Sequence-Based Prediction of Protein-Protein Binding Residues in Alpha-Helical Membrane Proteins

Abstract

A specific number of chains form alpha-helical membrane protein complexes in order to realize the biochemical function, i.e. as gateways to decide whether specific substances can be transported across the membrane or not. However, few structures of membrane proteins have been solved. The knowledge of protein-protein binding residues can help biologists figure out how the function works and solve the 3D structures.

We present a novel, sequence-based method to predict protein-protein binding residues from primary protein sequences by machine learning classifiers. We use a support vector regression model to predict relative solvent accessibility by features based on sequences, including position specific scoring matrix, conserved score, z-coordinate prediction, second structure prediction, physical parameter and sequence length. Afterwards, combining features mentioned above with the predicted solvent accessibility, we use ensemble support vector machines to predict protein-protein binding residues. To the best of our knowledge, there is no method to predict protein-protein binding residues in alpha-helical membrane proteins. Our method outperforms MAdaBoost successfully used in predicting protein-ligand binding residues and random forest used in protein-protein binding residues from surface residues. We also assess the importance of each individual type of features. PSSM profile and conserved score are shown to be more effective to predict protein-protein binding residues in alpha-helical membrane proteins.