Computational evaluation of interactions between olfactory receptor OR2W1 and its ligands

Mammalian olfactory receptors are a family of G protein‒coupled receptors (GPCRs) that occupy a large part of the genome. In human genes, olfactory receptors account for more than 40% of all GPCRs. Several types of GPCR structures have been identified, but there is no single olfactory receptor whose structure has been determined experimentally to date. The aim of this study was to model the interactions between an olfactory receptor and its ligands at the molecular level to provide hints on the binding modes between the OR2W1 olfactory receptor and its agonists and inverse agonists. The results demonstrated the modes of ligand binding in a three-dimensional model of OR2W1 and showed a statistically significant difference in binding affinity to the olfactory receptor between agonists and inverse agonists.

Implications of the simple chemical structure of the odorant molecules interacting with the olfactory receptor 1A1

G protein–coupled receptors (GPCRs), including olfactory receptors, account for the largest group of genes in the human genome and occupy a very important position in signaling systems. Although olfactory receptors, which belong to the broader category of GPCRs, play an important role in monitoring the organism’s surroundings, their actual three-dimensional structure has not yet been determined. Therefore, the specific details of the molecular interactions between the receptor and the ligand remain unclear. In this report, the interactions between human olfactory receptor 1A1 and its odorant molecules were simulated using computational methods, and we explored how the chemically simple odorant molecules activate the olfactory receptor.

Computational evaluation of interactions between olfactory receptor OR2W1 and its ligands

Mammalian olfactory receptors are a family of G protein‒coupled receptors (GPCRs) that occupy a large part of the genome. In human genes, olfactory receptors account for more than 40% of all GPCRs. Several types of GPCR structures have been identified, but there is no single olfactory receptor whose structure has been determined experimentally to date. The aim of this study was to model the interactions between an olfactory receptor and its ligands at the molecular level to provide hints on the binding modes between the OR2W1 olfactory receptor and its agonists and inverse agonists. The results demonstrated the modes of ligand binding in a three-dimensional model of OR2W1 and showed a statistically significant difference in binding affinity to the olfactory receptor between agonists and inverse agonists.

Implications of the simple chemical structure of the odorant molecules interacting with the olfactory receptor 1A1

G protein–coupled receptors (GPCRs), including olfactory receptors, account for the largest group of genes in the human genome and occupy a very important position in signaling systems. Although olfactory receptors, which belong to the broader category of GPCRs, play an important role in monitoring the organism’s surroundings, their actual three-dimensional structure has not yet been determined. Therefore, the specific details of the molecular interactions between the receptor and the ligand remain unclear. In this report, the interactions between human olfactory receptor 1A1 and its odorant molecules were simulated using computational methods, and we explored how the chemically simple odorant molecules activate the olfactory receptor.

System-Wide Expression and Function of Olfactory Receptors in Mammals

Olfactory receptors (ORs) in mammals are generally considered to function as chemosensors in the olfactory organs of animals. They are membrane proteins that traverse the cytoplasmic membrane seven times and work generally by coupling to heterotrimeric G protein. The OR is a G protein–coupled receptor that binds the guanine nucleotide-binding G(αolf) subunit and the Gβγ dimer to recognize a wide spectrum of organic compounds in accordance with its cognate ligand. Mammalian ORs were originally identified from the olfactory epithelium of rat. However, it has been recently reported that the expression of ORs is not limited to the olfactory organ. In recent decades, they have been found to be expressed in diverse organs or tissues and even tumors in mammals. In this review, the expression and expected function of olfactory receptors that exist throughout an organism's system are discussed.

ERRATUM: Figure Legend Correction. Characteristics in Molecular Vibrational Frequency Patterns between Agonists and Antagonists of Histamine Receptors

The legend of Figure 1 was given incorrectly. Histidine should be corrected as histamine.

Molecular Vibration-Activity Relationship in the Agonism of Adenosine Receptors

The molecular vibration-activity relationship in the receptor-ligand interaction of adenosine receptors was investigated by structure similarity, molecular vibration, and hierarchical clustering in a dataset of 46 ligands of adenosine receptors. The resulting dendrogram was compared with those of another kind of fingerprint or descriptor. The dendrogram result produced by corralled intensity of molecular vibrational frequency outperformed four other analyses in the current study of adenosine receptor agonism and antagonism. The tree that was produced by clustering analysis of molecular vibration patterns showed its potential for the functional classification of adenosine receptor ligands.

Characteristics in Molecular Vibrational Frequency Patterns between Agonists and Antagonists of Histamine Receptors

To learn the differences between the structure-activity relationship and molecular vibration-activity relationship in the ligand-receptor interaction of the histamine receptor, 47 ligands of the histamine receptor were analyzed by structural similarity and molecular vibrational frequency patterns. The radial tree that was produced by clustering analysis of molecular vibrational frequency patterns shows its potential for the functional classification of histamine receptor ligands.

Evolutionary Signature of Information Transfer Complexity in Cellular Membrane Proteomes

Cell membrane proteins play crucial roles in the cell's molecular interaction with its environment and within itself. They consist of membrane-bound proteins and many types of transmembrane (TM) proteins such as receptors, transporters, channel proteins, and enzymes. Membrane proteomes of cellular organisms reveal some characteristics in their global topological distribution according to their evolutionary positions, and show their own information transfer complexity. Predicted transmembrane segments (TMSs) in membrane proteomes with HMMTOP showed near power-law distribution and frequency characteristics in 6-TMS and 7-TMS proteins in prokaryotes and eukaryotes, respectively. This reaffirms the important roles of membrane receptors in cellular communication and biological evolutionary history.

Deducing Isoform Abundance from Exon Junction Microarray

Alternative splicing (AS) is an important mechanism of producing transcriptome diversity and microarray techniques are being used increasingly to monitor the splice variants. There exist three types of microarrays interrogating AS events-junction, exon, and tiling arrays. Junction probes have the advantage of monitoring the splice site directly. Johnson et al., performed a genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays (Science 302:2141-2144, 2003), which monitored splicing at every known exon-exon junctions for more than 10,000 multi-exon human genes in 52 tissues and cell lines. Here, we describe an algorithm to deduce the relative concentration of isoforms from the junction array data. Non-negative Matrix Factorization (NMF) is applied to obtain the transcript structure inferred from the expression data. Then we choose the transcript models consistent with the ECgene model of alternative splicing which is based on mRNA and EST alignment. The probe-transcript matrix is constructed using the NMF-consistent ECgene transcripts, and the isoform abundance is deduced from the non-negative least squares (NNLS) fitting of experimental data. Our method can be easily extended to other types of microarrays with exon or junction probes.