Computational Prediction of Protein Hot Spot Residues

Title: Computational Prediction of Protein Hot Spot Residues

Volume: 18 Issue: 9

Author(s): John Kenneth Morrow and Shuxing Zhang

Affiliation:

Keywords: Protein-protein interactions, hot spot residues, structure-based drug discovery, in silico prediction, alanine scanning, TRAF6

Abstract: Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of proteinprotein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

Cite this article as:

Kenneth Morrow John and Zhang Shuxing, Computational Prediction of Protein Hot Spot Residues, Current Pharmaceutical Design 2012; 18 (9) . https://dx.doi.org/10.2174/138161212799436412