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Large-scale analysis of human heavy chain V(D)J recombination patterns

Immunome Research 2008年第1期4卷 3-3页

作者： Volpe, Joseph M Kepler, Thomas B Center for Computational Immunology Duke University Durham NC United States Department of Immunology Duke Univeristy Medical Center Durham NC United States Department of Biostatistics and Bioinformatics Duke Univeristy Medical Center Durham NC United States Institute of Statistics and Decision Sciences Duke Univeristy Durham NC United States Computational Biology and Bioinformatics Graduate Program Institute for Genome Sciences and Policy Duke Univeristy Durham NC United States

Background. The processes involved in the somatic assembly of antigen receptor genes are unique to the immune system and are driven largely by random events. Subtle biases, however, may exist and provide clues to the molecular mechanisms involved in their assembly and selection. Large-scale efforts to provide baseline data about the genetic characteristics of immunoglobulin (Ig) genes and the mechanisms involved in their assembly have recently become possible due to the rapid growth of genetic databases. Results. We gathered and analyzed nearly 6,500 productive human Ig heavy chain genes and compared them with 325 non-productive Ig genes that were originally rearranged out of frame and therefore incapable of being biased by selection. We found evidence for differences in n-nucleotide tract length distributions which have interesting interpretations for the mechanisms involved in n-nucleotide polymerization. Additionally, we found striking statistical evidence for pairing preferences among D and J segments. We present a statistical model to support our hypothesis that these pairing biases are due to multiple sequential D-to-J rearrangements. Conclusion. We present here the most precise estimates of gene segment usage frequencies currently available along with analyses regarding n-nucleotide distributions and D-J segment pair preferences. Additionally, we provide the first statistical evidence that sequential D-J recombinations occur at the human heavy chain locus during B-cell ontogeny with an approximate frequency of 20%.

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Millimeter-scale genetic gradients and community-level molecular convergence in a hypersaline microbial mat

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Molecular Systems biology 2008年第1期4卷 198页

作者： Kunin, Victor Raes, Jeroen Harris, J. Kirk Spear, John R. Walker, Jeffrey J. Ivanova, Natalia Von Mering, Christian Bebout, Brad M. Pace, Norman R. Bork, Peer Hugenholtz, Philip Microbial Ecology Program DOE Joint Genome Institute Walnut Creek CA United States Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg Germany Department of Pediatrics University of Colorado Denver Aurora CO United States Division of Environmental Science and Engineering Colorado School of Mines Golden CO United States Department of Molecular Cellular and Developmental Biology University of Colorado Boulder CO United States Genome Biology Program DOE Joint Genome Institute Walnut Creek CA United States Institute of Molecular Biology Swiss Institute of Bioinformatics University of Zurich Zurich Switzerland Microbial Ecology/Biogeochemistry Research Laboratory Exobiology Branch NASA Ames Research Center Moffett Field CA United States Microbial Ecology Program DOE Joint Genome Institute Walnut Creek CA 94598 2800 Mitchell Drive United States

To investigate the extent of genetic stratification in structured microbial communities, we compared the metagenomes of 10 successive layers of a phylogenetically complex hypersaline mat from Guerrero Negro, Mexico. We found pronounced millimeter-scale genetic gradients that were consistent with the physicochemical profile of the mat. Despite these gradients, all layers displayed near-identical and acid-shifted isoelectric point profiles due to a molecular convergence of amino-acid usage, indicating that hypersalinity enforces an overriding selective pressure on the mat community. © 2008 EMBO and Nature Publishing Group All rights reserved.

关键词： Fine-scale Hypersalinity Metagenomics Microbial ecology Salt-in

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Refinement of under-determined loops of human prion protein by database-derived distance constraints

Refinement of under-determined loops of human prion protein ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Feng Cui Kriti Mukhopadhyay Won-Bin Young Robert L. Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics USA University Research Park Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University USA Program on Bioinformatics and Computational Biology USA

computational simulations of the conversion from the normal cellular prion (PrP c ) to the scrapie prion (PrP Sc ) are usually based on the structures determined by NMR because of the difficulties in crystallizing prion protein. Due to insufficient experimental restraints, a biologically critical loop region in PrP c (residues 167-171), which is a potential binding site for Protein X, is under-determined in most mammalian species. Here, we show that by adding information about distance constraints derived from a database of high-resolution protein structures, this under-determined loop as well as other secondary structural elements of the E200K variant of human prion protein (hPrP c ), a disease-related isoform, can be refined into more realistic structures in the structural ensemble with improved quality and increased accuracy. In particular, the ensemble becomes more compact after the refinement and the percentage of residues in the most favourable region of the Ramachandran diagram is increased to about 90% in the refined structures from the 80 to 85% range in the previously reported structures. Our results not only provide significantly improved structures of the prion protein and hence would facilitate insights into its conversion in the spongiform encephalopathies, but also demonstrate the strong potential for using databases of known protein structures for structure determination and refinement.

关键词： Humans Proteins Databases Diseases Nuclear magnetic resonance Genetic mutations computational biology Brain Pathogens Amino acids

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Analysis of Protein Protein Dimeric Interfaces

Analysis of Protein Protein Dimeric Interfaces

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Feihong Wu Fadi Towfic Drena Dobbs Vasant Honavar Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA

We analyzed the structural properties and the local surface environment of surface amino acid residues of proteins using a large, non-redundant dataset of 2383 protein chains in dimeric complexes from PDB. We compared the interface residues and non-interface residues based on six properties: side chain orientation, surface roughness, solid angle, ex value, hydrophobicity and interface cluster size. The results of our analysis show that interface residues have side chains pointing inward; interfaces are rougher, tend to be flat, moderately convex or concave and protrude more relative to non-interface surface residues. Interface residues tend to be surrounded by hydrophobic neighbors and tend to form clusters consisting of three or more interfaces residues. These findings are consistent with previous published studies using much smaller datasets, while allowing for more qualitative conclusions due to our larger dataset. Preliminary results suggest the possibility of using the six the properties to identify putative interface residues.

关键词： Proteins Rough surfaces Surface roughness bioinformatics Amino acids Solids Sequences Biomedical computing Computer interfaces Biomedical measurements

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Structural characterization of RNA-binding sites of proteins: Preliminary results

Structural characterization of RNA-binding sites of proteins...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Fadi Towfic David C. Gemperline Cornelia Caragea Feihong Wu Drena Dobbs Vasant Honavar Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA Department of Biology Department of Chemistry Carthage College Kenosha WI USA Department of Computer Science Iowa State University Ames IA USA Department of Genetics Development and Cell Biology Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA Department of Computer Science Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA

We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA...etc), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the protein data bank. Our analysis of surface roughness, solid angle and CX value of amino acid residues for each of the protein chains in the dataset shows that: The protein-RNA interface residues tend to be protruding compared to non-interface residues and tend to have higher surface roughness and exhibit moderately convex or concave solid angles. Furthermore, the protein chains in protein-RNA interfaces that contain Viral RNA and rRNA significantly differ from those that contain dsRNA, mRNA siRNA, snRNA, SRP RNA and tRNA with respect to their CX values. The results of this analysis sug gests the possibility of using such structural features to reliably identify protein-RNA interface residues when the structure of the protein is available but the structures of complexes formed by the protein with RNA are not.

关键词： RNA Protein engineering biology computing Amino acids bioinformatics Computer interfaces Rough surfaces Surface roughness Solids Machine learning

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The role of heritability in mapping expression quantitative trait loci

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BMC proceedings 2007年第1期1 Suppl 1卷 S86页

作者： Song Huang David Ballard Hongyu Zhao Program of Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA. song.huang@yale.edu

Gene expression, as a heritable complex trait, has recently been used in many genome-wide linkage studies. The estimated overall heritability of each trait may be considered as evidence of a genetic contribution to the total phenotypic variation, which implies the possibility of mapping genome regions responsible for the gene expression variation via linkage analysis. However, heritability has been found to be an inconsistent predictor of significant linkage signals. To investigate this issue in human studies, we performed genome-wide linkage analysis on the 3554 gene expression traits of 194 Centre d'Etude du Polymorphisme Humain individuals provided by Genetic Analysis Workshop 15. Out of the 422 expression traits with significant linkage signals identified (LOD > 5.3), 89 traits have low estimated heritability (h2 < 10%), among which 23 traits have an estimated heritability equal to 0. The linkage analysis on individual pedigree shows that the overall LOD scores may result from a few pedigrees with strong linkage signals. Screening gene expressions before linkage analysis using a relatively low heritability (h2 < 20%) may result in a loss of significant linkage signals, especially for trans-acting expression quantitative trait loci (49%).

关键词： Linkage Analysis Heritability Estimate Expression Trait Genetic Analysis Workshop Select Gene Expression

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Modeling protein interaction network and mechanisms in exocytosis

Modeling protein interaction network and mechanisms in exocy...

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Zhou, Wen Xia, Tian Tong, Jiansong Dickerson, Julie Su, Bo Gu, Xun Department of Mathematics Iowa State University Ames IA 50010 United States Program of Bioinformatics and Computational Biology Iowa State University Ames IA 50010 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50010 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50010 United States

ISBN: (纸本)1424415098

Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles which contain a wide range of intracellular molecules such as hormones, matrix proteins and neurotransmitters. Recent studies have shown this process is regulated by molecular interactions among a group of well defined and conserved proteins. To gain insight into the dynamics of these interactions, we use protein interaction network modeling to investigate exocytotic system (particularly in endocrine) computationally and mathematically. The protein interactions are formulated into ordinary differential equations (ODE). We then apply a novel mathematic approach to estimate model parameters from experimental data for SNAREs-only network (with three key proteins), which is a subset of this system. Our approach is able to sense temporal changes in protein concentration and ratios of multiple proteins and precisely reconstruct the dynamical process of exocytosis, including vesicle docking, priming and fusion. Additionally, the model suggests that initial concentration of synaptic proteins plays a crucial role in efficiency of vesicle fusion, based on system stability analysis. ©2007 IEEE.

关键词： Proteins

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Hot and cold: Spatial fluctuation in HIV-1 recombination rates

Hot and cold: Spatial fluctuation in HIV-1 recombination rat...

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Rajaram, M.L. Minin, V.N. Suchard, M.A. Dorman, K.S. Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Statistics University of Washington Seattle WA 98195 United States Departments of Biomathematics Human Genetics and Biostatistics University of California Los Angeles Los Angeles CA 90095 United States Departments of Statistics and Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States

ISBN: (纸本)1424415098

Coinfection of a single cell with two or more HIV strains may produce recombinant viruses upon template switching by the replication machinery. We applied a hierarchical multiple change point model to simultaneously infer inter-subtype recombination breakpoints and spatial variation in the recombination rate along the HIV-1 genome. We examined thousands of publicly available HIV-1 sequences representing the worldwide epidemic and focused on 544 unique recombinants with 1, 701 recombination breakpoints. Estimates of per site recombination rate revealed the presence of a novel hotspot in the pol gene, surrounded by a cluster of mutations associated with resistance to reverse transcriptase inhibitors. We also confirm the presence of a known hotspot in the env gene and a previously hypothesized hotspot in the gag gene. ©2007 IEEE.

关键词： Genes

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PRTAD: A database for protein residue torsion angle distributions

PRTAD: A database for protein residue torsion angle distribu...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xiaoyong Sun Di Wu Robert Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics Western Kentucky University Bowling Green KY USA Department of Biochemistry Biophysics and Molecular Biology Department of Mathematics Iowa State University Ames IA USA

PRTAD is a dedicated database and structural bioinformatics system for protein analysis and modeling. The database is developed to host and analyze the statistical data for protein residue level "virtual" bond and torsion angles obtained from their distributions in databases of known protein structures such as in the PDB Data Bank. PRTAD is capable of generating, caching, and displaying the statistical distributions of the angles of various types. The collected information can be used to extract geometric restraints or define statistical potentials for protein structure determination. PRTAD is supported with a friendly designed web interface so that users can easily specify the angle types, and retrieve, visualize, or download the distributions of the angles as they desire. The database PRTAD is freely accessible at http://***/prtad.

关键词： Bonding Spatial databases Protein engineering Amino acids bioinformatics computational biology Mathematics Data mining Niobium compounds Nuclear magnetic resonance

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Comparison of experimental and computed protein anisotropic temperature factors

Comparison of experimental and computed protein anisotropic ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Lei Yang Guang Song Robert L. Jernigan Program of Bioinformatics and Computational Biology Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA Department of Computer Science Iowa State University Ames IA USA Department of Biochemistry Biophysics and Molecular Biology L. H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA USA

Because of its appealing simplicity, the anisotropic network model (ANM) has been widely accepted and applied to study many molecular motion problems: such as the molecular mechanisms of GroEL-GroES function, allosteric changes in hemoglobin, ribosome motion, motor-protein motions, and conformational changes in general, etc. However, the validity of the ANM has not been closely examined. In this work, we use ANM to predict the anisotropic temperature factors of proteins. On the flip side, the rich, directional anisotropic temperature factor data available for hundreds of proteins in the protein data bank (PDB) are used as validation data to closely test the ANM model. The significance of this work is that it presents a timely, important evaluation of the model, shows the extent of its accuracy in reproducing experimental anisotropic temperature factors, and suggests ways to improve the model. An improved model will help us better understand the internal dynamics of proteins, which in turn can greatly expand the usefulness of the models, which has already been demonstrated in many applications.

关键词： Proteins Anisotropic magnetoresistance Temperature Fluctuations computational biology biology computing Computer networks bioinformatics Biochemistry Biophysics

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