Scalable design of repeat protein structural dynamics via probabilistic coarse-grained models
Scalable design of repeat protein structural dynamics via probabilistic coarse-grained models
复制标题
通过概率粗粒度模型重复蛋白质结构动力学的可扩展设计
DOI：
10.1101/2024.03.13.584748
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Sarvaharman S
中科院分区：
文献类型：
--
作者：
Sarvaharman S
计算蛋白质设计已经成为创造具有新功能的蛋白质的强大工具。然而，大多数现有的方法都忽略了结构动力学，即使它们在许多蛋白质功能中起着核心作用。此外，像分子动力学这样能够模拟蛋白质运动的方法在计算上要求很高，即使是中等大小的蛋白质也无法进行设计。在这里，我们开发了一个概率粗粒度模型来克服这些限制，并支持模块化重复蛋白的结构动力学设计。我们的模型使我们能够快速计算大型模块化蛋白质结构构象的概率分布，从而根据其动力学特征有效筛选设计候选物。我们通过探索4-6个模块重复蛋白的设计景观来证明这种能力。我们评估了超过65,000个蛋白质变体的灵活性、曲率和多状态潜力，并确定了特定模块在控制这些特征方面所起的作用。虽然我们这里的重点是蛋白质设计，但开发的方法很容易推广到任何模块化结构（例如，DNA折纸），提供了一种将动力学纳入各种生物设计工作流程的方法。
Computational protein design has emerged as a powerful tool for creating proteins with novel functionalities. However, most existing methods ignore structural dynamics even though they are known to play a central role in many protein functions. Furthermore, methods like molecular dynamics that are able to simulate protein movements are computationally demanding and do not scale for the design of even moderately sized proteins. Here, we develop a probabilistic coarse-grained model to overcome these limitations and support the design of the structural dynamics of modular repeat proteins. Our model allows us to rapidly calculate the probability distribution of structural conformations of large modular proteins, enabling efficient screening of design candidates based on features of their dynamics. We demonstrate this capability by exploring the design landscape of 4–6 module repeat proteins. We assess the flexibility, curvature and multi-state potential of over 65,000 protein variants and identify the roles that particular modules play in controlling these features. Although our focus here is on protein design, the methods developed are easily generalised to any modular structure (e.g., DNA origami), offering a means to incorporate dynamics into diverse biological design workflows.
登录
查看更多内容
影响因子：
6.8
作者：
Grove, Tijana Z.;Cortajarena, Aitziber L.;Regan, Lynne
通讯作者：
Regan, Lynne
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
14.8
作者：
Huang PS;Feldmeier K;Parmeggiani F;Velasco DAF;Höcker B;Baker D
通讯作者：
Baker D
影响因子：
3.7
作者：
Nivón LG;Moretti R;Baker D
通讯作者：
Baker D
影响因子：
5.6
作者：
Tyka MD;Keedy DA;André I;Dimaio F;Song Y;Richardson DC;Richardson JS;Baker D
通讯作者：
Baker D