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A simple physical model for binding energy hot spots in protein-protein complexes.

ABSTRACT: Protein-protein recognition plays a central role in most biological processes. Although the structures of many protein-protein complexes have been solved in molecular detail, general rules describing affinity and selectivity of protein-protein interactions do not accurately account for the extremely diverse nature of the interfaces. We investigate the extent to which a simple physical model can account for the wide range of experimentally measured free energy changes brought about by alanine mutation at protein-protein interfaces. The model successfully predicts the results of alanine scanning experiments on globular proteins (743 mutations) and 19 protein-protein interfaces (233 mutations) with average unsigned errors of 0.81 kcal/mol and 1.06 kcal/mol, respectively. The results test our understanding of the dominant contributions to the free energy of protein-protein interactions, can guide experiments aimed at the design of protein interaction inhibitors, and provide a stepping-stone to important applications such as interface redesign.

SUBMITTER: Kortemme T

PROVIDER: S-EPMC137846 | biostudies-literature | 2002 Oct

REPOSITORIES: biostudies-literature

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A simple physical model for binding energy hot spots in protein-protein complexes.

Kortemme Tanja T Baker David D

Proceedings of the National Academy of Sciences of the United States of America 20021015 22

Protein-protein recognition plays a central role in most biological processes. Although the structures of many protein-protein complexes have been solved in molecular detail, general rules describing affinity and selectivity of protein-protein interactions do not accurately account for the extremely diverse nature of the interfaces. We investigate the extent to which a simple physical model can account for the wide range of experimentally measured free energy changes brought about by alanine mut ...[more]

PMID: 12381794

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Project description:BackgroundProtein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods.ResultsHere, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones.ConclusionsPreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .

Dataset Information

A simple physical model for binding energy hot spots in protein-protein complexes.

Publications

A simple physical model for binding energy hot spots in protein-protein complexes.

Similar Datasets

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