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.
Highlights
The completion of thousands of proteome projects has led to an explosive increase in number of proteins with unknown functions
A mediumresolution function prediction is to predict the region in a protein that binds with DNA (DNA-binding residues or DNA-binding interface regions)
The method achieved a Matthews correlation coefficient (MCC) value of 0.77 that is higher than the best structure-based technique (DDNA3O)
Summary
The completion of thousands of proteome projects has led to an explosive increase in number of proteins with unknown functions. The comprehensive Uniprot database [1] contains 107 protein sequences and, yet, less than 5% of these sequences have annotated functions from Gene Ontology Annotation database [2]. This gap between the number of sequences and the number of sequences with annotations is widening rapidly as inexpensive and more efficient generation sequencing techniques become available. Function prediction of DNA-binding can be classified into three levels of resolution (low, medium and high). A low-resolution function prediction is a simple two-state prediction whether or not a protein binds to DNA. A high-resolution function prediction is to predict the complex structure between DNA and a target protein of unknown function
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