灵活的蛋白质-蛋白质对接与多轨代变压器
Lee-Shin Chu1, Jeffrey A Ruffolo2, Ameya Harmalkar1
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
GeoDock是一种用于蛋白质-蛋白质对接的新深度学习方法,可以快速准确地预测复杂结构. 它处理灵活的蛋白质,在基于结构的虚拟查方面,在速度和成功率上优于现有的方法.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 机器学习在药物发现中的作用
背景情况:
- 传统的蛋白质-蛋白质对接是缓慢的,因为广泛的采样和重新排名.
- 目前的深度学习对接方法很快,但成功率很低,并且假定结构刚硬.
- 刚性对接无法解释结合诱导的构造变化,这对于全抑制至关重要.
研究的目的:
- 为蛋白质-蛋白质对接开发一种快速而准确的深度学习方法.
- 为了实现像虚拟选这样的应用程序的高通量结构预测.
- 通过结合灵活性来解决刚性对接的局限性.
主要方法:
- GeoDock使用多轨代变压器网络架构.
- 它只需要对接伙伴的序列和结构作为输入,与需要MSA的方法不同.
- 该模型允许残留水平的灵活性来预测结合诱导的形状变化.
主要成果:
- 在严格的目标上,GeoDock取得了41%的成功率,超过了其他方法.
- 在灵活的目标上,GeoDock表现出与ClusPro.Pro.类似的顶级模型成功.
- 推理速度在单个GPU上每结构不到一秒,从而实现大规模选.
结论:
- GeoDock在快速和准确的蛋白质-蛋白质对接方面取得了重大进展.
- 该架构为预测蛋白质复合体中脊柱灵活性提供了基础.
- 该方法显示了基于结构的虚拟查和理解全性机制的前景.
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