Name
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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CSB.2004.1332551 | CSB |
Keywords | DocType | ISBN |
functional site regions,automatic prediction,automatic site detection method,functionally important site,genetics,low resolution,proteins,structural prediction programs,low resolution structural models,accurate detection,world-wide structural genomics,physiological models,biology computing,molecular biophysics,molecular configurations,structural model,funcsite,effective metal site detection,functional site,mgenthreader,low-resolution protein structures,structural prediction program,world-wide structural genomics initiative,effective site detection,backbone residue positions,metal site detection,neural nets,neural network classifiers,structural genomics,protein structure | Conference | 0-7695-2194-0 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaspreet Singh Suri | 1 | 337 | 29.90 |
| Liam J. McGuffin | 2 | 688 | 60.76 |
| Kevin Bryson | 3 | 482 | 41.87 |
| Jonathan J Ward | 4 | 193 | 15.94 |
| Lorenz Wernisch | 5 | 260 | 24.34 |
| David T. Jones | 6 | 546 | 35.06 |