Title:
From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions
From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions
| dc.contributor.author | Gao, Mu | |
| dc.contributor.author | Skolnick, Jeffrey | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for the Study of Systems Biology | |
| dc.date.accessioned | 2009-05-14T19:04:27Z | |
| dc.date.available | 2009-05-14T19:04:27Z | |
| dc.date.issued | 2009-04-03 | |
| dc.description | ©2009 Gao, Skolnick. 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. | en |
| dc.description | doi:10.1371/journal.pcbi.1000341 | |
| dc.description.abstract | DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein. | en |
| dc.identifier.citation | PLoS Computational Biology, 5(3): e1000341 | en |
| dc.identifier.issn | 1553-7358 | |
| dc.identifier.uri | http://hdl.handle.net/1853/27923 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en |
| dc.publisher.original | Public Library of Science | |
| dc.subject | Computational modeling | |
| dc.subject | DNA binding sites | |
| dc.subject | DNA–protein interactions | |
| dc.subject | Protein structure modeling | |
| dc.subject | Protein structure prediction | |
| dc.title | From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions | en |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Skolnick, Jeffrey | |
| local.contributor.corporatename | College of Sciences | |
| local.contributor.corporatename | School of Biological Sciences | |
| local.contributor.corporatename | Center for the Study of Systems Biology | |
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