Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.
Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.
Protein Structure Prediction and Analysis
End-to-end workflow for protein structure prediction starting from a sequence or UniProt accession. Combines ESMFold de novo prediction, AlphaFold database retrieval, experimental structure benchmarking from RCSB, ProtVar variant impact assessment, and ProtParam sequence property calculation.
KEY PRINCIPLES:
Sequence first — obtain or verify the protein sequence before prediction
ESMFold for fast de novo — works directly on sequence (up to ~800 residues); no database lookup needed
AlphaFold for reference — retrieve precomputed AlphaFold model for comparison; use qualifier parameter (UniProt accession)
Quality before interpretation — always report pLDDT scores; do not interpret low-confidence regions as folded
Experimental validation — compare predictions to RCSB experimental structures when available
ProtVar for variants — use when the question involves mutations or SNVs affecting structure
English-first queries — use English protein names in all tool calls; respond in the user's language
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
When to Use
Apply when users ask:
"Predict the structure of this sequence: [FASTA]"
"What does the AlphaFold model for [protein] look like?"
"How confident is the AlphaFold prediction for [protein]?"
"Is there an experimental structure for [protein] and how does it compare to AlphaFold?"
"How does mutation [variant] affect the structure of [protein]?"
"What are the physicochemical properties of [protein] sequence?"
"Predict the structure of this novel protein" / "I have a new sequence, can you model it?"
Not for (use tooluniverse-protein-structure-retrieval instead): retrieval-only tasks where user provides a PDB ID or wants to browse experimental structures without prediction.
Objective: Obtain or verify the protein sequence needed for ESMFold prediction.
If sequence is already provided
Use it directly for ESMFold_predict_structure. Check length:
1-400 residues: full prediction, high confidence expected
400-800 residues: prediction supported, may be slower
800 residues: ESMFold may fail or produce lower quality; recommend using AlphaFold instead
If only protein name or UniProt ID is provided
Retrieve sequence from UniProt_get_entry_by_accession:
accession: UniProt accession
Extract the sequence.value field from the response
Note: If only a name is given (not accession), first resolve with UniProt_search or MyGene_query_genes to get the UniProt accession, then fetch the sequence.
Phase 1: Sequence Properties
Objective: Calculate physicochemical properties before prediction to contextualize results.
Returns: ligands, cofactors, and ions transplanted onto the AlphaFold model by homology, with per-transplant local RMSD and source PDB IDs
When to use it: the apo AlphaFold model omits bound ligands/metals; run this to recover the likely cofactor/ligand/ion environment (e.g., ABL1 → STI/imatinib) for structure-guided binding-site interpretation
Workflow
Call alphafold_get_prediction and alphafold_get_summary
Extract mean pLDDT and per-residue confidence
Compare ESMFold vs AlphaFold pLDDT profiles:
Do they agree on low-confidence regions?
Large differences may indicate disordered/flexible regions
Note the AlphaFold model version (v1/v2/v3/v4)
Decision Logic
If no UniProt accession available: skip AlphaFold; use ESMFold only
If protein is a complex or has multiple chains: note that both tools predict single chains
If AlphaFold confidence is very high (mean pLDDT > 85): recommend using AlphaFold as primary reference
Phase 4: Experimental Structure Comparison
Objective: Check whether experimental structures exist in PDB and how predictions compare.
Tools
RCSBAdvSearch_search_structures (search by protein/gene name):
query: protein name or gene symbol
limit: number of results (default 10)
Returns: list of PDB entries with resolution, method, title
RCSBData_get_entry (details for a specific PDB ID):
pdb_id: 4-character PDB identifier
Returns: metadata including method, resolution, chains, ligands, release date
Workflow
Search for experimental structures using protein name
Filter for highest-resolution X-ray or cryo-EM structures
For the best experimental structure, retrieve entry details
Compare to predictions:
If experimental structure exists: note coverage, resolution, method
Flag regions predicted with high confidence but missing from experimental structure (could be disordered in crystal)
Flag regions in experimental structure with low pLDDT (may be crystal artifacts vs true fold)
Fallback
If RCSB search returns no results: note "no experimental structure found in PDB" and proceed with predictions only
ESMFold length limit: sequences longer than ~800 residues may fail or have reduced quality
Single-chain only: both ESMFold and standard AlphaFold predict monomers; complex prediction requires AlphaFold-Multimer (not available via these tools)
Disordered regions: pLDDT < 50 indicates intrinsically disordered regions (IDRs) — do not interpret these as structured
No dynamics: predicted structures are static; do not represent conformational flexibility or allosteric changes
Novel folds: ESMFold may struggle with proteins having no homologs in training data
AlphaFold DB coverage: some recently characterized proteins may not yet be in the AlphaFold database
