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Predicting DNA-Binding Proteins and Binding Residues by Complex Structure Prediction and Application to Human Proteome

Overview of attention for article published in PLOS ONE, May 2014
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Title
Predicting DNA-Binding Proteins and Binding Residues by Complex Structure Prediction and Application to Human Proteome
Published in
PLOS ONE, May 2014
DOI 10.1371/journal.pone.0096694
Pubmed ID
Authors

Huiying Zhao, Jihua Wang, Yaoqi Zhou, Yuedong Yang

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.

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The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 6%
Unknown 31 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 45%
Researcher 4 12%
Professor 3 9%
Other 2 6%
Lecturer > Senior Lecturer 1 3%
Other 2 6%
Unknown 6 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 27%
Biochemistry, Genetics and Molecular Biology 7 21%
Computer Science 5 15%
Engineering 2 6%
Mathematics 1 3%
Other 2 6%
Unknown 7 21%