Beyond Modeling: All-Atom Olfactory Receptor Model Simulations

Olfactory receptors (ORs) are a type of GTP-binding protein-coupled receptor (GPCR). These receptors are responsible for mediating the sense of smell through their interaction with odor ligands. OR-odorant interactions marks the first step in the process that leads to olfaction. Computational studies on model OR structures can generate focused and novel hypotheses for further bench investigation by providing a view of these interactions at the molecular level beyond inferences that are drawn merely from static docking. Here we have shown the specific advantages of simulating the dynamic environment associated with OR-odorant interactions. We present a rigorous protocol which ranges from the creation of a computationally derived model of an olfactory receptor to simulating the interactions between an OR and an odorant molecule. Given the ubiquitous occurrence of GPCRs in the membranes of cells, we anticipate that our OR-developed methodology will serve as a model for the computational structural biology of all GPCRs

A Framework for Exploring Functional Variability in Olfactory Receptor Genes

Background: Olfactory receptors (ORs) are the largest gene family in mammalian genomes. Since nearly all OR genes are orphan receptors, inference of functional similarity or differences between odorant receptors typically relies on sequence comparisons. Based on the alignment of entire coding region sequence, OR genes are classified into families and subfamilies, a classification that is believed to be a proxy for OR gene functional variability. However, the assumption that overall protein sequence diversity is a good proxy for functional properties is untested.Methodology: Here, we propose an alternative sequence-based approach to infer the similarities and differences in OR binding capacity. Our approach is based on similarities and differences in the predicted binding pockets of OR genes, rather than on the entire OR coding region.Conclusions: Interestingly, our approach yields markedly different results compared to the analysis based on the entire OR coding-regions. While neither approach can be tested at this time, the discrepancy between the two calls into question the assumption that the current classification reliably reflects OR gene functional variability.</p

Integrated olfactory receptor and microarray gene expression databases

<p>Abstract</p> <p>Background</p> <p>Gene expression patterns of olfactory receptors (ORs) are an important component of the signal encoding mechanism in the olfactory system since they determine the interactions between odorant ligands and sensory neurons. We have developed the Olfactory Receptor Microarray Database (ORMD) to house OR gene expression data. ORMD is integrated with the Olfactory Receptor Database (ORDB), which is a key repository of OR gene information. Both databases aim to aid experimental research related to olfaction.</p> <p>Description</p> <p>ORMD is a Web-accessible database that provides a secure data repository for OR microarray experiments. It contains both publicly available and private data; accessing the latter requires authenticated login. The ORMD is designed to allow users to not only deposit gene expression data but also manage their projects/experiments. For example, contributors can choose whether to make their datasets public. For each experiment, users can download the raw data files and view and export the gene expression data. For each OR gene being probed in a microarray experiment, a hyperlink to that gene in ORDB provides access to genomic and proteomic information related to the corresponding olfactory receptor. Individual ORs archived in ORDB are also linked to ORMD, allowing users access to the related microarray gene expression data.</p> <p>Conclusion</p> <p>ORMD serves as a data repository and project management system. It facilitates the study of microarray experiments of gene expression in the olfactory system. In conjunction with ORDB, ORMD integrates gene expression data with the genomic and functional data of ORs, and is thus a useful resource for both olfactory researchers and the public.</p

An Analysis of the Abstracts Presented at the Annual Meetings of the Society for Neuroscience from 2001 to 2006

Annual meeting abstracts published by scientific societies often contain rich arrays of information that can be computationally mined and distilled to elucidate the state and dynamics of the subject field. We extracted and processed abstract data from the Society for Neuroscience (SFN) annual meeting abstracts during the period 2001–2006 in order to gain an objective view of contemporary neuroscience. An important first step in the process was the application of data cleaning and disambiguation methods to construct a unified database, since the data were too noisy to be of full utility in the raw form initially available. Using natural language processing, text mining, and other data analysis techniques, we then examined the demographics and structure of the scientific collaboration network, the dynamics of the field over time, major research trends, and the structure of the sources of research funding. Some interesting findings include a high geographical concentration of neuroscience research in the north eastern United States, a surprisingly large transient population (66% of the authors appear in only one out of the six studied years), the central role played by the study of neurodegenerative disorders in the neuroscience community, and an apparent growth of behavioral/systems neuroscience with a corresponding shrinkage of cellular/molecular neuroscience over the six year period. The results from this work will prove useful for scientists, policy makers, and funding agencies seeking to gain a complete and unbiased picture of the community structure and body of knowledge encapsulated by a specific scientific domain

Workflow and Atlas System for Brain-Wide Mapping of Axonal Connectivity in Rat

Detailed knowledge about the anatomical organization of axonal connections is important for understanding normal functions of brain systems and disease-related dysfunctions. Such connectivity data are typically generated in neuroanatomical tract-tracing experiments in which specific axonal connections are visualized in histological sections. Since journal publications typically only accommodate restricted data descriptions and example images, literature search is a cumbersome way to retrieve overviews of brain connectivity. To explore more efficient ways of mapping, analyzing, and sharing detailed axonal connectivity data from the rodent brain, we have implemented a workflow for data production and developed an atlas system tailored for online presentation of axonal tracing data. The system is available online through the Rodent Brain WorkBench (www.rbwb.org; Whole Brain Connectivity Atlas) and holds experimental metadata and high-resolution images of histological sections from experiments in which axonal tracers were injected in the primary somatosensory cortex. We here present the workflow and the data system, and exemplify how the online image repository can be used to map different aspects of the brain-wide connectivity of the rat primary somatosensory cortex, including not only presence of connections but also morphology, densities, and spatial organization. The accuracy of the approach is validated by comparing results generated with our system with findings reported in previous publications. The present study is a contribution to a systematic mapping of rodent brain connections and represents a starting point for further large-scale mapping efforts

A Framework for Exploring Functional Variability in Olfactory Receptor Genes

BACKGROUND: Olfactory receptors (ORs) are the largest gene family in mammalian genomes. Since nearly all OR genes are orphan receptors, inference of functional similarity or differences between odorant receptors typically relies on sequence comparisons. Based on the alignment of entire coding region sequence, OR genes are classified into families and subfamilies, a classification that is believed to be a proxy for OR gene functional variability. However, the assumption that overall protein sequence diversity is a good proxy for functional properties is untested. METHODOLOGY: Here, we propose an alternative sequence-based approach to infer the similarities and differences in OR binding capacity. Our approach is based on similarities and differences in the predicted binding pockets of OR genes, rather than on the entire OR coding region. CONCLUSIONS: Interestingly, our approach yields markedly different results compared to the analysis based on the entire OR coding-regions. While neither approach can be tested at this time, the discrepancy between the two calls into question the assumption that the current classification reliably reflects OR gene functional variability

Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology