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310 Cell-Penetrating Peptides: Processes and Applications<br />

can also affect the dependency. For example, based on the mutational study, it was<br />

proposed that kinked signal peptides may approach SRP more closely. 205 In another<br />

study, leucine-rich or chemically denatured signal peptides were shown to increase<br />

the tendency for interacting with SRP. 206 Thus, SRP may have a chaperone function<br />

by maintaining the favorable conformation of signal peptides during their targeting.<br />

The recognition of signal peptides must compete with the recognition of other<br />

sorting signals in eukaryotic cells. However, apart from recognition of signal anchors<br />

and some exceptions, sequence features of signal peptides are rather unique and<br />

prediction of signal peptides from given amino acid sequences is relatively easier<br />

than prediction of mitochondrial targeting signals or chloroplast transit signals (see<br />

the following section). According to our extensive search of the optimized rule for<br />

signal sequence detection, only the sum of the hydropathy value 207 from the 6th<br />

residue to the 25th residue was a sufficiently powerful predictor. 208<br />

14.5 PREDICTIVE AND PROTEOME ANALYSES<br />

In the post-genomic era, prediction of signal peptides and the systematic analyses<br />

of secreted proteins within a proteome are of special importance. Prediction of signal<br />

peptides enables us to annotate open reading frames with no known homologous<br />

proteins. It can be used for screening candidates for drug targets. This situation<br />

might explain a part of the reason why a prediction program, SignalP, was enthusiastically<br />

welcomed; its paper is cited in ISI’s Red Hot Research list of 1997. 173 Not<br />

only theoretical works but also direct experimental studies on secreted proteins are<br />

now becoming major projects. Of course, both kinds of studies are desirable in order<br />

to stimulate each other.<br />

14.5.1 PREDICTION ALGORITHMS<br />

Predictive recognition of signal peptides has been most successful among predictions<br />

of various protein-sorting signals. Most of these algorithms can also predict cleavage<br />

sites. Although attempts have been made to detect candidate proteins dependent on<br />

the Tat-dependent pathway, all currently available prediction methods seem to deal<br />

with Sec-dependent signal peptides only. In addition, the distinction between SRPdependent<br />

and SRP-independent pathways has not been attempted, probably because<br />

of lack of sufficient data.<br />

It should also be noted that the existence of signal peptide is not always clear<br />

in a biological sense; a fraction of proteins with “weak” signal peptides can remain<br />

in the cytosol. 134 Moreover, it seems difficult to describe the optimized features of<br />

signal peptides because they are recognized by different proteins, e.g., SRP and<br />

translocon. The prediction of signal peptides has recently been reviewed by several<br />

authors, including myself. 188,209-211 Essentially, there are two approaches for the<br />

prediction of signal peptides: window-based methods and global structure-based<br />

methods. The former is conveniently divided into weight matrix methods and artificial<br />

neural network-based methods.

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