Using structural motif descriptors for sequence-based binding site prediction

Background: Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites. Results: We generate descriptors for 740 classified types of protein-protein binding sites and for more than 3,000 protein-ligand binding sites. Cross validation reveals that two thirds of the PPI descriptors are sufficiently conserved and significant enough to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally id...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Andreas Henschel, Christof Winter, Wan Kyu Kim, Michael Schroeder
• Keywords: Binding Site, BMCBI 2007, Many Protein Sequences, Protein-ligand Interactions