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Signal Peptides 311
14.5.1.1 Weight Matrix Methods
Various methods have been developed for the computational detection of molecular
signals. When the signal to be detected is highly conserved, search of its consensus
sequence, i.e., the pattern represented by regular expression such as NX(S/T), would
be sufficient. However, because molecular signals usually allow functionally conservative
mutations and because their degrees of allowance can vary for each position,
the weight matrix (or the position-specific scoring matrix; PSSM) method is
usually favored: namely, based on a compilation of known signals, the contribution
according to their frequency for each residue at each position is stored as a matrix
scanned over the target sequences to find places that give higher summation scores
than a predefined threshold. 212
Weight matrix method, then, is the simplest form of so-called window-based
methods, in which a window of fixed length is examined at each position of the
target sequence. This method was successfully applied to signal peptide prediction
by von Heijne. 213 The matrix corresponds to positions from –13 to +2 in terms of
the cleavage site. Thus, the sequence features of h-region and c-region, especially
the (3, –1) rule, should contribute to the score calculation. Two kinds of matrices
(for prokaryotes and eukaryotes) were constructed but these two were rather similar.
Although von Heijne’s method was originally developed for the detection of cleavage
sites, it is sufficiently useful to detect the presence of (cleavable) signal peptides.
Recently, a prediction method very similar to the weight matrix method was
proposed by Chou. 214 Then, the method was expanded to include the interresidue
correlation around the –3, –1, and +1 positions (the subsite coupling model). 215 That
is, conditional probabilities between these residues were introduced. This method
showed 92% accuracy in detecting signal peptides in an objective test.
14.5.1.2 Artificial Neural Network-Based Methods
A direct way to improve the simple weight matrix method is to let pattern representation
incorporate the effect of internal gaps or the correlation between different
positions. Artificial neural networks (ANNs) have been used in various problems of
bioinformatics to meet the latter need. 211,216 The ANN method is a sort of machinelearning
method in which a number of numeric parameters are iteratively adjusted
(or learned) to better distinguish positive and negative data. The weight matrix
method of signal peptide prediction was elaborated using this method by several
authors. 217,218 Among them, Neilsen et al.’s method has been most successful. 173
In their algorithm, two kinds of networks were constructed for each of the three
systems (Gram-positive bacteria, Gram-negative bacteria, and eukaryotes). One network
outputs the S-score, which shows the likelihood of an input sequence segment
with a fixed length (19 and 27 for bacteria and eukaryotes, respectively) being a
part of a signal peptide, the other network outputs the C-score, which is an indicator
on whether the input segment includes a cleavage site or not. Scanning the input
sequence with the two networks, these two scores are calculated at each position.
By combining these scores with a sort of averaging, the presence of signal peptide