Predicting binding sites of hydrolase-inhibitor complexes by combining several methods.
BACKGROUND
Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.


RESULTS
In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods.


CONCLUSIONS
We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.
Sen
Taner Z
TZ
L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu
Kloczkowski
Andrzej
A
Jernigan
Robert L
RL
Yan
Changhui
C
Honavar
Vasant
V
Ho
Kai-Ming
KM
Wang
Cai-Zhuang
CZ
Ihm
Yungok
Y
Cao
Haibo
H
Gu
Xun
X
Dobbs
Drena
D
D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004791:Enzyme Inhibitors / Q000737:chemistry*; D006867:Hydrolases / Q000037:antagonists & inhibitors* / Q000737:chemistry*; D008958:Models, Molecular; D015233:Models, Statistical; D015394:Molecular Structure; D010363:Pattern Recognition, Automated; D010455:Peptides / Q000737:chemistry; D010802:Phylogeny; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry; D013305:Streptomyces griseus / Q000378:metabolism
D001426:Bacterial Proteins; D004791:Enzyme Inhibitors; D010455:Peptides; D011506:Proteins; D006867:Hydrolases