Abstract
Identifying specific hot spot residues that contribute significantly to the affinity and specificity of protein interactions is a problem of the utmost importance. We present an interactive web server, PredHS, which is based on an effective structure-based hot spot prediction method. The PredHS prediction method integrates many novel structural and energetic features with two types of structural neighborhoods (Euclidian and Voronoi), and combines random forest and sequential backward elimination algorithms to select an optimal subset of features. PredHS achieved the highest performance identifying hot spots compared with other state-of-the-art methods, as benchmarked by using an independent experimentally verified dataset. The input to PredHS is protein structures in the PDB format with at least two chains that form interfaces. Users can visualize their predictions in an interactive 3D viewer and download the results as text files. PredHS is available at http://www.predhs.org.
Highlights
Studies of molecular mechanisms for protein–protein interactions revealed that usually only a small subset of binding interfaces named hot spots account for the majority of binding free energy and are critical for stability and function of protein association [1]
The PredHS web server trains prediction models based on a dataset of 265 experimentally mutated interface residues obtained from ASEdb [3] and the published data of Kortemme and Baker [10], among which 65 are hot spots
To make a fair comparison with other methods, we use an independent test dataset extracted from the Binding Interface Database (BID) database that contains alanine-mutation experiments of a different set of 127 interface residues, of which 39 are identified as hot spots
Summary
Studies of molecular mechanisms for protein–protein interactions revealed that usually only a small subset of binding interfaces named hot spots account for the majority of binding free energy and are critical for stability and function of protein association [1]. Identifying and understanding hot spots and their mechanisms on a large scale would have significant implications for practical applications including drug discovery [2] and protein design. Determined hot spots from alanine scanning mutagenesis experiments have been deposited in Alanine Scanning Energetics Database (ASEdb, [3]). Binding Interface Database (BID) presents experimentally verified hot spots at interfaces collected from literatures [4]. Computational prediction of hot spots has become a practical alternative
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