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Optimized local protein structure with support vector machine to predict protein secondary structure

机译:使用支持向量机优化局部蛋白质结构以预测蛋白质二级结构

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摘要

Protein includes many substances, such as enzymes, hormones and antibodies that are necessary for the organisms. Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. These proteins have different shapes and structures which distinct them from each other. By having unique structures, only proteins able to carried out their function efficiently. Therefore, determination of protein structure is fundamental for the understanding of the cell's functions. The function of a protein is also largely determined by its structure. The importance of understanding protein structure has fueled the development of protein structure databases and prediction tools. Computational methods which were able to predict protein structure for the determination of protein function efficiently and accurately are in high demand. In this study, local protein structure with Support Vector Machine is proposed to predict protein secondary structure.
机译:蛋白质包括许多有机物所必需的物质,例如酶,激素和抗体。活细胞由蛋白质和基因控制,这些蛋白质和基因通过复杂的分子途径相互作用以实现特定功能。这些蛋白质具有不同的形状和结构,将它们彼此区分开。通过具有独特的结构,只有蛋白质能够有效地发挥其功能。因此,蛋白质结构的确定对于理解细胞功能至关重要。蛋白质的功能在很大程度上也取决于其结构。了解蛋白质结构的重要性推动了蛋白质结构数据库和预测工具的发展。迫切需要能够预测蛋白质结构以有效和准确地确定蛋白质功能的计算方法。在这项研究中,提出了利用支持向量机的局部蛋白质结构来预测蛋白质的二级结构。

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