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. 2015 Dec 1;31(23):3850-2.
doi: 10.1093/bioinformatics/btv441. Epub 2015 Jul 30.

HHalign-Kbest: Exploring Sub-Optimal Alignments for Remote Homology Comparative Modeling


HHalign-Kbest: Exploring Sub-Optimal Alignments for Remote Homology Comparative Modeling

Jinchao Yu et al. Bioinformatics. .


Motivation: The HHsearch algorithm, implementing a hidden Markov model (HMM)-HMM alignment method, has shown excellent alignment performance in the so-called twilight zone (target-template sequence identity with ∼20%). However, an optimal alignment by HHsearch may contain small to large errors, leading to poor structure prediction if these errors are located in important structural elements.

Results: HHalign-Kbest server runs a full pipeline, from the generation of suboptimal HMM-HMM alignments to the evaluation of the best structural models. In the HHsearch framework, it implements a novel algorithm capable of generating k-best HMM-HMM suboptimal alignments rather than only the optimal one. For large proteins, a directed acyclic graph-based implementation reduces drastically the memory usage. Improved alignments were systematically generated among the top k suboptimal alignments. To recognize them, corresponding structural models were systematically generated and evaluated with Qmean score. The method was benchmarked over 420 targets from the SCOP30 database. In the range of HHsearch probability of 20-99%, average quality of the models (TM-score) raised by 4.1-16.3% and 8.0-21.0% considering the top 1 and top 10 best models, respectively.

Availability and implementation: (source code and server).


Supplementary information: Supplementary data are available at Bioinformatics online.

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