A threading-based method for the prediction of DNA-binding proteins with application to the human genome.
Diverse mechanisms for DNA-protein recognition have been elucidated in numerous atomic complex structures from various protein families. These structural data provide an invaluable knowledge base not only for understanding DNA-protein interactions, but also for developing specialized methods that predict the DNA-binding function from protein structure. While such methods are useful, a major limitation is that they require an experimental structure of the target as input. To overcome this obstacle, we develop a threading-based method, DNA-Binding-Domain-Threader (DBD-Threader), for the prediction of DNA-binding domains and associated DNA-binding protein residues. Our method, which uses a template library composed of DNA-protein complex structures, requires only the target protein's sequence. In our approach, fold similarity and DNA-binding propensity are employed as two functional discriminating properties. In benchmark tests on 179 DNA-binding and 3,797 non-DNA-binding proteins, using templates whose sequence identity is less than 30% to the target, DBD-Threader achieves a sensitivity/precision of 56%/86%. This performance is considerably better than the standard sequence comparison method PSI-BLAST and is comparable to DBD-Hunter, which requires an experimental structure as input. Moreover, for over 70% of predicted DNA-binding domains, the backbone Root Mean Square Deviations (RMSDs) of the top-ranked structural models are within 6.5 A of their experimental structures, with their associated DNA-binding sites identified at satisfactory accuracy. Additionally, DBD-Threader correctly assigned the SCOP superfamily for most predicted domains. To demonstrate that DBD-Threader is useful for automatic function annotation on a large-scale, DBD-Threader was applied to 18,631 protein sequences from the human genome; 1,654 proteins are predicted to have DNA-binding function. Comparison with existing Gene Ontology (GO) annotations suggests that approximately 30% of our predictions are new. Finally, we present some interesting predictions in detail. In particular, it is estimated that approximately 20% of classic zinc finger domains play a functional role not related to direct DNA-binding.
Gao
Mu
M
Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA.
Skolnick
Jeffrey
J
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000737:chemistry / Q000235:genetics* / Q000502:physiology; D003627:Data Interpretation, Statistical; D004331:Drosophila melanogaster / Q000235:genetics; D005796:Genes; D015894:Genome, Human; D006801:Humans; D008958:Models, Molecular; D011485:Protein Binding / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods*
D004268:DNA-Binding Proteins