by:load

/load — Load and Analyze a Protein Target

Load a protein target by name, PDB ID, or UniProt accession and run initial research analysis to prepare for a design campaign.

Instructions

Step 0: Show BY banner and read model profile

Display the BY session banner first:

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 BY ► LOADING TARGET
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Then read model profile:

MODEL_PROFILE=$(cat .by/config.json 2>/dev/null | grep -o '"model_profile"[[:space:]]*:[[:space:]]*"[^"]*"' | grep -o '"[^"]*"$' | tr -d '"' || echo "balanced")

Model lookup for this command:

Agent	quality	balanced	budget
by-research	opus	sonnet	sonnet

Step 1: Parse input

Determine the input type:

• PDB ID: 4-character alphanumeric (e.g., 1ABC, 7XYZ)
• UniProt accession: alphanumeric with pattern like P12345 or Q9UHD2
• Free text: treat as a target name or description for search

Step 2: Create campaign directory

CAMPAIGN_ID="campaign_$(date +%Y%m%d_%H%M%S)"
mkdir -p .by/campaigns/$CAMPAIGN_ID/{designs,screening,logs,research}
echo ".by/campaigns/$CAMPAIGN_ID" > .by/active_campaign

Initialize state.json with phase=RESEARCH, round=1, target info.

Step 3: Spawn by-research agent

Delegate to a by-research agent via Task() (model per profile table above).

Use MCP research tools (PDB, UniProt, SAbDab) -- NOT web search. The by-research agent has access to all structured databases via MCP servers.

Analyze target: {user_argument}

Tasks:

1. Resolve the target to a PDB structure and UniProt entry using MCP tools
2. Identify chains, domains, and key binding interfaces
3. Search SAbDab for existing antibodies against this target
4. Identify known epitopes and druggable sites
5. Assess target difficulty (structured/disordered, glycosylation, flexibility)
6. Recommend design strategy: binder vs antibody vs nanobody
7. Suggest initial parameters (scaffold, CDR lengths, seed count)

Write analysis to {campaign_dir}/research/target_analysis.json. Write human-readable summary to {campaign_dir}/research/summary.md.