结合对对结构相似性和深度学习接口接触预测,以估计CASP15中蛋白质复合模型的准确性
Raj S Roy1, Jian Liu1, Nabin Giri1
1Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, Missouri, USA.
Proteins
|June 26, 2023
概括
我们开发了MULTICOM_qa,这是一种用于估计蛋白质复杂模型准确性的混合方法. 这种方法结合了对相似性和深度学习接触预测,显示了评估四级结构质量的前景.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 预测蛋白质结构的方法
背景情况:
- 准确估计四级结构模型 (EMA) 对于理解蛋白质功能和相互作用至关重要.
- 目前用于估计蛋白质三级模型质量的现有方法对于四级模型并不总是有效的,特别是当模型质量低等时.
- 双向相似性方法在评估蛋白质复杂模型的质量方面存在局限性.
研究的目的:
- 开发和评估一种混合方法来估计蛋白质四元结构模型的准确性.
- 解决现有方法在处理低质量和类似蛋白质复合体模型方面的局限性.
- 确定影响四级结构模型估计准确性的关键因素.
主要方法:
- 开发了MULTICOM_qa,这是一种混合方法,集成了对相似度得分 (PSS) 和接口接触概率得分 (ICPS).
- 在ICPS组件中利用深度学习进行链间接触预测.
- 盲目参加了第15次蛋白质结构预测技术的批判性评估 (CASP15),以评估性能.
主要成果:
- MULTICOM_qa在估计CASP15.15的蛋白质组装模型的全球结构准确度方面表现出强的表现.
- 在预测和真实模型质量得分之间实现了0.66的平均每目标相关系数.
- 展示了有效的模型选择能力,每个目标的平均排名损失为0.14.
结论:
- 结合多模型 (PSS) 和单模型 (ICPS) 方法的混合方法是EMA的一个有希望的策略.
- 在大规模评估中,MULTICOM_qa成功估计了蛋白质复杂模型的准确性.
- 确定并分析了影响EMA的关键因素,包括目标和模型采样难度,以及模型质量分布.
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