Customised fragments libraries for protein structure prediction based on structural class annotations
- Submitting institution
-
Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-45-1374
- Type
- D - Journal article
- DOI
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10.1186/s12859-015-0576-2
- Title of journal
- BMC Bioinformatics
- Article number
- -
- First page
- 1
- Volume
- 16
- Issue
- -
- ISSN
- 1471-2105
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- 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
-
-
- Research group(s)
-
-
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes usage of structural class constraints for ab initio fragment-based protein structure prediction to decrease the size of the conformation search space. An implementation (“Rosetta_at_Kingston”), based on the state-of-the-art predictor Rosetta, demonstrated it enhances significantly the quality of Rosetta’s models for the same number of decoys. Among the 14 assessed predictions it made in the 2014 worldwide experiment for protein structure prediction (CASP11), 6 proved superior (average GDT_TS score: +25%) to those of the two official Rosetta groups, despite generating much fewer decoys (<10%). Moreover, three models scored higher than all its fragment-based competitors’.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -