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.

翻译：在进化进程中，自然界呈现出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，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，可预测能够满足复杂非线性力学性能设计目标的蛋白质设计。该模型利用预训练蛋白质语言模型对蛋白质序列的深度知识，并通过映射力学解折叠响应来生成新型蛋白质。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具有新颖性，并且能够满足目标力学性能，包括解折叠能、力学强度以及详细的力-分离曲线。该模型以力学特征为目标，提供了一种快速途径，可探索不受生物合成约束的庞大力学生物学蛋白质序列空间，从而发现具有优异力学性能的蛋白质材料。