ISIS: interaction sites identified from sequence.
BACKGROUND
Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein-protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues.


RESULTS
Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein-protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein-protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone.
Ofran
Yanay
Y
CUBIC & North-East Structural Genomics Consortium, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. yanay.ofran@columbia.edu
Rost
Burkhard
B
D000596:Amino Acids / Q000737:chemistry*; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D010363:Pattern Recognition, Automated / Q000379:methods*; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure; D020539:Sequence Analysis, Protein / Q000379:methods*
D000596:Amino Acids; D011506:Proteins