Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-42-1327
- Type
- D - Journal article
- DOI
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10.1186/s12859-020-3491-0
- Title of journal
- BMC Bioinformatics
- Article number
- -
- First page
- 170
- Volume
- 21
- Issue
- -
- ISSN
- 1471-2105
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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-
- Research group(s)
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- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper exploits the uneven complexity of the sequence-structure relationship of short fragments to improve ab initio fragment-based protein structure prediction. An implementation based on a state-of-the-art predictor, Rosetta, demonstrates that, by customising the number of candidates used during the fragment insertion process according to the expected complexity of predicted local secondary structures, model quality is either improved significantly or maintained while reducing processing times by 10. This research is significant as it can be directly applied by the many Rosetta users - over 10,000 licenses are in use -, and could be adapted for other fragment-base predictors.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -