AI自主发现蛋白质设计新规律，揭示肽链长度与结构稳定性关系。
Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles
- 多智能体协作生成假设、设计实验并自我修正，全流程无需人工干预。
- 发现80残基以上β折叠肽比α螺旋肽更耐拉伸，提出新机械设计原则。
- 识别出混合结构中高变异性'焦灼区'，适合生物设计与AI科研人员参考。
人工智能进展有望实现自主发现，但多数系统仍仅复现训练数据中的隐含知识。我们提出Sparks——一个跨模态多智能体AI模型，可自主完成从假设生成、实验设计到迭代优化的完整科研流程，产出可泛化的科学原理与报告，全程无需人类介入。应用于蛋白质科学时，Sparks发现了两个此前未知现象：(i) 长度依赖性力学交叉现象，即β折叠偏向肽在超过约80个残基后其解折叠力超过α螺旋肽，确立了新型肽类力学设计原则；(ii) 链长与二级结构稳定性图谱，揭示β折叠丰富结构异常稳定，并存在混合α/β折叠中高变异性的‘焦灼区’。这些发现源自完全自主推理循环，融合生成序列设计、高精度结构预测及物理感知属性模型，由成对的生成-反思智能体保障自我纠错与可重复性。核心成果表明，Sparks可独立开展严谨科学探究并发现先前未知的科学原理。
原文摘要 · Abstract (English)
Advances in artificial intelligence (AI) promise autonomous discovery, yet most systems still resurface knowledge latent in their training data. We present Sparks, a multi-modal multi-agent AI model that executes the entire discovery cycle that includes hypothesis generation, experiment design and iterative refinement to develop generalizable principles and a report without human intervention. Applied to protein science, Sparks uncovered two previously unknown phenomena: (i) a length-dependent mechanical crossover whereby beta-sheet-biased peptides surpass alpha-helical ones in unfolding force beyond ~80 residues, establishing a new design principle for peptide mechanics; and (ii) a chain-length/secondary-structure stability map revealing unexpectedly robust beta-sheet-rich architectures and a "frustration zone" of high variance in mixed alpha/beta folds. These findings emerged from fully self-directed reasoning cycles that combined generative sequence design, high-accuracy structure prediction and physics-aware property models, with paired generation-and-reflection agents enforcing self-correction and reproducibility. The key result is that Sparks can independently conduct rigorous scientific inquiry and identify previously unknown scientific principles.
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