Abstract

Protein-protein complexes play an important role in the physiology and the pathology of cellular functions, and therefore are attractive therapeutic targets. A small subset of residues known as “hot spots”, accounts for most of the protein-protein binding free energy. Computational methods play a critical role in identifying the hotspots on the proteinprotein interface. In this paper, we use a computational alanine scanning method with all-atom force fields for predicting hotspots for 313 mutations in 16 protein complexes of known structures. We studied the effect of force fields, solvation models, and conformational sampling on the hotspot predictions. We compared the calculated change in the protein-protein interaction energies upon mutation of the residues in and near the protein-protein interface, to the experimental change in free energies. The AMBER force field (FF) predicted 86% of the hotspots among the three commonly used FF for proteins, namely, AMBER FF, Charmm27 FF, and OPLS-2005 FF. However, AMBER FF also showed a high rate of false positives, while the Charmm27 FF yielded 74% correct predictions of the hotspot residues with low false positives. Van der Waals and hydrogen bonding energy show the largest energy contribution with a high rate of prediction accuracy, while the desolvation energy was found to contribute little to improve the hot spot prediction. Using a conformational ensemble including limited backbone movement instead of one static structure leads to better predicttion of hotpsots.

Highlights

  • Protein-protein complexes are involved in various physiological processes and are challenging targets for various pathological conditions

  • We have studied the role of implicit solvation methods such as Delphi [30], and Adaptive Poisson-Boltzman solvation APBS [31], in the hotspot predictions

  • Our results show that the Charmm27 FF predicts 74% of correct positives with a low percentage of false positives, while the AMBER force field (FF) performed better for the hot spot prediction (86% correct out of a total of 115 mutations), while showing a high rate of false positives

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Summary

Introduction

Protein-protein complexes are involved in various physiological processes and are challenging targets for various pathological conditions. Identification of hotspots in protein-protein complexes is still challenging both experimentally and computationally, yet important to understand protein-protein binding and complex stability. It has been found that hot spots cannot be unequivocally defined by any single attribute such as location of the residue in the protein complex, charge, protein shape, or hydrophobicity [3,4,5,6]. Alanine scanning mutagenesis has been used successfully on few proteinprotein complexes to probe the effect of these mutations on the stability of the protein-protein complexes to identify hot spots [7,8,9,10,11]. Computational alanine scanning methods that can reliably predict hotspots and quantify the binding free energy is desirable [13,14,15,16]

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