AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design

TL;DR

This paper introduces an integrated AI-driven framework for end-to-end protein binder design, combining multiple advanced tools and a protocol-based LLM coordination to improve workflow efficiency and reproducibility.

Contribution

The novel contribution is the development of an MCP-based agentic system that seamlessly integrates diverse biochemical tools for protein binder design in a unified, protocol-driven manner.

Findings

Enhanced reproducibility and automation in protein binder design workflows.

Successful integration of state-of-the-art tools like MaSIF, Rosetta, ProteinMPNN, and AlphaFold3.

Demonstrated capability for de novo binder generation from target structures.

Abstract

Modern AI technologies for drug discovery are distributed across heterogeneous platforms-including web applications, desktop environments, and code libraries-leading to fragmented workflows, inconsistent interfaces, and high integration overhead. We present an agentic end-to-end drug design framework that leverages a Large Language Model (LLM) in conjunction with the Model Context Protocol (MCP) to dynamically coordinate access to biochemical databases, modular toolchains, and task-specific AI models. The system integrates four state-of-the-art components: MaSIF (MaSIF-site and MaSIF-seed-search) for geometric deep learning-based identification of protein-protein interaction (PPI) sites, Rosetta for grafting protein fragments onto protein backbones to form mini proteins, ProteinMPNN for amino acid sequences redesign, and AlphaFold3 for near-experimental accuracy in complex structure prediction. Starting from a target structure, the framework supports de novo binder generation via surface analysis, scaffold grafting and pose construction, sequence optimization, and structure prediction. Additionally, by replacing rigid, script-based workflows with a protocol-driven, LLM-coordinated architecture, the framework improves reproducibility, reduces manual overhead, and ensures extensibility, portability, and auditability across the entire drug design process.