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In this paper, the effect of training a two-level neural network
algorithm for protein secondary structure prediction with the same
sequences presented as different alignment profiles has been
investigated. The general conclusions are:
- 1.
- By appropriate selection of database searching method, alignment
algorithm and scoring scheme, the prediction accuracy for the same
sequences using the same basic algorithm is improved by 7% points
from 69.5% to 76.4%.
- 2.
- Solvent accessibility prediction accuracy has been improved by
1.2% to 76.2% for a two state model, and has been extended to
include specific prediction of the 25, 5 and 0% relative
accessibility states.
- 3.
- Confidence in prediction has been improved. Residues predicted with
a confidence of 5 and greater, will be on average 84% accurate and
cover 68% of residues. The average prediction accuracy per protein
is 76.4% with a standard deviation of 8.4%.
- 4.
- Optimisation of learning parameters and training algorithm, may
further extend the accuracy of the secondary structure prediction
method.
- 5.
- In the years from 1993 to 1999, prediction accuracy has improved
from 70.6% [17] to over 76% (this work). Most of this
improvement has come from more sophisticated use of sequence
alignments rather than enhancements to the neural network algorithm.
- 6.
- The most dramatic improvements in prediction accuracy have come from
the use of PSIBLAST position specific scoring profiles in preference
to profiles derived from global multiple alignment methods such as
CLUSTALW and AMPS.
Given the expansion of structural genomics projects, which aim to
solve protein structures much more rapidly, the exploitation of these
data will only extend the ability to predict protein structure ever
more accurately.
Next: Availability
Up: No Title
Previous: Reliability scoring
James Cuff
2001-06-29