EigenTHREADER: analogous protein fold recognition by efficient contact map threading.
EigenTHREADER: analogous protein fold recognition by efficient contact map threading.
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DOI：
10.1093/bioinformatics/btx217
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发表时间：
2017-09-01
期刊：
影响因子：
--
通讯作者：
Jones DT
中科院分区：
文献类型：
--
作者：
Buchan DWA;Jones DT
Protein fold recognition when appropriate, evolutionarily-related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can be detected, fold recognition is a notoriously difficult problem. Here we present EigenTHREADER, a novel fold recognition method capable of identifying folds where no homologous structures can be identified. EigenTHREADER takes a query amino acid sequence, generates a map of intra-residue contacts, and then searches a library of contact maps of known structures. To allow the contact maps to be compared, we use eigenvector decomposition to resolve the principal eigenvectors these can then be aligned using standard dynamic programming algorithms. The approach is similar to the Al-Eigen approach of, but with improvements made both to speed and accuracy. With this search strategy, EigenTHREADER does not depend directly on sequence homology between the target protein and entries in the fold library to generate models. This in turn enables EigenTHREADER to correctly identify analogous folds where little or no sequence homology information is. EigenTHREADER outperforms well-established fold recognition methods such as pGenTHREADER and HHSearch in terms of True Positive Rate in the difficult task of analogous fold recognition. This should allow template-based modelling to be extended to many new protein families that were previously intractable to homology based fold recognition methods. All code used to generate these results and the computational protocol can be downloaded from https://github.com/DanBuchan/eigen_scripts. EigenTHREADER, the benchmark code and the data this paper is based on can be downloaded from: http://bioinfadmin.cs.ucl.ac.uk/downloads/eigenTHREADER/.
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影响因子：
2.9
作者：
Wu S;Zhang Y
通讯作者：
Zhang Y
影响因子：
2.9
作者：
Peng, Jian;Xu, Jinbo
通讯作者：
Xu, Jinbo
影响因子：
5.8
作者：
Söding, J
通讯作者：
Söding, J
影响因子：
3.7
作者：
Kosciolek T;Jones DT
通讯作者：
Jones DT
DOI：
10.1093/bioinformatics/btu739
发表时间：
2015-03-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Suzek BE;Wang Y;Huang H;McGarvey PB;Wu CH;UniProt Consortium
通讯作者：
UniProt Consortium