Refinement of The Prediction Methods of Signal Peptides for
The Genome Analyses of Saccharomyces cerevisiae and Bacillus subtilis
Refinement of The Prediction Methods of Signal Peptides for
The Genome Analyses of Saccharomyces cerevisiae and
Bacillus subtilis
Kenta Nakai (nakai@imcb.osaka-u.ac.jp)
Institute for Molecular and Cellular Biology,
Osaka University
1-3 Yamada-oka, Suita 565 Japan
Abstract
Since signal peptides play a crucial role for specifying the
in-vivo fate of proteins, prediction of
their existence is important for the
characterization of ORFs of unknown function. To make such
predictions as reliable as possible, the features of signal
peptides of two important model organisms, Saccharomyces
cerevisiae and Bacillus subtilis, were examined and the
accuracy of current prediction methods was refined
using these data. Direct optimization of the threshold
values of existing methods significantly raised the predictability but
the variables that were most effective for improvement were
different in these two organisms. In yeast, the maximum
hydrophobicity value of an 8-residue segment mainly contributed to
raising the predictability to 98.5% when estimated by the cross
validation procedure. In Bacillus species, the length of uncharged
segment and the charges in the N-terminal region (net charge and negative
charge) were combined to give a prediction accuracy of 98.2%
although the data size was relatively small in this case.
