LAUSR

Many integral membrane proteins, just like their globular counterparts, form either transient or permanent multi-subunit complexes to fulfill specific cellular roles. Although numerous interactions between these proteins have been experientially determined, the structural coverage of the complexes is very low. Therefore, the computational identification of the amino acid residues involved in the interaction interfaces is a crucial step towards the functional annotation of all membrane proteins.Here, we present MBPred, a sequence-based method for predicting the interface residues in transmembrane proteins. An unique feature of our method is that it contains separate random forest models for two different use cases: (a) when the location of transmembrane regions is precisely known from a crystal structure, and (b) when it is predicted from sequence. In stark contrast to the aqueous-exposed protein segments, we found that the interaction sites located in the membrane are not enriched for evolutionary conservation, most likely due to their restricted amino acid composition or their random distribution among buried and exposed residues. On the other hand, residue co-evolution proved to be a very informative feature which has not so far been used for predicting interaction sites in individual proteins. MBPred reaches AUC, precision and recall values of 0.79/0.73, 0.69/0.51 and 0.55/0.48 on the cross-validation and independent test dataset, respectively, thus outperforming the previously published method of Bordner as well as all methods trained on globular proteins. Moreover, we show that for the majority of complete interface patches, the method captures more than 50% of the involved residues.