Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling

Abstract: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 this https URL.

Machine Learning (cs.LG); Biomolecules (q-bio.BM)

arXiv:2607.23518 [cs.LG]

(or arXiv:2607.23518v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.23518 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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