让AI设计能灵活运动的蛋白质，突破静态结构限制。
Flexibility-Conditioned Protein Structure Design with Flow Matching
- 用神经网络预测残基灵活性，再逆向生成目标柔性的蛋白骨架。
- 生成的蛋白骨架经分子动力学验证，柔性符合预期且结构多样。
- 适合关注动态功能蛋白设计的研究者，如催化与识别相关领域。
几何深度学习与生成模型的进步使得能够设计出具备多种期望特性的新蛋白。然而，当前最先进方法通常仅限于生成具有静态目标属性（如基序和对称性）的蛋白。本文提出一种新框架，通过将结构生成条件设为灵活性，克服这一局限，因为灵活性对催化或分子识别等关键功能至关重要。我们首先引入BackFlip，一个用于从输入骨架结构预测每个残基灵活性的等变神经网络。基于BackFlip，我们提出FliPS，一种SE(3)-等变的条件流匹配模型，用于解决逆问题：生成表现出目标灵活性分布的骨架。实验表明，FliPS能够生成新颖且多样的蛋白骨架，其灵活性已通过分子动力学（MD）模拟验证。FliPS与BackFlip已在https://github.com/graeter-group/flips 开源。
原文摘要 · Abstract (English)
Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-art approaches are typically restricted to generating proteins with only static target properties, such as motifs and symmetries. In this work, we take a step towards overcoming this limitation by proposing a framework to condition structure generation on flexibility, which is crucial for key functionalities such as catalysis or molecular recognition. We first introduce BackFlip, an equivariant neural network for predicting per-residue flexibility from an input backbone structure. Relying on BackFlip, we propose FliPS, an SE(3)-equivariant conditional flow matching model that solves the inverse problem, that is, generating backbones that display a target flexibility profile. In our experiments, we show that FliPS is able to generate novel and diverse protein backbones with the desired flexibility, verified by Molecular Dynamics (MD) simulations. FliPS and BackFlip are available at https://github.com/graeter-group/flips .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。