Limits on alpha-helix prediction with neural network models

S. Hayward, J. F. Collins

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23 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)372-381
Number of pages10
JournalProteins: Structure, Function, and Bioinformatics
Issue number3
Publication statusPublished - Nov 1992

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