Data for: Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking

Published: 14 June 2019| Version 1 | DOI: 10.17632/cbk98yrszd.1
Contributors:
Bian Li,
Jeffrey Mendenhall,
Jens Meiler

Description

Protein-protein interactions (PPIs) are an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of amino acid composition and hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs. We also develop a neural network-based method, with an area under the curve for the receiver operating characteristic of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Å for these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

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Institutions

Vanderbilt University

Categories

Structural Biology, Computational Biology

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