Protein researchers often struggle with the manual analysis of thousands of peptide sequences, a process that is time-consuming and prone to error. To address this, developers can build an AI-powered research copilot using Amazon Bedrock AgentCore, a managed runtime for hosting agents, and the Strands Agents SDK. This assistant allows researchers to perform natural language queries, such as searching for peptides similar to a specific virus epitope, while receiving automated summaries of the scientific findings. The system architecture leverages an orchestrator agent that manages three specialized tools: a parser to extract search parameters, a searcher for vector-based similarity matching, and a summarizer to interpret the data.

The underlying search capability utilizes ESM-C 300M, a protein language model from EvolutionaryScale (a company focused on applying deep learning to biological research). This model generates 960-dimensional embeddings that represent structural and functional peptide properties. These embeddings are stored in an Amazon Aurora PostgreSQL-Compatible Edition database using the pgvector extension, which supports cosine similarity searches. By bundling model weights directly into Amazon SageMaker AI serverless endpoints, the system achieves efficient performance with minimal cold start latency, as no external model downloads are required during runtime.

The solution employs a modular design where the Strands orchestrator agent treats the parser and summarizer as distinct tools, despite these tools themselves being smaller, dedicated agents. This 'agents-as-tools' pattern simplifies the primary workflow, which parses the user query, retrieves candidate sequences through vector similarity, and generates concise scientific insights. The entire infrastructure is managed via AWS CloudFormation, with communication between the agent runtime and the database facilitated by the Amazon RDS Data API over HTTPS. This secure, containerized approach allows researchers to transition from a natural language request to a structured analysis report within a unified conversational interface.

Protein researchers often struggle with the manual analysis of thousands of peptide sequences, a process that is time-consuming and prone to error. To address this, developers can build an AI-powered research copilot using Amazon Bedrock AgentCore, a managed runtime for hosting agents, and the Strands Agents SDK. This assistant allows researchers to perform natural language queries, such as searching for peptides similar to a specific virus epitope, while receiving automated summaries of the scientific findings. The system architecture leverages an orchestrator agent that manages three specialized tools: a parser to extract search parameters, a searcher for vector-based similarity matching, and a summarizer to interpret the data.

The underlying search capability utilizes ESM-C 300M, a protein language model from EvolutionaryScale (a company focused on applying deep learning to biological research). This model generates 960-dimensional embeddings that represent structural and functional peptide properties. These embeddings are stored in an Amazon Aurora PostgreSQL-Compatible Edition database using the pgvector extension, which supports cosine similarity searches. By bundling model weights directly into Amazon SageMaker AI serverless endpoints, the system achieves efficient performance with minimal cold start latency, as no external model downloads are required during runtime.

The solution employs a modular design where the Strands orchestrator agent treats the parser and summarizer as distinct tools, despite these tools themselves being smaller, dedicated agents. This 'agents-as-tools' pattern simplifies the primary workflow, which parses the user query, retrieves candidate sequences through vector similarity, and generates concise scientific insights. The entire infrastructure is managed via AWS CloudFormation, with communication between the agent runtime and the database facilitated by the Amazon RDS Data API over HTTPS. This secure, containerized approach allows researchers to transition from a natural language request to a structured analysis report within a unified conversational interface.