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International Joint Conference on Neural Networks (IJCNN)

作者： Feihong Wu B. Olson D. Dobbs V. Honavar Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA USA

The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting protein binding sites. An appropriate choice of the kernel function is critical to the performance of SVM. Kernel functions offer a way to incorporate domain-specific knowledge into the classifier. We compare the performance of three types of kernels functions: identity kernel, sequence-alignment kernel, and amino acid substitution matrix kernel in the case of SVM classifiers for predicting protein-protein, protein-DNA and protein-RNA binding sites. The results show that the identity kernel is quite effective in on all three tasks. The substitution kernel based on amino acid substitution matrices that take into account structural or evolutionary conservation or physicochemical properties of amino acids yields modest improvement.

关键词： Kernel Proteins Amino acids Support vector machines Support vector machine classification Matrices Drugs Signal design Signal analysis Signal processing

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Breaking the high-throughput bottleneck

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Scientific Computing 2006年第4期23卷 22-26页

作者： Waters, Katrina M. Singhal, Mudita Webb-Robertson, Bobbie-Jo M. Stephan, Eric G. Gephart, Julie M. Computational Biology and Bioinformatics Groups United States Lab's Systems Biology Program Biological Sciences Division United States PNNL United States

The development of high-throughput (HTP) technologies such as transcriptomics (microarrays) and proteomics has made a revolution in the medical industry. PNNL is developing a systems biology computational framework using a problem-solving environment called bioinformatics Resource Manager (BRM) that automates routine data-merging and data-mining methods, and provides seamless linkage to data visualization tools. The BRM is designed to be operated by biologists looking for greater insights into their experimental results. BRM uses Remote Method Invocation (RMI) to launch several external data analysis tools, including NCBI's PubMed, the Conserved Domain Architectural Retrieval Tool (CDART) and the Basic Local Alignment Search Tool (BLAST). PQuad is a proteomics visualization tool that combines DNA or RNA sequence and proteomics data at multiple resolutions. It enables visualization of the data at the chromosome, gene and sequence levels.

关键词： Data visualization

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High torsional energy disulfides: Relationship between cross-strand disulfides and right-handed staples

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Journal of bioinformatics and computational biology 2006年第1期4卷 155-168页

作者： Haworth, Naomi L. Feng, Lina L. Wouters, Merridee A. Computational Biology and Bioinformatics Program Victor Chang Cardiac Research Institute Darlinghurst NSW 2010 384 Victoria Rd. Australia School of Chemistry University of Sydney Sydney NSW 2006 Australia School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney Sydney NSW 2052 Australia

Redox-active disulfides are capable of being oxidized and reduced under physiological conditions. The enzymatic role of redox-active disulfides in thiol-disulfide reductases is well-known, but redox-active disulfides are also present in non-enzymatic protein structures where they may act as switches of protein function. Here, we examine disulfides linking adjacent β-strands (cross-strand disulfides), which have been reported to be redox-active. Our previous work has established that these cross-strand disulfides have high torsional energies, a quantity likely to be related to the ease with which the disulfide is reduced. We examine the relationship between conformations of disulfides and their location in protein secondary structures. By identifying the overlap between cross-strand disulfides and various conformations, we wish to address whether the high torsional energy of a cross-strand disulfide is sufficient to confer redox activity or whether other factors, such as the presence of the cross-strand disulfide in a strained β-sheet, are required. © Imperial College Press.

关键词： Cross-strand disulfide Disulfide Redox signaling Right-handed staple

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Identifying interaction sites in "Recalcitrant" proteins: Predicted protein and RNA binding sites in REV proteins of HIV-1 and EIAV agree with experimental data

Identifying interaction sites in "Recalcitrant" proteins: Pr...

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11th Pacific Symposium on Biocomputing 2006, PSB 2006

作者： Terribllini, Michael Lee, Jae-Hyung Yan, Changhui Jernigan, Robert L. Carpenter, Susan Honavar, Vasant Dobbs, Drena Bioinformatics and Computational Biology Graduate Program LH. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50010 United States Department of Computer Science 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 Artificial Intelligence Research Laboratory Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA 50010 United States Department of Veterinary Microbiology and Pathology Washington State University Pullman WA. 99164 United States

ISBN: (纸本)9812564632

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation or gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the proiein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-I and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.

关键词： Binding sites

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The inflammatory and normal transcriptome of mouse bladder detrusor and mucosa

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BMC Physiology 2006年第1期6卷 1-20页

作者： Saban, Marcia R. Hellmich, Helen L. Turner, Mary Nguyen, Ngoc-Bich Vadigepalli, Rajanikanth Dyer, David W. Hurst, Robert E. Centola, Michael Saban, Ricardo Department of Physiology University Oklahoma Health Sciences Center Oklahoma City OK United States Department of Anesthesiology University of Texas Medical Branch Galveston TX United States Oklahoma Medical Research Foundation (OMRF) Arthritis and Immunology Research Program Microarray Core Facility Oklahoma City OK United States Daniel Baugh Institute for Functional Genomics and Computational Biology Department of Pathology Anatomy and Cell Biology Thomas Jefferson University Philadelphia United States Department of Microbiology and Immunology Laboratory for Genomics and Bioinformatics Oklahoma University Health Sciences Center Oklahoma City OK United States Department of Urology University Oklahoma Health Sciences Center Oklahoma City OK United States Cellular and Structural Biology University of Texas Health Science Center at San Antonio San Antonio TX United States

Background: An organ such as the bladder consists of complex, interacting set of tissues and cells. Inflammation has been implicated in every major disease of the bladder, including cancer, interstitial cystitis, and infection. However, scanty is the information about individual detrusor and urothelium transcriptomes in response to inflammation. Here, we used suppression subtractive hybridizations (SSH) to determine bladder tissue- and disease-specific genes and transcriptional regulatory elements (TRE)s. Unique TREs and genes were assembled into putative networks. Results: It was found that the control bladder mucosa presented regulatory elements driving genes such as myosin light chain phosphatase and calponin I that influence the smooth muscle phenotype. In the control detrusor network the Pax-3 TRE was significantly over-represented. During development, the Pax-3 transcription factor (TF) maintains progenitor cells in an undifferentiated state whereas, during inflammation, Pax-3 was suppressed and genes involved in neuronal development (synapsin I) were up-regulated. Therefore, during inflammation, an increased maturation of neural progenitor cells in the muscle may underlie detrusor instability. NF-κB was specifically over-represented in the inflamed mucosa regulatory network. When the inflamed detrusor was compared to control, two major pathways were found, one encoding synapsin I, a neuron-specific phosphoprotein, and the other an important apoptotic protein, siva. In response to LPS-induced inflammation, the liver X receptor was over-represented in both mucosa and detrusor regulatory networks confirming a role for this nuclear receptor in LPS-induced gene expression. Conclusion: A new approach for understanding bladder muscle-urothelium interaction was developed by assembling SSH, real time PCR, and TRE analysis results into regulatory networks. Interestingly, some of the TREs and their downstream transcripts originally involved in organogenesis and oncogene

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Assembling genomes on large-scale parallel computers 06

Assembling genomes on large-scale parallel computers

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： A. Kalyanaraman S.J. Emrich P.S. Schnable S. Aluru Department of Electrical and Computer Engineering Iowa State University Ames IA USA Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Iowa State University Ames IA US Dept. of Electr. & Comput. Eng. Iowa State Univ. Ames IA USA

ISBN: (纸本)9781424400546

Assembly of large genomes from tens of millions of short genomic fragments is computationally demanding requiring hundreds of gigabytes of memory and tens of thousands of CPU hours. New gene-enrichment sequencing strategies are expected to further exacerbate this situation. In this paper, we present a massively parallel genome assembly framework. The unique features of our approach include space-efficient and on-demand algorithms that consume only linear space, and heuristic strategies that reduce the number of expensive pairwise sequence alignments while maintaining assembly quality. As part of the ongoing efforts in maize genome sequencing, we applied our assembly framework to the largest available collection of maize genomic data. We report the partitioning of more than 1.6 million fragments of over 1.25 billion nucleotides total size into genomic islands in 2 hours on 1,024 processors of an IBM BlueGene/L supercomputer.

关键词： Assembly Genomics bioinformatics Large-scale systems Concurrent computing Sequences DNA Organisms biology computing Biological cells

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Refinement of NMR-determined protein structures with database derived distance constraints

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Journal of bioinformatics and computational biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database Structural bioinformatics

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A comparison of the robustness of evolutionary computation and random walks

A comparison of the robustness of evolutionary computation a...

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Congress on Evolutionary Computation

作者： J. Schonfeld D.A. Ashlock Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Mathematics Department Iowa State University Ames IA USA

Evolution and robustness are thought to be intimately connected. Are solutions to optimization problems produced by evolutionary algorithms more robust to mutation than those produced by other classes of search algorithms? We explore this question in a model system based on bivariate real functions. Bivariate real functions serve as a well understood model systems that are easy to visualize. Both the number and robustness of optimal solutions found in multiple trials with several typical optimization algorithms were compared. In the majority of the function landscapes explored the tournament selection evolutionary algorithm found optimal solutions which were significantly more robust to mutation than those discovered by the other algorithms.

关键词： Robustness Evolutionary computation Genetic mutations Evolution (biology) Biological systems Proteins Stochastic processes Systems engineering and theory Stochastic systems bioinformatics

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Finite Size Effects on Thermal Denaturation of Globular Proteins

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Physical Review Letters 2004年第26期93卷 268107-268107页

作者： Mai Suan Li Dmitri K. Klimov D. Thirumalai Institute of Physics Polish Academy of Sciences Al. Lotnikow 32/46 02-668 Warsaw Poland Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA Bioinformatics and Computational Biology Program School of Computational Sciences George Mason University Manassas Virginia 20110 USA

Finite size effects on the cooperative thermal denaturation of proteins are considered. A dimensionless measure of cooperativity, Ωc, scales as Nζ, where N is the number of amino acids. Surprisingly, we find that ζ is universal with ζ=1+γ, where the exponent γ characterizes the divergence of the susceptibility for a self-avoiding walk. Our lattice model simulations and experimental data are consistent with the theory. Our finding rationalizes the marginal stability of proteins and substantiates the earlier predictions that the efficient folding of two-state proteins requires TF≈Tθ, where Tθ and TF are the collapse and folding transition temperatures, respectively.

关键词： .stability Transitions PRL ending susceptibility experimental data Thermal denaturation folding transition exponent unfolded native state Globular Proteins cooperativity two-state NBA

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Perspectives on Human Population Structure at the Cusp of the Sequencing Era

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Genomics and Human Genetics 1000年第1期12卷 245-274页

作者： John Novembre Sohini Ramachandran *Both authors contributed equally. 1Department of Ecology and Evolutionary Biology and Interdepartmental Program on Bioinformatics University of California Los Angeles California 90403 email: jnovembre@ucla.edu 2Department of Ecology and Evolutionary Biology and Center for Computational Molecular Biology Brown University Providence Rhode Island 02912 email: sramachandran@brown.edu

Human groups show structured levels of genetic similarity as a consequence of factors such as geographical subdivision and genetic drift. Surveying this structure gives us a scientific perspective on human origins, sheds light on evolutionary processes that shape both human adaptation and disease, and is integral to effectively carrying out the mission of global medical genetics and personalized medicine. Surveys of population structure have been ongoing for decades, but in the past three years, single-nucleotide-polymorphism (SNP) array technology has provided unprecedented detail on human population structure at global and regional scales. These studies have confirmed well-known relationships between distantly related populations and uncovered previously unresolvable relationships among closely related human groups. SNPs represent the first dense genome-wide markers, and as such, their analysis has raised many challenges and insights relevant to the study of population genetics with whole-genome sequences. Here we draw on the lessons from these studies to anticipate the directions that will be most fruitful to pursue during the emerging whole-genome sequencing era.

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