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nnxqwei

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[资源] 基于氨基酸序列的蛋白质可溶性预测(无重复)!

在E.coli表达异源蛋白的时候,常会遇到表达的蛋白形成包涵体这样的麻烦事.

前段时间在表达和纯化一个蛋白质时,无论如何也纯化不出来,后来证实它形成包涵体.但我在表达载体的构建,诱导条件的摸索......已花了我大量的青春55555555

为什么不在做蛋白表达之前对它的可溶性特性进行预测呢???

所以在做实验之前,如果能对所表达的蛋白质的可溶性进行预测,将对我们选用表达载体,表达宿主,培养条件的选择等等就有很重要的指导意义.......少走一些弯路

Abstract:

MOTIVATION :Obtaining soluble proteins in sufficient concentrations is a recurring limiting factor in various experimental studies. Solubility is an individual trait of proteins which, under a given set of experimental conditions, is determined by their amino acid sequence. Accurate theoretical prediction of solubility from sequence is instrumental for setting priorities on targets in large-scale proteomics projects.

RESULTS: We present a machine-learning approach called PROSO to assess the chance of a protein to be soluble upon heterologous expression in E. coli based on its amino acid composition. The classification algorithm is organized as a two-layered structure in which the output of primary support vector machine classifiers serves as input for a secondary Naive Bayes classifier. Experimental progress information from the TargetDB database as well as previously published datasets were used as the source of training data. In comparison with previously published methods our classification algorithm possesses improved discriminatory capacity characterized by the Matthews Correlation Coefficient of 0.434 between predicted and known solubility states and the overall prediction accuracy of 72% (75% and 68% for positive and negative class respectively). We also provide experimental verification of our predictions using solubility measurements for 31 mutational variants of two different proteins.

基于氨基酸序列的蛋白质可溶性预测服务器网址:

http://webclu.bio.wzw.tum.de:8080/proso/

参考文献:
Smialowski P., Martin-Galiano A. J., Mikolajka A., et al. Protein Solubility: Sequence Based Prediction and Experimental Verification [J]. Bioinformatics，2006，

[ Last edited by nnxqwei on 2007-10-17 at 17:56 ]