MiniFold

Predict protein structures from single sequences.

Docs

Input

Protein sequence

30 credits

Output

Configure input settings on the left, then click "Submit job"

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

ABodyBuilder3

ABodyBuilder3 predicts antibody variable-domain structures from paired heavy and light chain sequences. It returns a PDB structure and, for the pLDDT checkpoint, per-residue confidence values.

AlphaFlow

Generate protein conformational ensembles with ESMFlow, the single-sequence AlphaFlow model family. Produces multiple diverse structures showing protein flexibility and dynamics.

AlphaFold2

AlphaFold2 via ColabFold for high-accuracy protein structure prediction. Uses MMSeqs2 API for MSA generation with no local databases required. Supports monomer and multimer prediction.

Boltz-2

Boltz-2 is a biomolecular foundation model for structure and binding affinity prediction. Supports proteins, ligands, DNA, and RNA in multi-component complexes. Automatically scales GPU resources for large complexes. Predicts binding affinity with near-FEP accuracy at 1000x faster speed.

Chai-1

Chai-1 is a multi-modal foundation model for molecular structure prediction. Predicts 3D structures for proteins, ligands, DNA, RNA, and multi-component complexes with high accuracy.

ESMfold

ESMfold is a fast, single-sequence protein structure predictor from Meta AI. Predicts 3D protein structures directly from amino acid sequences without requiring multiple sequence alignments (MSA), making it significantly faster than AlphaFold while automatically scaling GPU resources for larger proteins.

ESMFold2

ESMFold2 predicts protein structures and multi-chain protein complexes from amino acid sequences using Biohub protein language models. The first ProteinIQ release focuses on sequence-based protein folding with confidence metrics, native mmCIF structures, and optional PAE, distogram, and pair-chain iPTM outputs.

ImmuneBuilder

ImmuneBuilder predicts 3D structures of immune receptor proteins including antibodies, nanobodies, and T-cell receptors. It uses ABodyBuilder2, NanoBodyBuilder2, and TCRBuilder2/TCRBuilder2+ to generate structures with per-residue error estimates and optional ensemble artifacts.

IntelliFold 2

Controllable biomolecular structure prediction model for proteins, ligands, DNA, RNA, and multi-component complexes. IntelliFold 2 supports fast v2-Flash inference, optional MSA generation, and ranked confidence outputs.

LMI4Boltz

LMI4Boltz is a low-memory fork of Boltz for biomolecular structure and binding affinity prediction. It preserves Boltz inference behavior while reducing VRAM use with in-place pair updates, CPU offload, reduced precision pair representation, and aggressive chunking.

What is MiniFold?

How MiniFold achieves faster predictions

The result: predictions that complete in seconds rather than minutes, with benchmark accuracy matching ESMFold on CAMEO and CASP datasets.