通过近似的贝叶斯计算来选择蛋白质进化的依赖位置的结构约束的替代模型中的选择.
David Ferreiro1,2, Catarina Branco1,2, Miguel Arenas1,2
1CINBIO, Universidade de Vigo, 36310 Vigo, Spain.
Bioinformatics (Oxford, England)
|February 19, 2024
概括
我们开发了ProteinModelerABC,用于选择蛋白质进化模型. 该工具准确地选择结构约束替代 (SCS) 和实证模型,改进了家族遗传学推断.
科学领域:
- 计算生物学 计算生物学
- 分子进化分子进化
- 生物信息学是一种生物信息学.
背景情况:
- 精确的遗传学推断依赖于适当的分子进化模型.
- 蛋白质的传统实证替代模型具有局限性和不切实际的假设.
- 结构上受约束的替代 (SCS) 模型提供了更大的现实性,特别是站点依赖的进化,但实施起来很复杂.
研究的目的:
- 介绍一个新的计算框架,ProteinModelerABC,用于在蛋白质替代模型中进行选择.
- 为模型选择实施近似贝叶斯计算 (ABC),结合实证和站点依赖的SCS模型.
- 用模拟和真实蛋白质家族数据评估模型选择的ABC方法的准确性.
主要方法:
- 开发了ProteinModelerABC,这是一个实现近似贝叶斯计算 (ABC) 的计算框架.
- 在框架内集成了多种不同的经验和站点依赖的结构约束替代 (SCS) 模型.
- 采用ABC,并没有对模型选择进行回归调整,使用广泛的模拟数据进行验证.
主要成果:
- 蛋白质模型ABC框架在SCS和实证蛋白质进化模型之间进行选择时表现出可接受的准确性.
- 对各种蛋白质家族的分析表明,与最合适的经验模型相比,SCS模型提供了更好的匹配.
- 该方法成功地处理了SCS模型中固有的位置依赖进化的复杂性.
结论:
- ProteinModelerABC为选择适当的蛋白质替代模型提供了一种强大而准确的方法.
- 结构上受约束的替代模型,特别是取决于位点的替代模型,在蛋白质进化分析方面优于经验模型.
- 开发的框架在分子进化研究中促进了更可靠的家族遗传推断.
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