Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach
We present different means of classifying protein structure. One is made rigorous by mathematical knot invariants that coincide reasonably well with ordinary graphical fold classification and another classification is by packing analysis. Furthermore when constructing our mathematical fold classific...
| Main Authors: | , , |
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| Format: | Journal Article |
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Springer
2007
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| Online Access: | http://hdl.handle.net/20.500.11937/39498 |
| _version_ | 1848755607221305344 |
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| author | Ramnarayan, K. Bohr, H. Jalkanen, Karl |
| author_facet | Ramnarayan, K. Bohr, H. Jalkanen, Karl |
| author_sort | Ramnarayan, K. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We present different means of classifying protein structure. One is made rigorous by mathematical knot invariants that coincide reasonably well with ordinary graphical fold classification and another classification is by packing analysis. Furthermore when constructing our mathematical fold classifications, we utilize standard neural network methods for predicting protein fold classes from amino acid sequences. We also make an analysis of the redundancy of the structural classifications in relation to function and ligand binding. Finally we advocate the use of combining the measurement of the VA, VCD, Raman, ROA, EA and ECD spectra with the primary sequence as a way to improve both the accuracy and reliability of fold class prediction schemes. |
| first_indexed | 2025-11-14T08:58:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-39498 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:58:59Z |
| publishDate | 2007 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-394982019-02-19T05:35:18Z Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach Ramnarayan, K. Bohr, H. Jalkanen, Karl Knot theory vibrational spectroscopy neural networks We present different means of classifying protein structure. One is made rigorous by mathematical knot invariants that coincide reasonably well with ordinary graphical fold classification and another classification is by packing analysis. Furthermore when constructing our mathematical fold classifications, we utilize standard neural network methods for predicting protein fold classes from amino acid sequences. We also make an analysis of the redundancy of the structural classifications in relation to function and ligand binding. Finally we advocate the use of combining the measurement of the VA, VCD, Raman, ROA, EA and ECD spectra with the primary sequence as a way to improve both the accuracy and reliability of fold class prediction schemes. 2007 Journal Article http://hdl.handle.net/20.500.11937/39498 10.1007/s00214-007-0285-7 Springer fulltext |
| spellingShingle | Knot theory vibrational spectroscopy neural networks Ramnarayan, K. Bohr, H. Jalkanen, Karl Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach |
| title | Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach |
| title_full | Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach |
| title_fullStr | Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach |
| title_full_unstemmed | Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach |
| title_short | Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach |
| title_sort | classification of protein fold classes by knot theory and prediction of folds by neural networks: a combined theoretical and experimental approach |
| topic | Knot theory vibrational spectroscopy neural networks |
| url | http://hdl.handle.net/20.500.11937/39498 |