发表机构
Gulliver Lab, CNRS UMR 7083, ESPCI Paris, PSL University（高勒弗实验室，法国国家科学研究中心联合研究单位第7083号，巴黎高等物理化工学院，巴黎文理研究大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用强化学习设计功能胶体折叠体，揭示两种组装路径，发现功能源于组装过程，为制备可按需重构、自修复的胶体材料提供新思路。
AI 中文摘要
蛋白质的功能取决于其采用的结构，而该结构的形成取决于所经历的路径。在可编程 matter 中，目标在组装前已确定，其他任何形成的结构都被视为误差。本研究表明，路径本身构成了一个设计空间：使用强化学习将模型 DNA 包覆的液滴链折叠为刚性二维几何结构，揭示出两类路径：仅添加键的“下坡”路径，以及在达到目标前断裂并重建键的“迂回”路径，对部分情况而言，这是唯一存在的路径。通过相互作用对路径进行粗粒化处理，可得到实验可实现的方案：部分方案生成单一几何结构，部分生成多个结构；共享迂回路径的结构可在其间循环，共存结构会组装成均匀产物无法实现的超结构。功能源于路径而非组件设计，设计过程而非组件，可获得按需重构和自我修复的胶体材料。
英文摘要
A protein's function follows from the structure it adopts, and in some cases which structure that is depends on the pathway taken. In programmable matter the target is fixed before assembly, and whatever else forms is treated as error. Here we show that pathways themselves form a design space. Using reinforcement learning, we fold model DNA-coated droplet chains into rigid two-dimensional geometries, uncovering two classes of pathways: downhill, in which bonds are only added, and detour, in which bonds are broken and remade before the target is reached: for some the only route that exists. Coarse-graining pathways by interactions gives experimentally realizable protocols. Some produce one geometry, others several: structures sharing a detour route can be cycled between, while those that coexist assemble into superstructures inaccessible to a uniform product. Function emerges from the pathways rather than being designed. Designing the process instead of the components could give colloidal materials that reconfigure on demand.
Comments8 pages, 5 figures
