Computational systems bioinformatics. Computational Systems Bioinformatics Conference最新文献_第6页

Combining sequence and structural profiles for protein solvent accessibility prediction. 结合序列和结构特征预测蛋白质溶剂可及性。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0017

R. Bondugula, Dong Xu

引用次数: 8

Optimizing Bayes error for protein structure model selection by stability mutagenesis. 基于稳定性诱变的蛋白质结构模型选择贝叶斯误差优化。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Xiaoduan Ye, Alan M Friedman, Chris Bailey-Kellogg

{"title":"Optimizing Bayes error for protein structure model selection by stability mutagenesis.","authors":"Xiaoduan Ye, Alan M Friedman, Chris Bailey-Kellogg","doi":"","DOIUrl":"","url":null,"abstract":"Site-directed mutagenesis affects protein stability in a manner dependent on the local structural environment of the mutated residue; e.g., a hydrophobic to polar substitution would behave differently in the core vs. on the surface of the protein. Thus site-directed mutagenesis followed by stability measurement enables evaluation of and selection among predicted structure models, based on consistency between predicted and experimental stability changes (DeltaDeltaGo values). This paper develops a method for planning a set of individual site-directed mutations for protein structure model selection, so as to minimize the Bayes error, i.e., the probability of choosing the wrong model. While in general it is hard to calculate exactly the multi-dimensional Bayes error defined by a set of mutations, we leverage the structure of \"DeltaDeltaGo space\" to develop tight upper and lower bounds. We further develop a lower bound on the Bayes error of any plan that uses a fixed number of mutations from a set of candidates. We use this bound in a branch-and-bound planning algorithm to find optimal and near-optimal plans. We demonstrate the significance and effectiveness of this approach in planning mutations for elucidating the structure of the pTfa chaperone protein from bacteriophage lambda.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 ","pages":"99-108"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28337236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feedback algorithm and web-server for protein structure alignment. 蛋白质结构比对的反馈算法和web服务器。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Zhiyu Zhao, Bin Fu, Francisco J Alanis, Christopher M Summa

引用次数: 0

Fast and accurate multi-class protein fold recognition with spatial sample kernels. 利用空间样本核快速准确地识别多类蛋白质折叠。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Pavel Kuksa, Pai-Hsi Huang, Vladimir Pavlovic

引用次数: 0

Combining sequence and structural profiles for protein solvent accessibility prediction. 结合序列和结构特征预测蛋白质溶剂可及性。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Rajkumar Bondugula, Dong Xu

引用次数: 0

Extensive exploration of conformational space improves Rosetta results for short protein domains. 对构象空间的广泛探索改善了罗塞塔对短蛋白质结构域的结果。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Yaohang Li, Andrew J Bordner, Yuan Tian, Xiuping Tao, Andrey A Gorin

{"title":"Extensive exploration of conformational space improves Rosetta results for short protein domains.","authors":"Yaohang Li, Andrew J Bordner, Yuan Tian, Xiuping Tao, Andrey A Gorin","doi":"","DOIUrl":"","url":null,"abstract":"With some simplifications, computational protein folding can be understood as an optimization problem of a potential energy function on a variable space consisting of all conformation for a given protein molecule. It is well known that realistic energy potentials are very \"rough\" functions, when expressed in the standard variables, and the folding trajectories can be easily trapped in multiple local minima. We have integrated our variation of Parallel Tempering optimization into the protein folding program Rosetta in order to improve its capability to overcome energy barriers and estimate how such improvement will influence the quality of the folded protein domains. Here we report that (1) Parallel Tempering Rosetta (PTR) is significantly better in the exploration of protein structures than previous implementations of the program; (2) systematic improvements are observed across a large benchmark set in the parameters that are normally followed to estimate robustness of the folding; (3) these improvements are most dramatic in the subset of the shortest domains, where high-quality structures have been obtained for >75% of all tested sequences. Further analysis of the results will improve our understanding of protein conformational space and lead to new improvements in the protein folding methodology, while the current PTR implementation should be very efficient for short (up to approximately 80 a.a.) protein domains and therefore may find practical application in system biology studies.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 ","pages":"203-9"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28337723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable computation of kinship and identity coefficients on large pedigrees. 大型家系亲属关系和身份系数的可扩展计算。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0003

E. Cheng, Brendan Elliott, Z. Ozsoyoglu

{"title":"Scalable computation of kinship and identity coefficients on large pedigrees.","authors":"E. Cheng, Brendan Elliott, Z. Ozsoyoglu","doi":"10.1142/9781848162648_0003","DOIUrl":"https://doi.org/10.1142/9781848162648_0003","url":null,"abstract":"With the rapidly expanding field of medical genetics and genetic counseling, genealogy information is becoming increasingly abundant. An important computation on pedigree data is the calculation of identity coefficients, which provide a complete description of the degree of relatedness of a pair of individuals. The areas of application of identity coefficients are numerous and diverse, from genetic counseling to disease tracking, and thus, the computation of identity coefficients merits special attention. However, the computation of identity coefficients is not done directly, but rather as the final step after computing a set of generalized kinship coefficients. In this paper, we first propose a novel Path-Counting Formula for calculating generalized kinship coefficients, which is motivated by Wright's path-counting method for computing the inbreeding coefficient for an individual. We then present an efficient and scalable scheme for calculating generalized kinship coefficients on large pedigrees using NodeCodes, a special encoding scheme for expediting the evaluation of queries on pedigree graph structures. We also perform experiments for evaluating the efficiency of our method, and compare it with the performance of the traditional recursive algorithm for three individuals. Experimental results demonstrate that the resulting scheme is more scalable and efficient than the traditional recursive methods for computing generalized kinship coefficients.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"27-36"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64001087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

The effect of massive gene loss following whole genome duplication on the algorithmic reconstruction of the ancestral populus diploid. 全基因组复制后大量基因丢失对杨树祖先二倍体算法重建的影响。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0023

Chunfang Zheng

引用次数: 7

Method for effective virtual screening and scaffold-hopping in chemical compounds. 化合物的有效虚拟筛选和跳架方法。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2007-04-04 DOI: 10.1142/9781860948732_0041

Nikil Wale, G. Karypis, Ian A. Watson

引用次数: 9

Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks. 贝叶斯整合生物先验知识，用贝叶斯网络重构基因调控网络。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2007-01-01

Dirk Husmeier, Adriano V Werhli

引用次数: 0