| 摘要: |
| 为了克服生物信息学和计算生物学中字母或数字不受序列长度、氨基酸组成和位置、相邻氨基酸影响的缺陷,根据自然界普遍存在的随机性原理,创立计算变异学。计算变异学用氨基酸对可预测性、氨基酸分布概率和变异概率3种方法量化整个蛋白质及每个氨基酸,用活的、动态的测量指标量化分析蛋白质。计算变异学方法可以应用于研究蛋白质进化、遗传病定量诊断,分析蛋白质结构与功能、药物设计和病毒变异预测等领域。 |
| 关键词: 氨基酸对可预测性 氨基酸分布概率 氨基酸变异概率 蛋白质 计算变异学 |
| DOI： |
| 投稿时间：2009-08-05 |
| 基金项目:国际科技合作项目(2008DFA30710)、广西自然科学基金项目(广西重点实验室培育08-115-011和桂科自0991080)和广西科学院(桂科院研0701和09YJ17SW07)资助。 |
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| Creation and Application of Computational Mutation |
|
YAN Shao-min1, WU Guang2
|
| (1.National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, Nanning, Guangxi, 530007, China;2.Computational Mutation Project, DreamSciTech Consulting, Shenzhen, Guangdong, 518054, China) |
| Abstract: |
| Computational mutation is a discipline developed according to the random principle that lies in the very heart of the nature. It overcomes the limitation of bioinformatics and computational biology, where the letters and measures are not subject to the sequence length, amino-acid composition and position, neighboring amino acids, etc. Three methods are developed, amino-acid pair predictability, amino-acid distribution probability and mutating probability, to quantify a whole protein or each amino acid in proteins, which provides living, dynamic measures to quantitatively analyze protein. Currently the computational mutation is applied to studying the protein evolution, diagnosing genetic disorder, estimating protein structure and function, designing drug target, predicting mutation and so on. |
| Key words: amino-acid pair predictability amino-acid distribution probability amino-acid mutating probability protein computational mutation |
