Build a protein research copilot with Amazon Bedrock AgentCore

This article demonstrates how to build a conversational protein research copilot using Amazon Bedrock AgentCore that combines natural language parsing, vector similarity search with protein embeddings, and AI-powered result summarization.

• Use Strands Agents SDK to orchestrate three specialized tools: query parser, peptide searcher, and result summarizer
• Deploy ESM-C 300M protein language model as SageMaker serverless endpoint for generating 960-dimensional embeddings
• Store peptide embeddings in Aurora PostgreSQL with pgvector for cosine similarity search and metadata filtering
• Parse natural language queries into structured parameters using LLM-as-parser pattern with dedicated agent
• Deploy complete solution to Bedrock AgentCore runtime via AWS CodeBuild with Streamlit frontend on Fargate
• Reduce manual peptide research workflow from hours to under one minute per search query

The architecture demonstrates how to combine domain-specific embedding models with LLM orchestration for specialized research tasks, generalizable to genomics, drug design, and materials science applications.