Protein secondary structure prediction based on position-specific scoring matrices
Jones, D. T.. (1999) Protein secondary structure prediction based on position-specific scoring matrices. Journal of Molecular Biology, Vol.292 (No.2). pp. 195-202. ISSN 0022-2836Full text not available from this repository.
Official URL: http://dx.doi.org/10.1006/jmbi.1999.3091
A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST. Despite the simplicity and convenience of the approach used, the results are found to be superior to those produced by other methods, including the popular PHD method according to our own benchmarking results and the results from the recent Critical Assessment of Techniques for Protein Structure Prediction experiment (CASP3), where the method was evaluated by stringent blind testing. Using a new testing set based on a set of 187 unique folds, and three-way cross-validation based on structural similarity criteria rather than sequence similarity criteria used previously (no similar folds were present in both the testing and training sets) the method presented here (PSIPRED) achieved an average Q(3) score of between 76.5% to 78.3% depending on the precise definition of observed secondary structure used, which is the highest published score for any method to date. Given the success of the method in CASP3, it is reasonable to be confident that the evaluation presented here gives a fair indication of the performance of the method in general. (C) 1999 Academic Press.
|Item Type:||Journal Article|
|Subjects:||Q Science > QD Chemistry|
|Divisions:||Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010)|
|Journal or Publication Title:||Journal of Molecular Biology|
|Date:||17 September 1999|
|Number of Pages:||8|
|Page Range:||pp. 195-202|
|Access rights to Published version:||Restricted or Subscription Access|
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