可靠的蛋白质-蛋白质对接与AlphaFold,Rosetta和复制品交换
Ameya Harmalkar1, Sergey Lyskov1, Jeffrey J Gray1,2,3
1Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, United States.
eLife
|May 27, 2025
概括
AlphaRED将AlphaFold与基于物理的对接集成,以改善蛋白质复杂结构预测,特别是对于灵活的蛋白质. 这种方法提高了对挑战性案例的准确性,而AlphaFold单独在其中扎.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物物理学的生物物理.
背景情况:
- 预测蛋白质复杂结构至关重要,但具有挑战性,特别是在形状变化时.
- 在准确预测蛋白质复合体方面,AlphaFold-multimer (AFm) 显示出局限性,仅在43%的病例中取得成功.
- 当前的方法难以建模蛋白界面,并预测复杂的结构,当结合伙伴经历显著的结构变化时.
研究的目的:
- 为准确的蛋白质复杂结构预测开发一个强大的计算管道.
- 为了改善蛋白质与蛋白质相互作用中的构造变化的采样.
- 为了提高AlphaFold-multimer对具有挑战性的蛋白质复合体的预测准确度.
主要方法:
- 结合AlphaFold (AF) 用于结构模板生成与基于物理的复制品交换对接算法.
- 重用AF信任度 (pLDDT) 来估计蛋白质的灵活性和对接精度.
- 将这些指标集成到ReplicaDock 2.0协议中,以创建AlphaRED管道.
- 在254个蛋白质标的精选集合和对接基准集5.5.5上验证了管道.
主要成果:
- AlphaRED成功地对接了97个之前失败的AlphaFold预测.
- 在63%的基准目标中实现了CAPRI可接受的质量或更好的预测.
- 在具有挑战性的抗原-抗体点上表现出43%的成功率,明显超过AFm的20%的成功率.
结论:
- 将深度学习 (AlphaFold) 与基于物理的增强采样 (复制交换对接) 集成,为蛋白质复杂结构预测提供了一个强大的策略.
- AlphaRED提供了一个强大的in silico管道,能够准确地建模蛋白质接口并预测复杂结构,特别是那些涉及形状灵活性的结构.
- 开发的方法成功地解决了现有工具的局限性,例如针对特定具有挑战性的案例的AlphaFold-multimer.
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