Abstract
Background: Many protein sequences are still poorly annotated. Functional characterization of a protein often is 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 compile sequential segments that constitute one structural features of an interaction site into one profile Hidden Markov Model. 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 approximately 70% of the PPI-descriptors are sufficiently conserved and significant to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally identify the interface residues. Finally we test ligand-binding descriptors for the case of ATP. From sequences with Gene Ontology-annotation "ATP-binding", we recall 46% with a precision of 82%, whereas Prosite's P-loop motif recognizes 41% to the expense of a much larger amount of false positives (precision: 52%). Conclusions: The automatically generated descriptors are a useful collection, complementary to known Prosite/InterPro motifs. They are a tool to predict protein-protein as well as protein-ligand interactions along with their binding site residues for proteins where merely sequence information is available.

URL:
http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2007-028

BibTeX:

@article{REWERSE-RP-2007-028,
	author = {Andreas Henschel and Christof Winter and Wan Kyu Kim and Michael Schroeder},
	title = {Using structural motif descriptors for sequence-based binding
site prediction},
	journal = {BMC Bioinformatics},
	year = {2007},
	volume = {8(Suppl 4)},
	number = {S5},
	url = {http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2007-028}
}