HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures
dc.contributor.author | Li, Yunqi | |
dc.contributor.author | Roy, Ambrish | |
dc.contributor.author | Zhang, Yang | |
dc.date.accessioned | 2014-03-19T18:22:20Z | |
dc.date.available | 2014-03-19T18:22:20Z | |
dc.date.issued | 2009-08-20 | |
dc.identifier.citation | Li, Y., Roy, A., & Zhang, Y. (2009). HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures. PLoS ONE, 4(8). http://dx.doi.org/10.1371/journal.pone.0006701 | |
dc.identifier.uri | http://hdl.handle.net/1808/13262 | |
dc.description.abstract | Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD. | |
dc.description.sponsorship | The project is supported in part by the Alfred P. Sloan Foundation, NSF Career Award 0746198, and NIH grant GM-083107 and GM-084222. | |
dc.publisher | Public Library of Science | |
dc.rights | ©2009 Yunqi 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 | Atoms | |
dc.subject | Hydrogen | |
dc.subject | Hydrogen bonding | |
dc.subject | Nuclear magnetic resonance | |
dc.subject | Nucleons | |
dc.subject | Protein structure | |
dc.subject | Protein structure comparison | |
dc.subject | Protein structure prediction | |
dc.title | HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures | |
dc.type | Article | |
kusw.kuauthor | Li, Yunqi | |
kusw.kuauthor | Roy, Ambrish | |
kusw.kuauthor | Zhang, Yang | |
kusw.kudepartment | Molecular Biosciences | |
kusw.oastatus | fullparticipation | |
dc.identifier.doi | 10.1371/journal.pone.0006701 | |
kusw.oaversion | Scholarly/refereed, publisher version | |
kusw.oapolicy | This item meets KU Open Access policy criteria. | |
dc.rights.accessrights | openAccess |
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Except where otherwise noted, this item's license is described as: ©2009 Yunqi 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.