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This article is part of the supplement: SNP-SIG 2011: Identification and annotation of SNPs in the context of structure, function and disease

Open Access Proceedings

Predict impact of single amino acid change upon protein structure

Christian Schaefer12* and Burkhard Rost12345

Author affiliations

1 TUM, Bioinformatics - I12, Informatik, Boltzmannstr. 3, 85748 Garching, Germany

2 TUM Graduate School of Information Science in Health (GSISH), Boltzmannstr. 11, 85748 Garching, Germany

3 Institute of Advanced Study (IAS), TUM, Boltzmannstr. 3, 85748 Garching, Germany

4 New York Consortium on Membrane Protein Structure (NYCOMPS), TUM Bioinformatics, Boltzmannstr. 3, 85748 Garching, Germany

5 Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY 10032, USA

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Citation and License

BMC Genomics 2012, 13(Suppl 4):S4  doi:10.1186/1471-2164-13-S4-S4

Published: 18 June 2012

Abstract

Background

Amino acid point mutations (nsSNPs) may change protein structure and function. However, no method directly predicts the impact of mutations on structure. Here, we compare pairs of pentamers (five consecutive residues) that locally change protein three-dimensional structure (3D, RMSD>0.4Å) to those that do not alter structure (RMSD<0.2Å). Mutations that alter structure locally can be distinguished from those that do not through a machine-learning (logistic regression) method.

Results

The method achieved a rather high overall performance (AUC>0.79, two-state accuracy >72%). This discriminative power was particularly unexpected given the enormous structural variability of pentamers. Mutants for which our method predicted a change of structure were also enriched in terms of disrupting stability and function. Although distinguishing change and no change in structure, the new method overall failed to distinguish between mutants with and without effect on stability or function.

Conclusions

Local structural change can be predicted. Future work will have to establish how useful this new perspective on predicting the effect of nsSNPs will be in combination with other methods.