What is MiniFold?
MiniFold is a streamlined protein structure prediction model that delivers ESMFold-level accuracy at 10–20 times the speed. Developed for large-scale applications, MiniFold predicts three-dimensional protein structures from single amino acid sequences without requiring multiple sequence alignments (MSAs).
The model achieves its exceptional efficiency through a redesigned Evoformer architecture—a simplified version of the component introduced in AlphaFold2—paired with a lightweight structure module and custom GPU kernels optimized for protein folding tasks.
How MiniFold achieves faster predictions
Traditional folding models like AlphaFold2 achieve high accuracy through computationally expensive operations on large MSAs and complex structural refinement modules. ESMFold eliminated the MSA requirement by learning from a protein language model (ESM-2), but retained a relatively heavy Evoformer stack for processing evolutionary information.
MiniFold builds on ESM-2 like ESMFold does, but streamlines both the Evoformer and structure module. The redesigned Evoformer reduces computational overhead while preserving the model's ability to capture long-range dependencies in protein sequences. Custom GPU kernels exploit parallelism at the hardware level, cutting both inference time and peak memory consumption by substantial margins.
The result: predictions that complete in seconds rather than minutes, with benchmark accuracy matching ESMFold on CAMEO and CASP datasets.
How to use MiniFold online
ProteinIQ runs MiniFold on GPU infrastructure, delivering structure predictions directly in your browser with no installation required.
Input
| Input | Description |
|---|---|
Protein sequence | FASTA or raw amino acid sequence (single chain). Accepts file upload (.fasta, .fa, .txt), text input, or RCSB PDB ID fetch. |
Settings
MiniFold offers two model variants and configurable recycling to balance speed and accuracy:
| Setting | Options | Description |
|---|---|---|
Model size | 48L (default), 12L | Number of layers in the Evoformer. 48L provides better accuracy but takes longer. 12L is faster with minimal accuracy loss for well-characterized protein families. |
Recycling iterations | 1–6 (default: 3) | Number of refinement passes through the structure module. Higher values improve quality for difficult sequences but increase computation time. |
Output
MiniFold returns a ranked set of structure predictions in PDB format:
- 3D structure: Atomic coordinates for predicted protein conformation
- pLDDT confidence scores: Per-residue confidence estimates (0–100 scale)
Interpreting confidence scores
MiniFold inherits pLDDT (predicted local distance difference test) scoring from AlphaFold:
| pLDDT range | Interpretation |
|---|---|
| > 90 | Very high confidence; backbone geometry and side-chain positions likely accurate |
| 70–90 | Generally reliable; backbone probably correct, some side-chain uncertainty |
| 50–70 | Low confidence; treat with caution, especially for functional site annotation |
| < 50 | Very low confidence; region may be intrinsically disordered or beyond model capability |
Regions with low pLDDT often correspond to flexible loops, disordered tails, or structural motifs lacking representation in the training data.
When to use MiniFold versus other predictors
Use MiniFold when:
- Processing hundreds or thousands of sequences (virtual screening, mutational libraries)
- Working in resource-constrained environments (limited GPU memory or compute budget)
- Speed matters more than absolute accuracy for initial screening
- The protein family is well-studied (MiniFold performs best on sequences with strong ESM-2 representation)
Use ESMFold when:
- You need multi-chain complex predictions
- Sequences exceed MiniFold's tested length range
- Slightly higher accuracy justifies longer wait times
Use AlphaFold 2 when:
- Maximum accuracy is critical (e.g., for experimental design or publication-quality models)
- Evolutionary information is sparse (AlphaFold's MSA generation helps with orphan proteins)
- You can afford the computational cost (AlphaFold 2 is 100–200× slower than MiniFold)
Use Chai-1 or Boltz-2 when:
- Predicting protein-ligand, protein-DNA, or protein-RNA complexes
- Binding affinity estimation is needed (Boltz-2 only)
Limitations
MiniFold inherits the single-sequence limitation of ESMFold: without MSA information, it relies entirely on patterns learned during training. This works well for proteins with homologs in the training set, but struggles with:
- Novel folds: Proteins lacking clear evolutionary relationships to training examples
- Large conformational changes: Models predict static structures and cannot capture dynamic rearrangements
- Intrinsic disorder: Regions without stable structure produce unreliable predictions
- Multi-chain complexes: MiniFold is trained for monomers and does not predict inter-chain interfaces
For sequences outside these constraints, MSA-based predictors like AlphaFold 2 generally outperform single-sequence models.
Related tools
- ESMFold: Single-sequence structure prediction with multimer support. Slower than MiniFold but handles protein complexes.
- AlphaFold 2: MSA-based prediction for maximum accuracy. Best for difficult targets with sparse homology.
- Chai-1: Multi-modal structure prediction supporting proteins, ligands, DNA, and RNA in complex assemblies.
- Boltz-2: Biomolecular structure and binding affinity prediction with near-FEP accuracy for drug discovery applications.