| Autor: |
Hopf TA; Department of Systems Biology, Harvard University, Boston, United States., Schärfe CP; Department of Systems Biology, Harvard University, Boston, United States., Rodrigues JP; Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands., Green AG; Department of Systems Biology, Harvard University, Boston, United States., Kohlbacher O; Applied Bioinformatics, Quantitative Biology Center, University of Tübingen, Tübingen, Germany., Sander C; Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States., Bonvin AM; Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands., Marks DS; Department of Systems Biology, Harvard University, Boston, United States. |
| Abstrakt: |
Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution. |
