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312 Cell-Penetrating Peptides: Processes and Applications
and its cleavage site is predicted. The authors reported that their algorithm, SignalP,
can predict the presence of signal peptides with high accuracy (72.4% for eukaryotes)
and the program has been widely used. Later, SignalP was incorporated in TargetP,
which can also predict the presence of mitochondrial targeting signals and chloroplast
transit signals. 219 In version 2.0 of SignalP, the hidden Markov model method
(see below) is also incorporated (http://www.cbs.dtu.dk/services/SignalP-2.0/).
One of the potential problems of the ANN-based approach is that the method
often uses too many numeric parameters compared with the size of the training data
used. In such a case, overfitting of the network to the training data is a danger; i.e.,
the network may memorize the examples without extracting general sequence features.
To overcome this difficulty, Jagla and Schuchhardt used the adaptive encoding
artificial neural network method in which they could reduce the number of parameters
to one tenth without loss of prediction accuracy. 220 The approach of Bannai
et al. grows from a motivation to overcome the difficulty that learned knowledge is
very hard to interpret from the obtained parameter sets. 208 Thus, they extensively
searched a much simpler parameter space directly interpretable as prediction rules.
14.5.1.3 Global Structure-Based Methods
The other category of signal peptide prediction methods is an approach trying to
recognize the three-domain (tripartite) structure of signal peptides. A pioneering
work on this approach was done by McGeoch. 221 In his algorithm, the start site of
the h-region was first searched within the N-terminal 12 residues; then, the length
of the h-region and the maximally hydrophobic eight-residue segment in it were
used to judge the presence of signal peptide. Later, the method was refined using
discriminant analysis in a general purpose prediction program of subcellular localization
sites, PSORT/PSORT II. 222-224 At that time, the third variable, net charge of
the n-region, was also considered.
Note that McGeoch’s algorithm does not take the (–3, –1) rule into account.
Therefore, it cannot predict the cleavage site. However, this feature was used in
PSORT to detect signal anchors (uncleavable signal peptides) by combining von
Heijne’s and McGeoch’s algorithms; namely, a protein is predicted to have a signal
anchor when McGeoch’s method predicts positively and von Heijne’s algorithm
negatively. 222 However, as mentioned earlier, it is difficult to exactly predict signal
anchors exactly only from the absence of potential cleavage sites.
Hidden Markov model (HMM) has been widely used in various problems of
speech recognition and bioinformatics, including representation of protein functional
domains and gene finding. 216,225 It is a kind of probabilistic method where a predefined
model is optimized from a set of training data and then the constructed
model is used to scan target sequences. One of the strengths of HMM is that it can
flexibly model various sequence features, including the tripartite structure of signal
peptides. An application of HMM to signal peptide prediction was done by Nielsen
and Krogh. 226 Their program, named SignalP-HMM, can predict cleavable signal
peptides and also signal anchors by modeling the structure of type II signal anchor.