Protein Functional Annotation Pipeline
Build a high-performance protein annotation agent using the Google ADK (Agent Development Kit). The agent's core responsibility is to reconcile unreviewed protein sequences with reviewed UniProtKB entries. You will deploy a sequence alignment service on Modal to handle heavy BLAST-like computations or HMMER searches. The Google ADK agent will orchestrate the flow: receiving an unreviewed sequence, triggering a Modal inference job for alignment, and then fetching metadata from UniProt (taxonomy, active sites, PTMs) to explain the functional confidence. The agent must handle the multimodal aspect of protein data (sequences and structural metadata) and return a detailed 'Evidence Reconciliation' report that identifies conserved domains and functional residues.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a high-performance protein annotation agent using the Google ADK (Agent Development Kit). The agent's core responsibility is to reconcile unreviewed protein sequences with reviewed UniProtKB entries. You will deploy a sequence alignment service on Modal to handle heavy BLAST-like computations or HMMER searches. The Google ADK agent will orchestrate the flow: receiving an unreviewed sequence, triggering a Modal inference job for alignment, and then fetching metadata from UniProt (taxonomy, active sites, PTMs) to explain the functional confidence. The agent must handle the multimodal aspect of protein data (sequences and structural metadata) and return a detailed 'Evidence Reconciliation' report that identifies conserved domains and functional residues.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
Modal Execution
Verifies that the alignment was actually performed on Modal serverless.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Annotation Accuracy
Percentage of correctly identified functional residues. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Initialize Google ADK with Gemini 1.5 Pro to reason over protein sequence motifs and literature
Implement a 'modal.Function' that wraps Biopython's Blast/Align tools for serverless execution
Design an agent tool that queries the UniProt REST API using specific accessions and returns XML/JSON metadata
Build a logic gate that assigns confidence levels (Gold, Silver, Bronze) based on the 'Protein Existence' (PE) level in UniProt
Utilize Google ADK's Vertex AI integration to store and retrieve sequence embeddings for similarity search
Programmatically extract 'Features' (FT) from UniProt records to identify active site residues
Create a final reporting tool that generates a Markdown summary of the protein's predicted function with alignment visualizations
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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