MAESD: A Unified Multi-Agent Evolutionary Framework for Protein Sequence Design

Traditional computational protein design heavily relies on expert-level biological inputs to define structural and functional constraints, posing significant barriers in terms of technical implementation and workflow construction. To address this gap, we capitalize on recent advancements in large language models (LLMs)─which excel at complex reasoning in specialized domains by leveraging knowledge bases to generate expert-grade outputs. In this study, we first propose the protein evolutionary paradigm, a design paradigm that emulates the core logic of natural protein evolution by taking biological function as the ultimate target, achieving progressive optimization of protein sequences under explicit functional and structural constraints through iterative evolutionary refinement. Guided by this paradigm, we present MAESD (Multiagent Evolutionary Framework for Protein Sequence Design), a unified computational framework for function- and structure-constrained evolutionary protein design guided by natural language instructions. This paradigm integrates multiagent collaborative reasoning to bridge the semantic gap between natural language descriptions and biological constraints, while adopting an iterative evolutionary optimization mechanism to ensure the biological plausibility of designed sequences at each iteration. MAESD operates through two core collaborative modules for sequence generation: (1) A semantic-to-biological translation module, which employs LLMs and biological databases to interpret user-provided natural language biological requirements and extract actionable protein design constraints; (2) an evolutionary loop module, which realizes iterative sequence refinement via a ″generation-validation″ cycle─utilizing ProGen2 and ProteinMPNN for sequence generation and integrating structural, energetic, and functional verification to filter and optimize sequences. By fusing natural language understanding with evolutionary computation, MAESD reduces the engineering and implementation burden of protein design workflows by automating pipeline integration and parameter adaptation, while expert biological judgment remains necessary for interpreting results and guiding experimental decisions.