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Chapter 22 : Methods for the Computational Prediction of Periplasmic Proteins

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Abstract:

This chapter addresses several different computational methods for the identification of periplasmic proteins from sequence information alone. The benefits, pitfalls, and performance of the methods are discussed, and an approach for the optimal computational identification of periplasmic proteins from a sequenced genome is presented. Recognizing that PSORT I could be significantly improved, the authors' group set out to develop a new method, PSORTb, for the prediction of protein subcellular localization in bacteria. Indeed, many of the other methods described in the chapter use ePSORTdb as a source of training and testing data. By parsing the remaining records into an easy-to-manipulate format such as tab-delimited text format, researchers can then identify periplasmic proteins by either manually reviewing each annotated localization site or extracting any records with an instance of the word “periplasm”. The former approach is slow, but has the advantage of allowing the researchers to incorporate their own expert knowledge into the review process. Of all the methods developed for signal peptide prediction, the suite of tools developed at the Technical University of Denmark has consistently been ranked as the best by several independent evaluations. These programs include SignalP, LipoP, and TatP, which are discussed individually. The chapter has presented an overview of a selection of methods for the computational identification of periplasmic proteins. While the analytical pipeline described in the chapter will identify a large proportion of periplasmic proteins with a moderate to high degree of confidence, the need still exists for improved localization prediction methods.

Citation: Gardy J, Brinkman F. 2007. Methods for the Computational Prediction of Periplasmic Proteins, p 391-405. In Ehrmann M (ed), The Periplasm. ASM Press, Washington, DC. doi: 10.1128/9781555815806.ch22

Key Concept Ranking

Outer Membrane Proteins
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Integral Membrane Proteins
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References

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Tables

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TABLE 1

A summary of the computational methods for bacterial protein localization prediction

Citation: Gardy J, Brinkman F. 2007. Methods for the Computational Prediction of Periplasmic Proteins, p 391-405. In Ehrmann M (ed), The Periplasm. ASM Press, Washington, DC. doi: 10.1128/9781555815806.ch22
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TABLE 2

Statistics used in the calculation of performance

Citation: Gardy J, Brinkman F. 2007. Methods for the Computational Prediction of Periplasmic Proteins, p 391-405. In Ehrmann M (ed), The Periplasm. ASM Press, Washington, DC. doi: 10.1128/9781555815806.ch22
Generic image for table
TABLE 3

An independent comparison of the performance of five prokaryotic subcellular localization prediction methods

Citation: Gardy J, Brinkman F. 2007. Methods for the Computational Prediction of Periplasmic Proteins, p 391-405. In Ehrmann M (ed), The Periplasm. ASM Press, Washington, DC. doi: 10.1128/9781555815806.ch22
Generic image for table
TABLE 4

Methods for the prediction of transmembrane α-helices

Citation: Gardy J, Brinkman F. 2007. Methods for the Computational Prediction of Periplasmic Proteins, p 391-405. In Ehrmann M (ed), The Periplasm. ASM Press, Washington, DC. doi: 10.1128/9781555815806.ch22
Generic image for table
TABLE 5

Methods for the prediction of β-barrel outer membrane proteins

Citation: Gardy J, Brinkman F. 2007. Methods for the Computational Prediction of Periplasmic Proteins, p 391-405. In Ehrmann M (ed), The Periplasm. ASM Press, Washington, DC. doi: 10.1128/9781555815806.ch22

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