ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction
dc.contributor.author | Wu, Sitao | |
dc.contributor.author | Zhang, Yang | |
dc.date.accessioned | 2014-03-14T20:12:19Z | |
dc.date.available | 2014-03-14T20:12:19Z | |
dc.date.issued | 2008-10-15 | |
dc.identifier.citation | Wu, S., & Zhang, Y. (2008). ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction. PLoS ONE, 3(10). http://dx.doi.org/10.1371/journal.pone.0003400 | |
dc.identifier.uri | http://hdl.handle.net/1808/13174 | |
dc.description.abstract | We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ~10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0×10−300 (or <1.0×10−148) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10–20°) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling. | |
dc.description.sponsorship | Funding: The project is supported in part by the Alfred P. Sloan Foundation, NSF Career Award 0746198, and NIH grant GM-083107. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | |
dc.publisher | Public Library of Science | |
dc.rights | Copyright: ©2008 Wu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Alpha Helix | |
dc.subject | Data Processing | |
dc.subject | Dihedral Angles | |
dc.subject | Entropy | |
dc.subject | Forecasting | |
dc.subject | Protein structure prediction | |
dc.subject | Proteomic Databases | |
dc.subject | Support Vector Machines | |
dc.title | ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction | |
dc.type | Article | |
kusw.kuauthor | Wu, Sitao | |
kusw.kuauthor | Zhang, Yang | |
kusw.kudepartment | Molecular Biosciences | |
kusw.kudepartment | Bioinformatics | |
kusw.oastatus | na | |
dc.identifier.doi | 10.1371/journal.pone.0003400 | |
kusw.oaversion | Scholarly/refereed, publisher version | |
kusw.oapolicy | This item does not meet KU Open Access policy criteria. | |
dc.rights.accessrights | openAccess |
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Except where otherwise noted, this item's license is described as: Copyright: ©2008 Wu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.