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2011 ACM Conference on bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011

作者： Lin, Tu-Liang Vammi, Santhosh Kumar Song, Guang Computer Science Department Iowa State University Ames IA 50010 United States Program of Bioinformatics and Computational Biology Iowa State University Ames IA 50010 United States

ISBN: (纸本)9781450307963

Many computational approaches have been developed and used for sampling protein conformations near the native state. However, it has been difficult to evaluate the quality of the conformations sampled or to compare them among the various sampling schemes. In this work, we develop a novel method for evaluating the quality of conformation ensembles and apply it to evaluate ubiquitin conformations generated from four widely-used conformation sampling approaches, namely, MD simulation, Elastic Network Model (ENM), CONCOORD, and *** choose ubiquitin because there exists abundant experimental residual dipolar coupling (RDC) data on this protein. RDC data contains rich ensemble-averaged information about a given protein and thus provide tight constraints that can be used for probing what conformations should make up the protein ensemble. Our results demonstrate that the conformations generated by MD simulations are the best among all sampling methods. Specifically, MD simulation performs significantly better than the other methods in capturing the side chain motions. The backbone flexibility modeled and sampled by tCONCOORD comes quite close, with CONCOORD and ENM trailing behind. Copyright © 2011 ACM.

关键词： Conformations

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Modularity detection in protein-protein interaction networks

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BMC Research Notes 2011年第1期4卷 1-6页

作者： Narayanan, Tejaswini Gersten, Merril Subramaniam, Shankar Grama, Ananth Department of Electrical and Computer Engineering University of California San Diego United States Graduate Program in Bioinformatics and Systems Biology University of California San Diego United States Department of Bioengineering University of California San Diego United States Department of Computer Science Purdue University West Lafayette IN United States

Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, *** and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the applic

关键词： Biological Network Jaccard Index Input Network High Betweenness Jaccard Similarity Coefficient

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Prediction of mitochondrial proteins of malaria parasite using improved hybrid method and reduced amino acid alphabet

Prediction of mitochondrial proteins of malaria parasite usi...

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2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Chen, Ying-Li Li, Qian-Zhong Zhang, Li-Qing Laboratory of Theoretical Biophysics School of Physical Science and Technology Inner Mongolia University Hohhot China Department of Computer Science Virginia Tech Blacksburg VA United States Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg VA United States

ISBN: (纸本)9781424493524

The rate of human death and morbidity due to malaria is increasing in many parts of the developing countries. Thus, there is a great need to understand the critical pathways in malaria parasite in order to develop effective drugs and vaccines. In this work, based on the measure of diversity definition, we introduce the increment of diversity fusion (IDF), an improved hybrid method to predict mitochondrial proteins of malaria parasite. We conduct our experiment on an expanded protein dataset where we require the pairwise identity between two proteins is less than 25%. By choosing amino acids composition as the only input vector, we are able to achieve 65.4% accuracy with 0.32 Mathew's correlation coefficient (MCC) for the jackknife test. Further, incorporting the compositions of the N-terminal and C-terminal regions into the input vector, we show that the prediction results are improved to 82.0% accuracy with 0.64 MCC in the jackknife test. In addition, by combining with the several reduced amino acid alphabet and the hydropathy distribution along protein sequence, we achieve maximum 83.4% accuracy with 0.67 MCC in the jackknife test by using the 64 dipeptide compositions of the reduced amino acid alphabet obtained from Protein Blocks method. © 2011 IEEE.

关键词： Proteins

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Ranking docked models of protein-protein complexes using predicted partner-specific protein-protein interfaces: A preliminary study 11

Ranking docked models of protein-protein complexes using pre...

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2011 ACM Conference on bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Jordan, Rafael A. El-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Computer Science Pontificia Universidad Javeriana Cali Colombia Department of Systems and Computer Engineering AI-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting proteinprotein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://***/DockRank/. Copyright © 2011 ACM.

关键词： Proteins

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Improving protein-RNA interface prediction by combining sequence homology based method with a naive bayes classifier: Preliminary results 11

Improving protein-RNA interface prediction by combining sequ...

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2011 ACM Conference on bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Walia, Rasna EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Systems and Computer Engineering Al-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Protein-RNA interactions play important roles in cellular processes like protein synthesis, RNA processing, and gene expression regulation. Reliable identification of the interfaces involved in RNA-protein interactions is essential for comprehending the mechanisms and the functional implications of these interactions and provides a valuable guide for rational drug discovery and design. Because the determination of 3D structures of protein-RNA complexes has various technical limitations and is typically costly, reliable in silico interface prediction methods that require only the sequence information are urgently needed. We present HomPRIP, a homologous sequence based method for predicting protein-RNA interfaces, based on our conservation analysis of protein-RNA interfaces. We test Hom-PRIP on a benchmark dataset of 199 proteins and compare it with the state-of-the-art protein-RNA interface prediction methods. Our results show that HomPRIP can reliably identify protein-RNA interface residues in 71% of test proteins with at least one putative sequence homolog passing the similarity thresholds of HomPRIP. Moreover, to facilitate predictions for proteins with no identified homologs, we develop HomPRIP-NB, a method combining the HomPRIP predictor and a Naive Bayes (NB) classifier trained using evolutionary information derived from alignments against the NCBI nr database. Our results suggest that HomPRIP-NB significantly outperforms the state-of-the-art machine learning methods for predicting protein-RNA interface residues. Copyright © 2011 ACM.

关键词： Proteins

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Parsimonious reconstruction of network evolution

Parsimonious reconstruction of network evolution

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Lecture Notes in computer science

作者： Patro, Rob Sefer, Emre Malin, Justin Marçais, Guillaume Navlakha, Saket Kingsford, Carl Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 United States Department of Computer Science University of Maryland College Park MD 20742 United States Computational Biology Bioinformatics and Genomics Concentration Biological Sciences Graduate Program University of Maryland College Park MD 20742 United States Program in Applied Mathematics Statistics and Scientific Computation University of Maryland College Park MD 20742 United States School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9783642230370

We consider the problem of reconstructing a maximally parsimonious history of network evolution under models that support gene duplication and loss and independent interaction gain and loss. We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. © 2011 Springer-Verlag.

关键词： Genes

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MilP: The Monomer Identification and Isolation program

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Evolutionary bioinformatics 2012年 8卷

作者： Christopher Bun1 William Ziccardi Jeffrey Doering Catherine Putonti Department of Computer Science Loyola University Chicago 820 N Michigan Avenue Chicago IL 60611 USA.Current Affiliation: Department of Computer Science University of Chicago 1100 E 58th Street Chicago IL 60637 USA. Department of Biology Loyola University Chicago 1032 W Sheridan Road Chicago IL 60660 USA. Department of Computer Science Loyola University Chicago 820 N Michigan Avenue Chicago IL 60611 USA.Department of Biology Loyola University Chicago 1032 W Sheridan Road Chicago IL 60660 USA.Bioinformatics Program Loyola University Chicago 1032 W Sheridan Road Chicago IL 60660 USA.

Repetitive elements within genomic DNA are both functionally and evolutionarily informative. Discovering these sequences ab initio is computationally challenging, compounded by the fact that selection on these repeats is often relaxed; thus sequence identity between repetitive elements can vary significantly. Here we present a new application, the Monomer Identification and Isolation program (MilP), which provides functionality to both search for a particular repeat as well as discover repetitive elements within a larger genomic sequence. To compare MilP's performance with other repeat detection tools, analysis was conducted for synthetic sequences as well as several α21-II clones and HC21 BAC sequences. The primary benefit of MilP is the fact that it is a single tool capable of searching for both known monomelic sequences as well as discovering the occurrence of repeats ab initio, per the user's required sensitivity of the search. Furthermore, the report functionality helps easily facilitate subsequent phylogenetic analysis.

关键词： monomer discovery repetitive element identification pericentromeric repeats

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Literature-based Evaluation of Microarray Normalization Procedures

Literature-based Evaluation of Microarray Normalization Proc...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Nicholas A. Furlotte Lijing Xu Robert W. Williams Ramin Homayouni Computer Science department University of California Los Angeles Los Angeles USA Bioinformatics Program University of Memphis Memphis USA Dept. of department and Neurobiology University of Tennessee HSC Memphis USA

Normalization procedures attempt to remove non- biological variance within microarray datasets. The choice of normalization procedure is important, as it has a dramatic effect on downstream data analysis. Although many normalization procedures have been developed, comparison and evaluation of their performance is difficult. We present a method to evaluate normalization procedures by utilizing gene- gene associations derived from the biomedical literature via Latent Semantic Indexing. The functional coherence of co- regulated genes obtained from different normalized data sets is calculated in order to evaluate the effectiveness of each normalization procedure. The method was tested on three popular normalization procedures (MAS5, PDNN and RMA) applied to gene expression data across 71 recombinant inbred mouse brain samples. Results show that, on average, MAS5 outperforms both PDNN and RMA by producing a higher number of functionally cohesive gene sets. These results demonstrate that our literature-based cohesion analysis can provide an objective method for evaluation of normalization procedures.

关键词： Probes bioinformatics Correlation Indexes Mice Arrays Strain

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A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers

A molecular evolutionary algorithm for learning hypernetwork...

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Congress on Evolutionary Computation

作者： Ji-Hoon Lee Bado Lee Joon Shik Kim Russell Deaton Byoung-Tak Zhang Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea School of Computer Science & Engineering Seoul National University Seoul South Korea Biointelligence Laboratory Seoul National University Seoul South Korea Department of Computer Science and Engineering University of Arkansas Fayetteville USA Biointelligence Laboratory School of Computer Science & Engineering Seoul National University Seoul South Korea

We describe a "molecular" evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for selection, amplification and mutation operators for evolving hypernetworks. We analyze the convergence properties of the molecular evolutionary algorithms on simulated DNA computers. The performance of the algorithms is demonstrated on the task of simulating the cognitive process of learning a language model from a drama corpus to identify the style of an unknown drama. We also discuss other applications of the molecular evolutionary algorithms. In addition to their feasibility in DNA computing, which opens a new horizon of in vitro evolutionary computing, the molecular evolutionary algorithm provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation.

关键词： DNA Libraries Training computers Evolutionary computation In vitro Estimation

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A scalable approach for alignments in fuzzy biological networks

A scalable approach for alignments in fuzzy biological netwo...

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2010 International Conference on bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010

作者： Liu, Yunkai Verone, Alissa Bioinformatics Program Computer and Information Science Department Gannon University United States

ISBN: (纸本)9781617820694

Interactions between biological entities, whether cells, proteins or genes are under meticulous study. As high throughput calculations have made biological network a popular target for study, methods for comparison have not been mastered. Multiple classes of bionetwork models exist, such as metabolic, protein-gene, or protein-protein interactions;the latter being of most importance to our research. Several different computational approaches have been taken by introducing bionetworks as tools to evaluate genomic or biological data. The primary focus was to develop a new method for comparison of bionetworks with great amount of noises. In this paper, an algorithm was designed for the pairwise comparison of bionetworks, considering the most conserved and most variable areas. Copyright© (2010) by the International Society for Research in science and Technology.

关键词： Genes

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