NDLI: Automatic prediction of functional site regions in low-resolution protein structures

Content Provider	IEEE Xplore Digital Library
Author	Jaspreet Singh Sodhi McGuffin, L.J. Bryson, K. Ward, J.J. Wernisch, L. Jones, D.T.
Copyright Year	2004
Description	Author affiliation: Univ. Coll. London, UK (Jaspreet Singh Sodhi; McGuffin, L.J.; Bryson, K.; Ward, J.J.; Wernisch, L.; Jones, D.T.)
Abstract	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.
Starting Page	702
Ending Page	703
File Size	325646
Page Count	2
File Format	PDF
ISBN	0769521940
DOI	10.1109/CSB.2004.1332551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Sequences Biological system modeling Design methodology Genomics DNA Predictive models Robustness Bioinformatics Biological information theory
Content Type	Text
Resource Type	Article

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