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Statistical analysis of physical-chemical properties and prediction of protein-protein interfaces

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Abstract

We have developed a fully automated method, InterProSurf, to predict interacting amino acid residues on protein surfaces of monomeric 3D structures. Potential interacting residues are predicted based on solvent accessible surface areas, a new scale for interface propensities, and a cluster algorithm to locate surface exposed areas with high interface propensities. Previous studies have shown the importance of hydrophobic residues and specific charge distribution as characteristics for interfaces. Here we show differences in interface and surface regions of all physical chemical properties of residues as represented by five quantitative descriptors. In the current study a set of 72 protein complexes with known 3D structures were analyzed to obtain interface propensities of residues, and to find differences in the distribution of five quantitative descriptors for amino acid residues. We also investigated spatial pair correlations of solvent accessible residues in interface and surface areas, and compared log-odds ratios for interface and surface areas. A new scoring method to predict potential functional sites on the protein surface was developed and tested for a new dataset of 21 protein complexes, which were not included in the original training dataset. Empirically we found that the algorithm achieves a good balance in the accuracy of precision and sensitivity by selecting the top eight highest scoring clusters as interface regions. The performance of the method is illustrated for a dimeric ATPase of the hyperthermophile, Methanococcus jannaschii, and the capsid protein of Human Hepatitis B virus. An automated version of the method can be accessed from our web server at http://curie.utmb.edu/prosurf.html.

Comparison of the experimentally observed interface (blue) of the dimeric ATPase of the hyperthermophile Methanococcus jannaschii with the prediction of InterProSurf using a cluster (red) and patch (yellow) algorithm.

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Acknowledgements

This work was supported by National Institutes of Health Grants R21 AI055746 and R01 AI064913.

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555-0857, USA

    Surendra S. Negi & Werner Braun

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  1. Surendra S. Negi
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  2. Werner Braun
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Correspondence to Werner Braun.

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Negi, S.S., Braun, W. Statistical analysis of physical-chemical properties and prediction of protein-protein interfaces. J Mol Model 13, 1157–1167 (2007). https://doi.org/10.1007/s00894-007-0237-0

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  • DOI: https://doi.org/10.1007/s00894-007-0237-0

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