Protein-protein interaction hotspots carved into sequences.

TitleProtein-protein interaction hotspots carved into sequences.
Publication TypeJournal Article
Year of Publication2007
AuthorsOfran, Y, Rost, B
JournalPLoS Comput Biol
Date Published2007 Jul
KeywordsAmino Acid Sequence, Amino Acids, Binding Sites, Databases, Protein, Knowledge Bases, Models, Molecular, Multiprotein Complexes, Neural Networks (Computer), Predictive Value of Tests, Protein Binding, Protein Conformation, Protein Interaction Mapping, Proteins, Proteomics, Sequence Analysis, Protein, User-Computer Interface

Protein-protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein-protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as "hotspots," these essential residues are defined as residues that impede protein-protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein-protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein-protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at

Alternate JournalPLoS Comput. Biol.
PubMed ID17630824
PubMed Central IDPMC1914369
Grant List2-R01-LM007329 / LM / NLM NIH HHS / United States
R01-GM64633 / GM / NIGMS NIH HHS / United States