变色龙：基于情境化建模和混合采样的跨上下文绑定器设计
Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling
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中文总结 AI 辅助
针对蛋白质结合剂设计中现有方法的局限，变色龙通过跨上下文结合景观建模统一多目标和多状态设计，采用I3CD训练范式及MoPS采样策略，能有效生成适应多样需求的序列。
中文摘要 AI 辅助
生成模型的快速发展为蛋白质结合剂设计带来了新潜力，这是结构生物学中的关键任务。但现有方法多在单目标、单状态假设下实现，限制了对高级功能导向蛋白质设计所需的多目标或多状态相互作用的建模能力。本文介绍了变色龙，它通过将问题表述为跨上下文结合景观建模，统一了多目标和多状态结合剂设计。该框架由上下文感知序列-结构协同建模的上下文复杂协同设计（I3CD）训练范式支撑。推理时采用路径混合采样（MoPS），优化跨上下文的单个序列，缓解高质量多构象配对数据稀缺问题。在新构建的基准CROSS上的广泛评估表明，变色龙能有效生成适应不同构象景观和多目标要求的序列。代码可通过此https URL获取。
英文摘要
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.
