Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins.
BACKGROUND
Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.


RESULTS
In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.


CONCLUSIONS
Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.
Dong
Qiwen
Q
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. qwdong@insun.hit.edu.cn
Wang
Xiaolong
X
Lin
Lei
L
Guan
Yi
Y
D000596:Amino Acids / Q000235:genetics / Q000378:metabolism*; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D019143:Evolution, Molecular; D008958:Models, Molecular; D046912:Multiprotein Complexes / Q000737:chemistry* / Q000235:genetics; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
D000596:Amino Acids; D046912:Multiprotein Complexes; D011506:Proteins