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

Ze Song1, Hailong Yang1, Zhaohong Deng1

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

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|March 13, 2026
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Summary
This summary is machine-generated.

This study introduces a new protein design framework, MAESD, that uses large language models (LLMs) and evolutionary principles to create novel protein sequences. MAESD simplifies complex protein design by translating natural language into biological constraints for iterative sequence optimization.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Artificial intelligence in life sciences

Background:

  • Traditional protein design requires extensive biological expertise and complex workflows.
  • Large language models (LLMs) offer advanced reasoning capabilities for specialized domains.
  • Bridging the gap between natural language and biological constraints is crucial for accessible protein design.

Purpose of the Study:

  • To develop a unified computational framework for protein design guided by natural language.
  • To emulate natural protein evolution for optimized sequence design under constraints.
  • To reduce the technical barriers in protein design workflows.

Main Methods:

  • Proposed the protein evolutionary paradigm, mimicking natural evolution for protein design.
  • Developed MAESD (Multiagent Evolutionary Framework for Protein Sequence Design).
  • Integrated multiagent collaborative reasoning, LLMs, biological databases, and evolutionary computation (ProGen2, ProteinMPNN) for sequence generation and validation.

Main Results:

  • MAESD translates natural language instructions into actionable protein design constraints.
  • The framework employs an iterative "generation-validation" cycle for sequence refinement.
  • MAESD automates pipeline integration and parameter adaptation, reducing engineering burden.

Conclusions:

  • MAESD facilitates function- and structure-constrained evolutionary protein design using natural language.
  • The framework enhances accessibility by simplifying protein design workflows.
  • Expert biological judgment remains essential for result interpretation and experimental guidance.