Inter-helical Residue Contact Prediction in $\alpha$-Helical Transmembrane Proteins Using Structural Features

Abstract

Residue contact maps offer a 2-d, reduced representation of 3-d protein structures and constitute a structural constraint and scaffold in structural modeling. Precise residue contact maps are not only helpful as an intermediate step towards generating effective 3-d protein models, but also useful in their own right in identifying binding sites and hence providing insights about a protein’s functions. Indeed, many computational methods have been developed to predict residue contacts using a variety of features based on sequence, physio-chemical properties, and co-evolutionary information. In this work, we set to explore the use of structural information for predicting inter-helical residue contact in transmembrane proteins. Specifically, we extract structural information from a neighborhood around a residue pair of interest and train a classifier to determine whether the residue pair is a contact point or not. To make the task practical, we avoid using the 3-d coordinates directly, instead we extract features such as relative distances and angles. Further, we exclude any structural information of the residue pair of interest from the input feature set in training and testing of the classifier. We compare our method to a state-of-the-art method that uses non-structural information on a benchmark data set. The results from experiments on held out datasets show that the our method achieves above 90% precision for top L/2 and L inter-helical contacts, significantly outperforming the state-of-the-art method and may serve as an upper bound on the performance when using non-structural information. Further, we evaluate the robustness of our method by injecting Gaussian normal noise into PDB coordinates and hence into our derived features. We find that our model’s performance is robust to high noise levels.