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首页> 外文会议>International conference on swarm, evolutionary, and memetic computing >Hybrid Biogeography Based Simultaneous Feature Selection and Prediction of N-Myristoylation Substrate Proteins Using Support Vector Machines and Random Forest Classifiers
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Hybrid Biogeography Based Simultaneous Feature Selection and Prediction of N-Myristoylation Substrate Proteins Using Support Vector Machines and Random Forest Classifiers

机译：基于支持向量机和随机森林分类器的基于混合生物地理学的同时特征选择和N-肉豆蔻酰化底物蛋白预测

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摘要

Majority of proteins undergo important post-translational modifications (PTM) that may alter physical and chemical properties of the protein and mainly their functions. Laboratory processes of determining PTM sites in proteins are laborious and expensive. On the contrary, computational approaches are far swifter and economical; and the models for prediction of PTMs can be quite accurate too. Among the PTMs, Protein N- terminal N-myristoylation by myristoyl-CoA protein N-myristoyltransferase (NMT) is an important lipid anchor modification of eukaryotic and viral proteins; occurring in about 0.5% encoded NMT substrates. Reliable recognition of myristoylation capability from the substrate amino acid sequence is useful for proteomic functional annotation projects as also in building therapeutics targeting the NMT. Using computational techniques, prediction-based models can be developed and new functions of protein substrates can be identified.In this study, we employ Biogeography based Optimization (BBO) for feature selection along with Support Vector Machines (SVM) and Random Forest for classification of N-myristoylation sequences. The simulations indicate that N-myristoylation sites can be identified with high accuracy using hybrid BBO wrappers in combination with weighted filter methods.
机译：大多数蛋白质会经历重要的翻译后修饰（PTM），这可能会改变蛋白质的物理和化学性质，主要是改变其功能。确定蛋白质中PTM位点的实验室过程既费力又昂贵。相反，计算方法更加快捷和经济。而且PTM的预测模型也可以非常准确。在PTM中，肉豆蔻酰基-CoA蛋白N-肉豆蔻酰基转移酶（NMT）的N-末端N-肉豆蔻酰化蛋白是真核和病毒蛋白的重要脂质锚定修饰。发生在约0.5％编码的NMT底物中。从底物氨基酸序列可靠识别肉豆蔻酰化能力可用于蛋白质组功能注释项目，也可用于构建靶向NMT的治疗药物。使用计算技术，可以开发基于预测的模型，并可以识别蛋白质底物的新功能。在这项研究中，我们采用基于生物地理的优化（BBO）进行特征选择，并使用支持向量机（SVM）和随机森林进行分类。 N-肉豆蔻酰化序列。模拟表明，使用混合BBO包装纸和加权过滤器方法可以高精度识别N-肉豆蔻酰化位点。

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