Protein Coupled Receptor superfamily of proteins. Unlike G-Protein Coupled Receptors, ORs exhibit a combinatorial response to odors/ligands. ORs display an affinity towards a range of odor molecules rather than binding to a specific set of ligands and conversely a single odorant molecule may bind to a number of olfactory receptors with varying affinities. The diversity in odor recognition is linked to the highly variable transmembrane domains of these receptors. The purpose of this study is to decode the odor-olfactory receptor interactions using in silico docking studies. In this study, a ligand (odor molecules) dataset of 125 molecules was used to carry out in silico docking using the GLIDE docking tool (SCHRODINGER Inc Pvt LTD). Previous studies, with smaller datasets of ligands, have shown that orthologous olfactory receptors respond to similarly-tuned ligands, but are dramatically different in their efficacy and potency. Ligand docking results were applied on homologous pairs (with varying sequence identity) of ORs from human and mouse genomes and ligand binding residues and the ligand profile differed among such related olfactory receptor sequences. This study revealed that homologous sequences with high sequence identity need not bind to the same/ similar ligand with a given affinity. A ligand profile has been obtained for each of the 20 receptors in this analysis which will be useful for expression and mutation studies on these receptors.