Chamaileon可同时设计多靶点、多构象的蛋白质结合剂，突破传统单目标限制。
Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling
- 通过上下文感知的联合序列-结构建模，统一多靶点多构象设计问题
- 在新构建的CROSS基准上实现跨构象适应性序列生成，性能显著优于基线
- 适合需要多功能蛋白设计的科研人员，尤其关注复杂生物系统应用
生成模型的快速发展为蛋白质结合剂设计带来了新可能，该任务在结构生物学中至关重要。现有方法大多基于单靶点、单构象假设，难以建模多靶点或多构象交互需求。本文提出Chamaileon，将多靶点与多构象结合剂设计统一为跨上下文结合景观建模问题。其核心是上下文感知的序列-结构联合建模训练范式（I3CD）。推理阶段采用可扩展的路径混合采样（MoPS），在单一序列上优化多上下文适应性，缓解高质量多构象配对数据稀缺的问题。在新构建的基准CROSS上的大量评估表明，Chamaileon能有效生成适应多样构象景观和多靶点需求的序列。代码已开源：https://github.com/caohengyuan/Chamaileon。
原文摘要 · Abstract (English)
The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination. However, existing approaches are predominantly implemented under a single-target, single-state assumption, limiting their ability to model multi-target or multi-state interactions required for advanced function-oriented protein design. Here, we introduce Chamaileon, which unifies multi-target and multi-state binder design by formulating the problem as cross-context binding landscape modeling. The framework is underpinned by a training paradigm termed In-Context Complex Co-Design (I3CD) for context-aware sequence-structure co-modeling. During inference, we employ Mixture-of-Paths Sampling (MoPS), a scalable strategy that optimizes a single sequence across contexts while alleviating the scarcity of high-quality multi-conformational paired data. Extensive evaluation on our newly constructed benchmark, CROSS, demonstrates that Chamaileon effectively generates sequences adaptable to diverse conformational landscapes and multi-target requirements. The code is available on https://github.com/caohengyuan/Chamaileon.
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