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首页> 外文期刊>Expert systems with applications >An Expert System To Predict Protein Thermostability Using Decision Tree
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An Expert System To Predict Protein Thermostability Using Decision Tree

机译：使用决策树预测蛋白质热稳定性的专家系统

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摘要

Protein thermostability information is closely linked to commercial production of many biomaterials. Recent developments have shown that amino acid composition, special sequence patterns and hydrogen bonds, disulfide bonds, salt bridges and so on are of considerable importance to thermostability. In this study, we present a system to integrate these various factors that predict protein thermostability. In this study, the features of proteins in the PGTdb are analyzed. We consider both structure and sequence features and correlation coefficients are incorporated into the feature selection algorithm. Machine learning algorithms are then used to develop identification systems and performances between the different algorithms are compared. In this research, two features, (E + F + M + R)/residue and chargedon-charged, are found to be critical to the thermostability of proteins. Although the sequence and structural models achieve a higher accuracy, sequence-only models provides sufficient accuracy for sequence-only thermostability prediction.
机译：蛋白质的热稳定性信息与许多生物材料的商业化生产紧密相关。最近的发展表明，氨基酸组成，特殊的序列模式和氢键，二硫键，盐桥等对于热稳定性具有相当重要的意义。在这项研究中，我们提出了一个系统，可以整合预测蛋白质热稳定性的各种因素。在这项研究中，分析了PGTdb中蛋白质的特征。我们考虑结构和序列特征，并将相关系数纳入特征选择算法。然后，将机器学习算法用于开发识别系统，并比较不同算法之间的性能。在这项研究中，发现两个特征（E + F + M + R）/残基和带电/不带电对蛋白质的热稳定性至关重要。尽管序列模型和结构模型具有更高的准确性，但是仅序列模型为仅序列热稳定性预测提供了足够的准确性。

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