Predicting protein structure using hidden Markov models

Predicting protein structure using hidden Markov models
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使用隐马尔可夫模型预测蛋白质结构

DOI:
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发表时间:
1997
期刊:
Proteins: Structure, Function, and Bioinformatics
影响因子:
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通讯作者:
C. Sander
C. Sander
中科院分区:
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文献类型:
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作者:
K. Karplus;Kimmen Sjölander;C. Barrett;M. Cline;D. Haussler;R. Hughey;L. Holm;C. Sander

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我们讨论了基于隐马尔可夫模型的方法如何在CASP 2实验的折叠识别部分中执行。隐马尔可夫模型是为来自蛋白质数据库(PDB)的1,000多个结构的代表性集合构建的。每个CASP 2靶序列针对该HSP 70文库进行评分。此外,为每个靶序列构建HMM,并针对该靶模型对PDB中的所有序列进行评分,两种方法的良好评分表明靶序列与结构同源的概率很高。与CASP 2中用于中等难度靶标的其他方法相比,该方法工作良好,其中PDB中最接近的结构可以与靶标对齐,具有至少15%的残基同一性。Proteins,Suppl.1:134-139,1997。© 1998 Wiley利斯公司
We discuss how methods based on hidden Markov models performed in the fold‐recognition section of the CASP2 experiment. Hidden Markov models were built for a representative set of just over 1,000 structures from the Protein Data Bank (PDB). Each CASP2 target sequence was scored against this library of HMMs. In addition, an HMM was built for each of the target sequences and all of the sequences in PDB were scored against that target model, with a good score on both methods indicating a high probability that the target sequence is homologous to the structure. The method worked well in comparison to other methods used at CASP2 for targets of moderate difficulty, where the closest structure in PDB could be aligned to the target with at least 15% residue identity. Proteins, Suppl. 1:134–139, 1997. © 1998 Wiley‐Liss, Inc.
DOI: --
发表时间: 1995
期刊: Proceedings. International Conference on Intelligent Systems for Molecular Biology
影响因子: --
作者:
S. Eddy
通讯作者: S. Eddy
DOI: 10.1006/jmbi.1994.1104
发表时间: 1994-02-04
影响因子: 5.6
作者:
KROGH, A;BROWN, M;HAUSSLER, D
通讯作者: HAUSSLER, D
DOI: --
发表时间: 1996
期刊: Computer applications in the biosciences : CABIOS
影响因子: --
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DOI: --
发表时间: 1993-07
期刊: Proceedings. International Conference on Intelligent Systems for Molecular Biology
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DOI: 10.1093/protein/9.5.381
发表时间: 1996
期刊: Protein engineering
影响因子: --
作者:
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