对基因序列数据库搜索工具进行大规模评估,用于基于同质性的蛋白质功能预测
Chengxin Zhang1, Lydia Freddolino1
1Department of Computational Medicine and Bioinformatics, Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, United States.
Briefings in bioinformatics
|July 22, 2024
概括
优化序列搜索工具和参数显著改善蛋白质功能预测. 仔细选择和配置BLASTp,MMseqs2和DIAMOND等工具可以提高预测基因本体学 (GO) 术语的准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 使用序列数据库搜索的基于同质性的功能转移是预测蛋白质功能的基本方法,包括基因本体学 (GO) 术语.
- 这些序列搜索是现代机器学习和深度学习方法的组成部分,用于蛋白质功能预测.
研究的目的:
- 系统地评估不同序列搜索工具和参数设置对蛋白质功能预测准确性的影响.
- 开发和验证一个改进的评分函数,用于从同源序列中得出GO预测.
主要方法:
- 在用于GO术语预测的大型基准数据集上对流行的序列搜索工具 (例如BLASTp,MMseqs2,DIAMOND) 的比较分析.
- 系统地探索每个工具的各种搜索参数配置.
- 开发和测试一种新的评分功能,用于基于同质性的GO预测.
主要成果:
- 在GO期预测的默认参数下,BLASTp和MMseqs2通常优于其他工具,包括DIAMOND.
- 优化的参数设置使得 DIAMOND 能够实现与 BLASTp 和 MMseqs2.2 相当的性能.
- 新开发的评分功能在预测来自同类命中的GO术语方面始终超越了以前的方法.
结论:
- 搜索工具的选择和,至关重要的是,参数优化对于有效的基于同质性的蛋白质功能预测至关重要.
- 这些发现为现有的蛋白质功能预测算法提供了易于实施的改进.
- 这项研究有助于推进未来更准确的蛋白质功能预测工具的开发.
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