Support vector machines for prediction of protein signal sequences and their cleavage sites.
Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is an important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy (see, e.g. K.C. Chou, Current Protein and Peptide Science 2002;3:615-22). In this paper, support vector machines (SVMs), a new machine learning method, is applied to approach this problem. The overall rate of correct prediction for 1939 secretary proteins and 1440 nonsecretary proteins was over 91%. It has not escaped our attention that the new method may also serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.
Cai
Yu-Dong
YD
Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk
Lin
Shuo-liang
SL
Chou
Kuo-Chen
KC
D005822:Genetic Vectors; D006868:Hydrolysis; D021382:Protein Sorting Signals; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*
D021382:Protein Sorting Signals; D011506:Proteins