FRAGSION : ultra-fast protein fragment library generation by IOHMM sampling
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissionsoup.com..
MOTIVATION: 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.
RESULTS: Here, we present FRAGSION, a database-free method to efficiently generate protein fragment library by sampling from an Input-Output Hidden Markov Model. FRAGSION offers some unique features compared to existing approaches in that it (i) is lightning-fast, consuming only few seconds of CPU time to generate fragment library for a protein of typical length (300 residues); (ii) can generate dynamic-size fragments of any length (even for the whole protein sequence) and (iii) offers ways to handle noise in predicted secondary structure during fragment sampling. On a FM dataset from the most recent Critical Assessment of Structure Prediction, we demonstrate that FGRAGSION provides advantages over the state-of-the-art fragment picking protocol of ROSETTA suite by speeding up computation by several orders of magnitude while achieving comparable performance in fragment quality.
AVAILABILITY AND IMPLEMENTATION: Source code and executable versions of FRAGSION for Linux and MacOS is freely available to non-commercial users at http://sysbio.rnet.missouri.edu/FRAGSION/ It is bundled with a manual and example data.
CONTACT: chengjimissouri.edu.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2016 |
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| Erschienen: |
2016 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:32 |
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| Enthalten in: |
Bioinformatics (Oxford, England) - 32(2016), 13 vom: 01. Juli, Seite 2059-61 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bhattacharya, Debswapna [VerfasserIn] |
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| Anmerkungen: |
Date Completed 21.08.2017 Date Revised 02.12.2018 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1093/bioinformatics/btw067 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Date Revised 02.12.2018 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a MOTIVATION: 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 | ||
| 520 | |a RESULTS: Here, we present FRAGSION, a database-free method to efficiently generate protein fragment library by sampling from an Input-Output Hidden Markov Model. FRAGSION offers some unique features compared to existing approaches in that it (i) is lightning-fast, consuming only few seconds of CPU time to generate fragment library for a protein of typical length (300 residues); (ii) can generate dynamic-size fragments of any length (even for the whole protein sequence) and (iii) offers ways to handle noise in predicted secondary structure during fragment sampling. On a FM dataset from the most recent Critical Assessment of Structure Prediction, we demonstrate that FGRAGSION provides advantages over the state-of-the-art fragment picking protocol of ROSETTA suite by speeding up computation by several orders of magnitude while achieving comparable performance in fragment quality | ||
| 520 | |a AVAILABILITY AND IMPLEMENTATION: Source code and executable versions of FRAGSION for Linux and MacOS is freely available to non-commercial users at http://sysbio.rnet.missouri.edu/FRAGSION/ It is bundled with a manual and example data | ||
| 520 | |a CONTACT: chengjimissouri.edu | ||
| 520 | |a SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 7 | |a Proteins |2 NLM | |
| 700 | 1 | |a Adhikari, Badri |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jilong |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Jianlin |e verfasserin |4 aut | |
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