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310 Cell-Penetrating Peptides: Processes and Applications
can also affect the dependency. For example, based on the mutational study, it was
proposed that kinked signal peptides may approach SRP more closely. 205 In another
study, leucine-rich or chemically denatured signal peptides were shown to increase
the tendency for interacting with SRP. 206 Thus, SRP may have a chaperone function
by maintaining the favorable conformation of signal peptides during their targeting.
The recognition of signal peptides must compete with the recognition of other
sorting signals in eukaryotic cells. However, apart from recognition of signal anchors
and some exceptions, sequence features of signal peptides are rather unique and
prediction of signal peptides from given amino acid sequences is relatively easier
than prediction of mitochondrial targeting signals or chloroplast transit signals (see
the following section). According to our extensive search of the optimized rule for
signal sequence detection, only the sum of the hydropathy value 207 from the 6th
residue to the 25th residue was a sufficiently powerful predictor. 208
14.5 PREDICTIVE AND PROTEOME ANALYSES
In the post-genomic era, prediction of signal peptides and the systematic analyses
of secreted proteins within a proteome are of special importance. Prediction of signal
peptides enables us to annotate open reading frames with no known homologous
proteins. It can be used for screening candidates for drug targets. This situation
might explain a part of the reason why a prediction program, SignalP, was enthusiastically
welcomed; its paper is cited in ISI’s Red Hot Research list of 1997. 173 Not
only theoretical works but also direct experimental studies on secreted proteins are
now becoming major projects. Of course, both kinds of studies are desirable in order
to stimulate each other.
14.5.1 PREDICTION ALGORITHMS
Predictive recognition of signal peptides has been most successful among predictions
of various protein-sorting signals. Most of these algorithms can also predict cleavage
sites. Although attempts have been made to detect candidate proteins dependent on
the Tat-dependent pathway, all currently available prediction methods seem to deal
with Sec-dependent signal peptides only. In addition, the distinction between SRPdependent
and SRP-independent pathways has not been attempted, probably because
of lack of sufficient data.
It should also be noted that the existence of signal peptide is not always clear
in a biological sense; a fraction of proteins with “weak” signal peptides can remain
in the cytosol. 134 Moreover, it seems difficult to describe the optimized features of
signal peptides because they are recognized by different proteins, e.g., SRP and
translocon. The prediction of signal peptides has recently been reviewed by several
authors, including myself. 188,209-211 Essentially, there are two approaches for the
prediction of signal peptides: window-based methods and global structure-based
methods. The former is conveniently divided into weight matrix methods and artificial
neural network-based methods.