FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information. Issue 8 (August 2016)
- Record Type:
- Journal Article
- Title:
- FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information. Issue 8 (August 2016)
- Main Title:
- FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information
- Authors:
- Yaseen, Ashraf
Nijim, Mais
Williams, Brandon
Qian, Lei
Li, Min
Wang, Jianxin
Li, Yaohang - Abstract:
- Abstract Background The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions. Results In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context. The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor. Conclusions Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called "FLEXc" and available onlineAbstract Background The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions. Results In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context. The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor. Conclusions Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called "FLEXc" and available online at:http://hpcr.cs.odu.edu/flexc . … (more)
- Is Part Of:
- BMC bioinformatics. Volume 17:Issue 8(2016)
- Journal:
- BMC bioinformatics
- Issue:
- Volume 17:Issue 8(2016)
- Issue Display:
- Volume 17, Issue 8 (2016)
- Year:
- 2016
- Volume:
- 17
- Issue:
- 8
- Issue Sort Value:
- 2016-0017-0008-0000
- Page Start:
- 593
- Page End:
- 600
- Publication Date:
- 2016-08
- Subjects:
- Bioinformatics -- Periodicals
Computational biology -- Periodicals
570.285 - Journal URLs:
- http://www.biomedcentral.com/bmcbioinformatics/ ↗
http://www.pubmedcentral.nih.gov/tocrender.fcgi?journal=13 ↗
http://link.springer.com/ ↗ - DOI:
- 10.1186/s12859-016-1117-3 ↗
- Languages:
- English
- ISSNs:
- 1471-2105
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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British Library HMNTS - ELD Digital store - Ingest File:
- 10044.xml