---
title: ESMFold on the platform
description: Predict protein structures with ESMFold and interpret confidence metrics.
tags:
  - esmfold
---

ESMFold predicts a protein's 3D structure directly from its amino acid sequence — no multiple sequence alignment, no database lookup. Reach for it when your target isn't in a database: novel, engineered, or mutated sequences. If the protein is already known by UniProt ID, [fetch it with AlphaFold](/docs/alphafold) instead (faster, no GPU); if an experimental structure has been deposited, [download it from the PDB](/docs/pdb). See [what you can achieve](/docs/capabilities#structure) for the full set.

## Try it

**Try this prompt** — [open in a guest chat](/chat/new?guest=true&prompt=I%20want%20the%203D%20shape%20of%20KRAS%20G12C%20(UniProt%20P01116)%20and%20every%20druggable%20pocket%20on%20it%20%E2%80%94%20focus%20on%20the%20Switch%20II%20binding%20site%20and%20check%20whether%20sotorasib's%20covalent%20binding%20mode%20is%20recapitulated.%20Then%20assess%20drug-likeness%20on%20the%20docked%20compound%20and%20tell%20me%20which%20non-Switch-II%20pockets%20score%20above%200.5%20druggability%20for%20a%20non-covalent%20allosteric%20back-up.).

> I want the 3D shape of KRAS G12C (UniProt P01116) and every druggable pocket on it — focus on the Switch II binding site and check whether sotorasib's covalent binding mode is recapitulated. Then assess drug-likeness on the docked compound and tell me which non-Switch-II pockets score above 0.5 druggability for a non-covalent allosteric back-up.

_Folds with ESMFold, then chains into P2Rank pockets, DiffDock, and ADMET-AI — no tool names required._

## What ESMFold does

ESMFold uses the **ESM-2 language model** to predict atomic-level protein structures from sequence alone, much faster than MSA-based methods. It is not the most accurate predictor available — its authors report an average TM-score of 0.83 on CAMEO and 0.68 on CASP14 against AlphaFold2's 0.88 and 0.85 on the same sets. The trade is speed and MSA independence for accuracy, and [how ESMFold results compare to the bench](/docs/esmfold/translatability) states the size of that trade with citations.

## Citations

> Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, dos Santos Costa A, Fazel-Zarandi M, Sercu T, Candido S, Rives A. Evolutionary-scale prediction of atomic-level protein structure with a language model. *Science*. 2023 Mar 17;379(6637):1123-1130. doi: [10.1126/science.ade2574](https://doi.org/10.1126/science.ade2574)

## Key achievements

- **Speed**: Order of magnitude faster than MSA-based methods at inference time
- **No MSA required**: ESMFold does not require a multiple sequence alignment (MSA) step, making it fully "standalone"
- **Scale**: Speed enables high-throughput structural genomics at scale

## Core capabilities

- **Single-sequence prediction**: Predicts structures from primary amino acid sequences alone—no evolutionary information needed
- **End-to-end pipeline**: Uses token embeddings from the pre-trained ESM-2 language model directly
- **Confidence estimation**: Provides per-residue confidence scores (pLDDT) for reliability assessment
- **Accuracy depends on language-model perplexity**: proteins the language model finds unsurprising score close to AlphaFold2; accuracy falls as perplexity rises (Pearson r = −0.55 with TM-score on CAMEO, −0.67 on CASP14, author-reported)

## Technical approach

ESMFold is built upon a **BERT-like transformer architecture** trained using masked residue prediction. The model learns to predict hidden amino acids based on surrounding context, developing deep understanding of protein sequence-structure relationships.

The model predicts:
- **Backbone coordinates**: Cα, C, N, and O atom positions
- **Side chain orientations**: χ angles for amino acid side chains
- **Inter-residue distances**: Predicted distance and angle distributions
- **Confidence metrics**: Per-residue and per-structure reliability scores

## When to use ESMFold

## Primary use cases

### Structure prediction for novel proteins
- **De novo designed proteins**: Validate computational designs before experimental characterization
- **Synthetic biology constructs**: Predict structures of engineered proteins and fusion constructs
- **Mutant analysis**: Assess structural impacts of point mutations or domain insertions

### High-throughput structural genomics
- **Target selection**: Prioritize proteins for experimental structure determination
- **Virtual screening**: Generate structural models for drug design campaigns
- **Functional annotation**: Predict structures to infer protein function from sequence

### Early-stage drug discovery
- **Target validation**: Generate initial models for novel therapeutic targets
- **Antibody modeling**: nanobody and antibody *frameworks* only. ⚠︎ On a 75-nanobody benchmark the framework came out at 0.6–0.7 Å median RMSD but CDR3 at 3.3 Å, and on 200 antibody–antigen complexes ESMFold produced **zero** successful predictions — see [how ESMFold results compare to the bench](/docs/esmfold/translatability)
- **Protein-protein interaction studies**: ⊘ Not supported in practice. ESMFold v1 is single-chain, reaches acceptable DockQ on only 41% of dimers, and scored 0/200 on antibody–antigen complexes. Use [Boltz-2](/docs/boltz2) or [Chai-1](/docs/chai) for complexes

## Webapp integration and workflow

### Input methods in .bloom*

**Direct sequence entry:**
- Paste amino acid sequences in FASTA or raw format
- Real-time validation with sequence length and composition checks
- Automatic detection of standard vs. non-standard amino acids
- Preview of sequence properties (length, molecular weight, isoelectric point)

**UniProt integration:**
- Direct UniProt accession lookup (e.g., "P12345")
- Automatic fetching of canonical sequences
- Isoform selection for proteins with multiple variants
- Integration with UniProt annotations and features

**Previous job integration:**
- Reuse sequences from other tools (blast searches, sequence analysis)
- Automatic extraction from multi-sequence outputs
- Provenance tracking maintains connection to original data source

### Advanced job configuration

**Parameter optimization:**
- **Num recycles**: Control refinement iterations (1-10, default 3)
- **Chunk size**: Handle very long sequences (>1,000 residues)
- **CPU offloading**: Memory management for large proteins

**Job management:**
- Custom job naming for better organization
- Project-based quota tracking and management
- Integration with .bloom*'s job queuing and monitoring system

### Interactive visualization

**Built-in 3D viewer:**
- Real-time structure prediction progress
- Confidence-colored models (pLDDT scores)
- Interactive inspection of predicted domains and secondary structure
- Export options for external visualization tools

**Quality assessment:**
- Per-residue confidence visualization
- Predicted aligned error (PAE) matrices
- Secondary structure predictions
- Domain boundary identification

## Integration with .bloom* ecosystem

### Upstream data sources

**Sequence databases:**
- Direct UniProt and PDB sequence lookup
- Integration with BLAST search results
- Import from custom sequence libraries

**Previous analyses:**
- Reuse sequences from motif searches
- Integration with sequence clustering results
- Connection to phylogenetic analysis outputs

### Downstream applications

**Structure-based drug design:**
- Direct integration with DiffDock for molecular docking
- Pocket analysis and binding site identification
- Virtual screening preparation

**Protein engineering:**
- Model validation for computational design
- Assessment of stability and folding
- Analysis of protein-protein interfaces — ⊘ use a co-folding model, not ESMFold (see [translatability](/docs/esmfold/translatability))

**Functional studies:**
- Domain identification and characterization
- Active site prediction and validation
- Evolutionary conservation mapping

### Workflow automation

**Batch processing:**
- Multiple sequence submission in single jobs
- Parallel processing for high-throughput studies
- Integration with .bloom*'s batch job management

**Project organization:**
- Automatic linking to research projects
- Sharing capabilities for collaborative studies
- Export options for publications and presentations

## Best paired with

- **Molecular dynamics**: Refine ESMFold models with simulation
- **Docking studies**: ESMFold structures can be docked into ([DiffDock](/docs/diffdock), AutoDock), but **a predicted receptor costs pose accuracy**. On the PDBBind test set, DiffDock's top-1 success (RMSD < 2 Å) fell from 35.0% on crystal receptors to 21.7% on `esmfold_v1` receptors, and search-based docking collapsed to 2–10%. Details and citation in [how ESMFold results compare to the bench](/docs/esmfold/translatability)
- **Experimental validation**: Guide NMR or X-ray crystallography experiments
- **Disorder prediction**: Identify regions needing special handling

## Scope and limitations

## Supported inputs

### Sequence requirements
- **Single protein chains**: Multi-chain complexes not supported
- **Standard amino acids**: 20 canonical amino acids only
- **Length range**: 10-2,500 amino acids (optimal performance: 50-500 residues)
- **Sequence formats**: Raw amino acid sequence or FASTA format

### Input validation
- **Invalid characters**: Non-standard amino acids rejected
- **Length limits**: Sequences above 2,500 residues are rejected; confidence may drop in some regions above ~1,000 residues
- **Gaps and unknowns**: X characters treated as glycine for prediction

## Prediction limitations

### Accuracy considerations
- **Novel folds**: Lower accuracy for proteins with novel tertiary structures
- **Disordered regions**: Poor performance on intrinsically disordered proteins
- **Low complexity**: Struggles with low-complexity sequences (poly-A, etc.)
- **Transmembrane proteins**: Limited accuracy for membrane-spanning regions

### Confidence interpretation
- **pLDDT scores**: Range from 0-100, with &gt;90 indicating high confidence
- **Domain boundaries**: Confidence often drops at domain interfaces
- **Flexible loops**: Lower confidence for loops and linkers
- **Structured cores**: Highest confidence for α-helices and β-sheets

## Performance characteristics

### Speed and scaling
- **Typical runtime**: 10-60 seconds for average protein (50-200 residues)
- **Scaling**: Roughly linear with sequence length
- **Hardware requirements**: GPU acceleration recommended for best performance
- **Batch processing**: Supports multiple sequences in single job

### Deterministic behavior
- **Reproducible results**: Same sequence always produces identical structure
- **Version consistency**: Results stable across model versions for same input
- **Parameter sensitivity**: Small changes in parameters rarely affect major structure

## Quality assessment and validation

## Confidence metrics (pLDDT)

Per-residue confidence scores indicating local structural accuracy:

| pLDDT Range | Confidence Level | Interpretation |
|-------------|------------------|----------------|
| 90-100 | Very high | On CAMEO, ESMFold predictions above this threshold had a **median all-atom RMSD<sub>95</sub> of 1.42 Å** (backbone 0.94 Å), author-reported |
| 70-90 | Confident | On CAMEO, predictions above pLDDT 70 averaged **LDDT 0.83** against AlphaFold2's 0.85, author-reported. Fold level is reliable; individual contacts are not guaranteed |
| 50-70 | Low | Topology only. Treat contacts as unestablished |
| &lt;50 | Very low | Not a structural prediction; often a disorder signal |

These are per-residue confidences, not a global guarantee — see [how ESMFold results compare to the bench](/docs/esmfold/translatability) for what each band has been measured to support.

## Predicted aligned error (PAE)

Measures expected position error between residue pairs:

- **PAE matrix**: NxN matrix showing predicted error between all residue pairs
- **Domain identification**: High PAE between domains indicates separate structural units
- **Rigid body movements**: Low PAE within domains, high between domains

## Quality control checks

### Structural validation
- **Stereochemistry**: Check bond lengths, angles, and Ramachandran plot
- **Packing quality**: Assess side chain packing and cavity identification
- **Secondary structure**: Verify predicted helices and sheets match sequence

### Biological plausibility
- **Hydrophobic core**: Buried hydrophobic residues should form core
- **Surface properties**: Polar residues should face solvent
- **Domain organization**: Multi-domain proteins should show clear boundaries

## Integration and workflow

## Pre-ESMFold preparation

### Sequence curation
- **Signal peptides**: Remove N-terminal signal sequences if present
- **Propeptide removal**: Remove propeptides that aren't part of mature protein
- **PTM consideration**: Account for disulfide bonds, glycosylation sites
- **Isoform selection**: Choose most biologically relevant isoform

### Parameter optimization
- **Num recycles**: Default is 3; increase up to 10 for marginally better accuracy at the cost of runtime
- **Chunk size**: Adjust for very long sequences (>500 residues)
- **CPU offloading**: Enable for memory-constrained environments

## Post-ESMFold refinement

### Structure optimization
- **Energy minimization**: Reduce steric clashes and optimize geometry
- **Side chain refinement**: Optimize rotamer positions
- **Loop modeling**: Rebuild low-confidence loop regions

### Validation and analysis
- **Quality metrics**: Calculate MolProbity scores, clash analysis
- **Comparison tools**: Align with known structures using TM-align or FATCAT
- **Functional assessment**: Check active site geometry, binding pockets

## Integration patterns

### Drug discovery workflow
1. **Target identification** → ESMFold prediction
2. **Pocket analysis** → Lead identification
3. **Virtual screening** → Hit optimization

### Structural biology
1. **Sequence analysis** → ESMFold prediction
2. **Model validation** → Experimental planning
3. **Structure determination** → Model refinement

## Common pitfalls and best practices

## Input-related issues
- **Avoid truncated sequences**: Use full-length proteins when possible
- **Check sequence quality**: Ensure no sequencing errors or frameshifts
- **Consider isoforms**: Different splice variants may have different structures

## Interpretation errors
- **Don't over-trust high confidence**: Even pLDDT &gt;90 can have domain-level errors
- **Validate functionally important regions**: Focus confidence assessment on active sites
- **Use multiple models**: Compare predictions from different tools when possible

## Performance optimization
- **Batch similar sizes**: Group sequences of similar length for efficient processing
- **GPU utilization**: Use GPU instances for faster predictions
- **Memory management**: Enable CPU offloading for large proteins

## Chat Usage

### Example Prompts

- "Fold this protein sequence with ESMFold: MKTAYIAKQRQISFVKSHFSRQDILDLWIYHTQGYFP"
- "Predict the structure of EGFR kinase domain (P00533, residues 696-1022)"
- "Fetch human insulin from UniProt and fold it with ESMFold"

### Workflow Chaining

- **Feeds into**: DiffDock (docking), P2Rank (pocket prediction), PDB2PQR (electrostatics)
- **Accepts from**: UniProt (sequence fetch), BLAST (hit sequences), ESM-3 (embeddings)

### AlphaFold DB Fetch

For proteins already in the AlphaFold Protein Structure Database, you can fetch pre-computed structures instead of running prediction:

- "Get the AlphaFold structure for P12345"
- Pre-computed structures use the same 3D viewer and pLDDT coloring as ESMFold predictions
- Use ESMFold when: no AlphaFold structure exists, you need a specific isoform, or you want to fold a novel/engineered sequence

## FAQ

### What is ESMFold and how does it work?

  ESMFold is a protein structure prediction method that uses a protein language model to predict 3D structures directly from single amino acid sequences — no multiple sequence alignment required.

### How does ESMFold compare to other folding methods?

  ESMFold is significantly faster than MSA-based methods because it skips multiple sequence alignment generation, and it pays for that in accuracy: its authors report an average TM-score of 0.83 on CAMEO and 0.68 on CASP14, against AlphaFold2 at 0.88 and 0.85. The gap is smallest on sequences the language model finds unsurprising. Reach for ESMFold when no MSA is available or when speed matters, not when you want the most accurate model available. See [how ESMFold results compare to the bench](/docs/esmfold/translatability).

## Read next