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Hayward, S. 
ORCID: https://orcid.org/0000-0001-6959-2604 and Collins, J. F.
(1992)
Limits on alpha-helix prediction with neural network models.
Proteins: Structure, Function, and Bioinformatics, 14 (3).
pp. 372-381.
ISSN 0887-3585
Abstract
Using a backpropagation neural network model we have found a limit for secondary structure prediction from local sequence. By including only sequences from whole α-helix and non-α-helixstructures in our training and test sets—sequences spanning boundaries between these two structures were excluded—it was possible to investigate directly the relationship between sequence and structure for α-helix. A group of non-α-helix sequences, that was disrupting overall prediction success, was indistinguishable to the network from α-helix sequences. These sequences were found to occur at regions adjacent to the termini of α-helices with statistical significance, suggesting that potentially longer α-helices are disrupted by global constraints. Some of these regions spanned more than 20 residues. On these whole structure sequences, 10 residues in length, a comparatively high prediction success of 78% with a correlation coefficient of 0.52 was achieved. In addition, the structure of the input space, the distribution of β-sheet in this space, and the effect of segment length were also investigated.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Computational Biology |
| Depositing User: | EPrints Services |
| Date Deposited: | 01 Oct 2010 13:41 |
| Last Modified: | 24 Sep 2024 10:34 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/3044 |
| DOI: | 10.1002/prot.340140306 |
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