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FRAGSION: ultra-fast protein fragment library generation by IOHMM sampling
Author(s) -
Debswapna Bhattacharya,
Badri Adhikari,
Jilong Li,
Jianlin Cheng
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw067
Subject(s) - fragment (logic) , computer science , executable , robustness (evolution) , source code , hidden markov model , protein structure prediction , sampling (signal processing) , algorithm , data mining , artificial intelligence , protein structure , programming language , biochemistry , filter (signal processing) , computer vision , gene , chemistry , physics , nuclear magnetic resonance
Speed, accuracy and robustness of building protein fragment library have important implications in de novo protein structure prediction since fragment-based methods are one of the most successful approaches in template-free modeling (FM). Majority of the existing fragment detection methods rely on database-driven search strategies to identify candidate fragments, which are inherently time-consuming and often hinder the possibility to locate longer fragments due to the limited sizes of databases. Also, it is difficult to alleviate the effect of noisy sequence-based predicted features such as secondary structures on the quality of fragment.

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