title: Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.
abstract: As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.
authors.affiliation: School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, United States of America; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America; QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
authors.forename: Huiying
authors.initials: H
authors.lastname: Zhao
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America; Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China.
authors.forename: Jihua
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, United States of America; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America; Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China; Institute for Glycomics and School of Information and Communication Technique, Griffith University, Southport, Queensland, Australia.
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, United States of America; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America; Institute for Glycomics and School of Information and Communication Technique, Griffith University, Southport, Queensland, Australia.
authors.forename: Yuedong
authors.initials: Y
authors.lastname: Yang
mesh_terms: D001665:Binding Sites; D004247:DNA / Q000378:metabolism*; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism*; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D020543:Proteome / Q000737:chemistry / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
chemical_list: D004268:DNA-Binding Proteins; D020543:Proteome; D004247:DNA