Protein attributes contribute to halo-stability, bioinformatics approach.
Protein attributes contribute to halo-stability, bioinformatics approach.
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DOI：
10.1186/1746-1448-7-1
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发表时间：
2011-05-18
期刊：
影响因子：
--
通讯作者：
Ebrahimi M
中科院分区：
文献类型：
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作者：
Ebrahimie E;Ebrahimi M;Sarvestani NR;Ebrahimi M
嗜盐蛋白可以耐受高浓度的盐。了解适盐性特征是耐盐作物工程的第一步。为此，我们研究了蛋白质的功能，有助于嗜盐生物的耐盐性。我们比较了嗜盐和非嗜盐蛋白质的850多个特征与各种筛选，聚类，决策树和广义规则归纳模型，以寻找编码耐盐的模式。各种属性加权算法选择的251个蛋白质属性作为重要特征对晕稳定性有贡献，其中14个属性被90%的模型选择，氢的计数在70%的属性加权模型中获得最高值（1.0），显示了该属性在特征选择建模中的重要性。其他属性主要是二肽的频率。当对具有或不具有特征选择过滤的数据集进行K-Means和TwoStep聚类建模时，未发现组数的变化。虽然诱导的树的深度不高，但树的准确率高于94%，疏水残基的频率是构建树的最重要特征。决策树模型的性能评价与穷举CHAID和CHAID模型具有相同的值和最佳的正确率。我们没有发现任何显着差异的百分比的正确性，性能评估，和平均正确性的各种决策树模型，或没有特征选择。我们首次分析了不同的筛选、聚类和决策树算法用于区分嗜盐蛋白和非嗜盐蛋白的性能，结果表明氨基酸组成可以用于区分耐盐蛋白和盐敏感蛋白。
Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins.