ProtAgents: Protein discovery via large language model multi-agent
  collaborations combining physics and machine learning
A. Ghafarollahi M. J. Buehler
Designing de novo proteins beyond those found in nature holds significant
promise for advancements in both scientific and engineering applications.
Current methodologies for protein design often rely on AI-based models, such as
surrogate models that address end-to-end problems by linking protein structure
to material properties or vice versa. However, these models frequently focus on
specific material objectives or structural properties, limiting their
flexibility when incorporating out-of-domain knowledge into the design process
or comprehensive data analysis is required. In this study, we introduce
ProtAgents, a platform for de novo protein design based on Large Language
Models (LLMs), where multiple AI agents with distinct capabilities
collaboratively address complex tasks within a dynamic environment. The
versatility in agent development allows for expertise in diverse domains,
including knowledge retrieval, protein structure analysis, physics-based
simulations, and results analysis. The dynamic collaboration between agents,
empowered by LLMs, provides a versatile approach to tackling protein design and
analysis problems, as demonstrated through diverse examples in this study. The
problems of interest encompass designing new proteins, analyzing protein
structures and obtaining new first-principles data -- natural vibrational
frequencies -- via physics simulations. The concerted effort of the system
allows for powerful automated and synergistic design of de novo proteins with
targeted mechanical properties. The flexibility in designing the agents, on one
hand, and their capacity in autonomous collaboration through the dynamic
LLM-based multi-agent environment on the other hand, unleashes great potentials
of LLMs in addressing multi-objective materials problems and opens up new
avenues for autonomous materials discovery and design.