Predicting protein-protein binding sites in membrane proteins

Andrew J Bordner

doi:10.1186/1471-2105-10-312

Abstract

BackgroundMany integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.ResultsWe present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed.ConclusionGiven a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures.

Highlights

Many integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function
Even as new techniques are developed to speed up the experimental determination of membrane protein structures, the combinatorial nature of proteinprotein interactions precludes solving the structures of all possible protein complexes from an organism's proteome
There are considerably fewer experimental structures of membrane proteins than nonmembrane proteins, because the predictions are made for individual surface residues there is a sufficient quantity of independent examples for training a Random Forest classifier that gives accurate results

Summary

Introduction

Like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available. Similar to non-membrane proteins, many membrane proteins form complexes in order to carry out their biological function. Structural details of these protein-protein interactions can aid in generating experimentally verifiable mechanistic hypotheses for the relevant complexes and can form a basis for the structure-based discovery of therapeutics to modulate these interactions. Even as new techniques are developed to speed up the experimental determination of membrane protein structures, the combinatorial nature of proteinprotein interactions precludes solving the structures of all possible protein complexes from an organism's proteome

Methods

Results

Conclusion