MOTIVATION: Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance. RESULTS: We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions. AVAILABILITY AND IMPLEMENTATION: Source code and test data are available at www.bif.wur.nl/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution.

MOTIVATION: Transcription factor interactions are the cornerstone of combinatorial control, which is a crucial aspect of the gene regulatory system. Understanding and predicting transcription factor interactions based on their sequence alone is difficult since they are often part of families of factors sharing high sequence identity. Given the scarcity of experimental data on interactions compared to available sequence data, however, it would be most useful to have accurate methods for the prediction of such interactions. RESULTS: We present a method consisting of a Random Forest-based feature-selection procedure that selects relevant motifs out of a set found using a correlated motif search algorithm. Prediction accuracy for several transcription factor families (bZIP, MADS, homeobox and forkhead) reaches 60-90%. In addition, we identified those parts of the sequence that are important for the interaction specificity, and show that these are in agreement with available data. We also used the predictors to perform genome-wide scans for interaction partners and recovered both known and putative new interaction partners.