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ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine

Alireza Ghafarollahi1, Markus J Buehler1,2

  • 1Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA mbuehler@MIT.EDU.

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Summary
This summary is machine-generated.

We developed ProtAgents, a novel platform for designing new proteins using Large Language Models (LLMs) and AI agents. This system enables automated, collaborative protein design with targeted properties.

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Area of Science:

  • Biotechnology
  • Materials Science
  • Artificial Intelligence

Background:

  • Current AI models for protein design often lack flexibility, limiting the integration of diverse knowledge and comprehensive analysis.
  • Existing methods focus on specific material objectives or structural properties, hindering broader applications.

Purpose of the Study:

  • Introduce ProtAgents, a platform for de novo protein design leveraging Large Language Models (LLMs) and multi-agent collaboration.
  • Demonstrate the platform's versatility in designing novel proteins, analyzing structures, and performing physics-based simulations.

Main Methods:

  • Utilized a multi-agent system where each AI agent possesses distinct capabilities (knowledge retrieval, structure analysis, simulations).
  • Employed Large Language Models (LLMs) to enable dynamic collaboration and communication between agents.
  • Integrated physics-based simulations to generate first-principles data, such as natural vibrational frequencies.

Main Results:

  • Successfully designed de novo proteins with targeted mechanical properties through automated and synergistic efforts.
  • Showcased the platform's ability to handle diverse protein design and analysis tasks.
  • Generated new first-principles data (natural vibrational frequencies) via physics simulations.

Conclusions:

  • ProtAgents offers a versatile and flexible approach to de novo protein design and analysis.
  • The LLM-powered multi-agent environment facilitates autonomous collaboration for complex, multi-objective materials problems.
  • This platform opens new avenues for autonomous materials discovery and design.