Automatic prediction of functional site regions in low-resolution protein structures
Sodhi, J. S., McGuffin, L. J., Bryson, K., Ward, J. J., Wernisch, L. and Jones, D. T. (2004) Automatic prediction of functional site regions in low-resolution protein structures. In: Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004. IEEE, pp. 702-703.
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To link to this article DOI: 10.1109/CSB.2004.1332551
World-wide structural genomics initiatives are rapidly accumulating structures for which limited functional information is available. Additionally, state-of-the art structural prediction programs are now capable of generating at least low resolution structural models of target proteins. Accurate detection and classification of functional sites within both solved and modelled protein structures therefore represents an important challenge. We present a fully automatic site detection method, FuncSite, that uses neural network classifiers to predict the location and type of functionally important sites in protein structures. The method is designed primarily to require only backbone residue positions without the need for specific side-chain atoms to be present. In order to highlight effective site detection in low resolution structural models FuncSite was used to screen model proteins generated using mGenTHREADER on a set of newly released structures. We found effective metal site detection even for moderate quality protein models illustrating the robustness of the method.
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