ForceGen：基于蛋白质语言扩散模型、利用非线性力学解折叠响应的端到端从头蛋白质生成
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model
AI总结：
本研究提出ForceGen生成模型，结合预训练蛋白质语言模型与扩散机制，基于非线性力学解折叠响应从头设计蛋白质，经全原子模拟验证可满足目标力学性能，为力学生物学蛋白质材料发现提供新路径。
摘要：
在进化过程中，自然界产生了一系列卓越的蛋白质材料，包括弹性蛋白、丝蛋白、角蛋白和胶原蛋白，它们具有优异的力学性能，在力学生物学中发挥着关键作用。然而，超越自然设计来发现满足特定力学性能的蛋白质仍然具有挑战性。本文提出了一种生成模型，可预测满足复杂非线性力学性能设计目标的蛋白质设计方案。该模型利用预训练蛋白质语言模型中关于蛋白质序列的深层知识，通过映射力学解折叠响应来创建新型蛋白质。通过全原子分子模拟进行直接验证，我们证明所设计的蛋白质是全新的，且符合目标力学性能，包括解折叠能量、力学强度，以及详细的解折叠力-分离曲线。该模型为探索不受生物合成限制的巨大力学生物学蛋白质序列空间提供了快速途径，以力学特征为目标，助力发现具有优异力学性能的蛋白质材料。
英文摘要：
Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pre-trained protein language model and maps mechanical unfolding responses to create novel proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are novel, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as target to enable the discovery of protein materials with superior mechanical properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
