在对突变的蛋白质-蛋白质结合亲和力变化预测中偏差的量化
Matsvei Tsishyn1,2, Fabrizio Pucci1,2, Marianne Rooman1,2
1Computational Biology and Bioinformatics, Université Libre de Bruxelles, Roosevelt Ave, 1050, Brussels, Belgium.
Briefings in bioinformatics
|January 10, 2024
概括
预测突变后蛋白质结合亲和力变化的计算模型显示出希望,但与一般化和偏见作斗争. 为了准确预测未见的突变,需要进一步改进.
科学领域:
- 生物物理学的生物物理.
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 预测突变对蛋白质-蛋白质结合亲和力的影响对于了解疾病和生物技术应用至关重要.
- 许多计算方法,包括基于物理和机器学习的方法,已被开发来预测这些亲和力变化.
- 对于这些预测工具而言,SKEMPI 2.0等现有基准可能呈现出过于乐观的性能指标.
研究的目的:
- 为了对突变后蛋白质结合亲和力变化的八个突出的计算预测器进行基准测试.
- 为了确定这些预测方法中的偏差和数据集依赖性.
- 评估这些工具对未见的突变和各种数据集的概括性.
主要方法:
- 基准测试八个成熟的蛋白质结合亲和力预测器.
- 使用SKEMPI 2.0数据集进行评估.
- 采用来自SARS-CoV-2尖端蛋白和ACE2复合体的深度突变生成数据作为额外的测试案例.
- 分析预测偏差,数据集依赖性和概括性.
主要成果:
- 大多数经过测试的方法都显示出稳定性和准确性,但表现出有限的概括性,特别是对于未见的突变.
- 与基于物理学的方法相比,纯粹的机器学习方法显示出更严重的概括性问题.
- 观察到显著的预测偏差,特别是倾向于预测破坏稳定的突变.
结论:
- 目前用于预测突变诱导的蛋白质结合亲和力变化的计算模型需要进一步提高概括性和稳定性.
- 方法开发人员应该意识到并解决预测偏差,特别是关于突变属性的偏差.
- 未来的工作应该集中在开发更可靠的方法,这些方法在多样化和未见的突变数据上表现良好.
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