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Application of SVM to predict membrane protein types

✍ Scribed by Yu-Dong Cai; Pong-Wong Ricardo; Chih-Hung Jen; Kuo-Chen Chou

Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
233 KB
Volume
226
Category
Article
ISSN
0022-5193

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✦ Synopsis

As a continuous effort to develop automated methods for predicting membrane protein types that was initiated by Chou and Elrod (PROTEINS: Structure, Function, and Genetics, 1999, 34, 137-153), the support vector machine (SVM) is introduced. Results obtained through re-substitution, jackknife, and independent data set tests, respectively, have indicated that the SVM approach is quite a promising one, suggesting that the covariant discriminant algorithm (Chou and Elrod, Protein Eng. 12 (1999) 107) and SVM, if effectively complemented with each other, will become a powerful tool for predicting membrane protein types and the other protein attributes as well.

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## Abstract A new method is described for prediction of protein membrane topology (intra‐ and extracellular sidedness) from multiply aligned amino acid sequences after determination of the membrane‐spanning segments. The prediction technique relies on residue compositional differences in the protei