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.

通过深度学习的生成模型，预测蛋白质设计以满足复杂的非线性力学特性设计目标，并通过分子模拟验证展示出设计的蛋白质在力学性质方面的新颖性和达到的目标力学特性，为探索巨大的力学生物学蛋白质序列空间提供了快速的途径。

ForceGen: 基于非线性力学展开响应的蛋白质语言扩散模型的端到端全新蛋白质生成

ForceGen: End-to-end de novo protein generation based on nonlinear  mechanical unfolding responses using a protein language diffusion model

Materials in biology span all the scales from Angstroms to meters and
typically consist of complex hierarchical assemblies of simple building blocks.
Here we describe an application of category theory to describe structural and
resulting functional properties of biological protein materials by developing
so-called ologs. An olog is like a "concept web" or "semantic network" except
that it follows a rigorous mathematical formulation based on category theory.
This key difference ensures that an olog is unambiguous, highly adaptable to
evolution and change, and suitable for sharing concepts with other olog. We
consider simple cases of alpha-helical and amyloid-like protein filaments
subjected to axial extension and develop an olog representation of their
structural and resulting mechanical properties. We also construct a
representation of a social network in which people send text-messages to their
nearest neighbors and act as a team to perform a task. We show that the olog
for the protein and the olog for the social network feature identical
category-theoretic representations, and we proceed to precisely explicate the
analogy or isomorphism between them. The examples presented here demonstrate
that the intrinsic nature of a complex system, which in particular includes a
precise relationship between structure and function at different hierarchical
levels, can be effectively represented by an olog. This, in turn, allows for
comparative studies between disparate materials or fields of application, and
results in novel approaches to derive functionality in the design of de novo
hierarchical systems. We discuss opportunities and challenges associated with
the description of complex biological materials by using ologs as a powerful
tool for analysis and design in the context of materiomics, and we present the
potential impact of this approach for engineering, life sciences, and medicine.

使用范畴理论构建 ologs 来描述生物蛋白材料的结构和功能特性，以实现不同层次精确关系的比较研究，以推导设计新型分层体系的功能性，这样的描述有助于工程、生命科学和医学等领域的研究。