Protein structures in the PDB and in molecular dynamics simulations rarely describe a single rigid shape. Loops open and close, termini wander, domains breathe, and binding sites can appear only in a subset of conformations. AlphaFlow models that variability by turning AlphaFold-style structure prediction into a generative ensemble method.
AlphaFlow, developed by Jing, Berger, and Jaakkola, includes two model families. AlphaFlow uses AlphaFold with multiple sequence alignments. ESMFlow uses ESMFold and runs from sequence alone. ProteinIQ supports both model families: ESMFlow for single-sequence ensemble generation, and AlphaFlow for MSA-backed AlphaFold-style ensemble generation.
For a single best-guess structure, ESMFold or AlphaFold 2 is usually the clearer choice. AlphaFlow is useful when the question is about plausible conformational spread rather than one static model.
How to use AlphaFlow online
AlphaFlow runs online in ProteinIQ by selecting an input mode, providing one protein sequence, choosing a matching model variant, and generating a conformational ensemble as downloadable PDB files. ESMFlow mode needs only a sequence. AlphaFlow mode accepts the same sequence plus an optional .a3m multiple sequence alignment, and generates the alignment automatically when none is uploaded.
Inputs
Input
Accepted format
Notes
Protein sequence
FASTA, raw amino acid sequence, .fasta, .fa, or .txt
One protein sequence between 10 and 1600 residues. Standard amino acids are expected.
Pre-computed MSA
Optional .a3m
Appears only after AlphaFlow input mode is set to AlphaFlow. The uploaded alignment must be valid A3M with the query sequence first. Invalid .a3m files fail clearly rather than falling back to generated MSA.
RCSB fetch
PDB ID, for example 1UBQ
ProteinIQ fetches FASTA from RCSB when a PDB ID is used. The resulting sequence, not the PDB coordinates, is used for ESMFlow inference.
Job name
Text
Optional label for finding the run later.
AlphaFlow on ProteinIQ is designed for single-chain protein sequences. Multimer interfaces, ligand-bound states, membranes, cofactors, and chain-specific contacts are not modeled directly from the input. When a PDB ID is fetched from RCSB, the sequence is used as the input and the deposited coordinates are not used as a structural template.
A3M validation
Uploaded .a3m alignments are checked before the job starts. ProteinIQ accepts permissive A3M content, but rejects files that would make the AlphaFold-style feature pipeline ambiguous.
Check
Requirement
FASTA headers
Each record starts with a non-empty header line beginning with >.
Sequence records
Every header has at least one sequence line.
Characters
Sequences use plausible A3M characters: amino acid letters, lowercase insertion letters, -, ., or *.
Aligned length
After stripping lowercase insertion characters, every record has the same aligned column length.
Query sequence
The first record has a non-empty ungapped query sequence after removing lowercase insertions and gap-like characters.
If no .a3m is uploaded in AlphaFlow mode, ProteinIQ generates an MSA server-side with an MMseqs2 workflow compatible with ColabFold-style A3M output. The generated alignment is returned with the results so the run can be inspected or repeated.
Before running
Trim expression tags and purification handles: Flexible His-tags or long artificial linkers can dominate the ensemble and make biologically relevant motion harder to see.
Keep domains interpretable: Multi-domain proteins can be useful, but long flexible linkers may cause large global rearrangements. Running individual domains can make local flexibility easier to inspect.
Check the sequence alphabet: Ambiguous residues such as X, B, Z, or U should be resolved before inference when possible.
Start with 10 samples: The default gives enough structures to spot repeated motion without making the first run slow. Increase to 25 or 50 when comparing domain movement, loop states, or binding-site exposure.
Human ubiquitin (1UBQ, 76 residues) is a useful sanity-check target because the beta-grasp fold should remain compact while termini and surface loops vary modestly across samples. A run where the core unfolds or most samples look unrelated is a warning sign to inspect the input sequence and settings.
Model settings
Setting
Default
Description
AlphaFlow input mode
ESMFlow
Controls both the input form and the available model variants. ESMFlow uses sequence only. AlphaFlow shows the optional .a3m upload and otherwise generates an MSA server-side.
Model variant
ESMFlow MD Base in ESMFlow mode, AlphaFlow MD Base in AlphaFlow mode
Selects the checkpoint. The menu is scoped to the selected input mode, so ESMFlow variants appear only in ESMFlow mode and AlphaFlow variants appear only in AlphaFlow mode.
Number of samples
10
Number of PDB conformations to generate, from 1 to 50. More samples give a better view of ensemble spread, but runtime scales roughly linearly with sample count.
MD models learn conformational variation from molecular dynamics trajectories. PDB models learn diversity across experimental structures. Base models are the full checkpoints. Distilled models are faster and automatically use the distilled inference settings.
Choosing a model variant
Variant
Best fit
Practical tradeoff
ESMFlow MD Base
Studying thermal flexibility, mobile loops, termini, and domain breathing around physiological conditions
Recommended default. Slower than distilled models, but closest to the full MD-trained ESMFlow behavior.
ESMFlow PDB Base
Looking for experimental-state diversity, such as alternative crystal forms or condition-dependent conformations
Can reflect broader structural heterogeneity than MD-like local motion.
ESMFlow MD Distilled
Fast exploratory ensemble generation when many sequences need screening
Faster, with some accuracy loss relative to the base model. Uses No diffusion and Noisy first automatically.
ESMFlow PDB Distilled
Fast comparison of experimental-ensemble-like conformations
Useful for triage, not the best choice for final structural interpretation.
AlphaFlow MD Base
MSA-backed molecular-dynamics-like ensembles
Uses AlphaFold-style MSA features. Slower and more expensive than ESMFlow, but closer to the original AlphaFlow method.
AlphaFlow MD Distilled
Faster MSA-backed MD-like exploratory ensembles
Uses the distilled inference mode.
AlphaFlow PDB Base
MSA-backed experimental-state ensemble diversity
Enables Self conditioning and MSA resampling by default for PDB-style sampling.
AlphaFlow PDB Distilled
Faster MSA-backed PDB-like ensembles
Uses PDB-style sampling plus the distilled inference mode.
Advanced settings
Setting
Default
Description
Inference steps
10
Number of denoising steps, from 2 to 50. The standard setting is 10. Lower values truncate generation and can reduce diversity. Higher values cost more runtime.
Diversity (tmax)
1.0
Starting point in the generation schedule, from 0.2 to 1.0. 1.0 samples from the full schedule. Lower values keep samples closer together.
Self conditioning
Off
Enables --self_cond. AlphaFlow PDB variants enable it automatically for PDB-style sampling.
No diffusion
Off
Enables --no_diffusion. Distilled variants enable this automatically.
Noisy first
Off
Enables --noisy_first. Distilled variants enable this automatically.
The two controls that most users should change first are Number of samples and Model variant. Inference steps, Diversity (tmax), and the boolean inference flags are better treated as reproducibility controls. Changing them changes the sampling procedure, not just the display.
Results
ESMFlow returns one primary PDB file per sample. AlphaFlow returns a canonical multi-model PDB containing the full sampled ensemble, plus individual sample PDB files for convenient viewing. When AlphaFlow uses an uploaded or generated .a3m, that alignment is returned as a secondary downloadable file for reproducibility.
Output
Meaning
sample_01.pdb, sample_02.pdb, ...
Individual predicted conformations in PDB format. Each file is a complete protein model for the same input sequence.
{name}.pdb
AlphaFlow modes only. Multi-model PDB containing the sampled ensemble.
{name}.a3m
AlphaFlow modes only. Uploaded or generated MSA used for inference.
Model variant
Checkpoint used for the run, such as esmflow_md_base or esmflow_pdb_distilled.
Sample index
Position of the sampled conformation in the generated ensemble. It is not a confidence rank.
Inference settings
steps, tmax, self_cond, no_diffusion, and noisy_first are recorded so runs can be compared later.
AlphaFlow does not return a pLDDT confidence table or a binding score. The value of the run is in the ensemble itself: how similar the structures are, which regions move, and whether the sampled conformations suggest more than one plausible state.
Pattern in the ensemble
Likely interpretation
Follow-up check
Core secondary structure overlaps across most samples
The model has a consistent fold hypothesis
Inspect loops, termini, and domain interfaces rather than whole-protein RMSD alone.
One loop repeatedly opens or closes
The loop may be mobile or weakly constrained by sequence context
Compare with known active-site loops, disorder predictions, or mutagenesis data.
Two domains stay folded but change relative orientation
Hinge-like motion may be plausible
Align one domain first, then measure displacement of the other domain.
Only one sample shows an extreme rearrangement
The structure is more likely an outlier than a stable alternate state
Look for the same movement in additional samples before using it for docking or design.
Most samples are globally inconsistent
The input may be too long, disordered, or outside the model's reliable regime
Try domain-level constructs or compare a single-structure prediction with ESMFold.
Interpreting AlphaFlow ensembles
An AlphaFlow output should be read as a set of plausible conformations, not as a ranked list where sample 1 is best. The sample index only records generation order.
Large differences across samples usually point to regions where the model sees conformational uncertainty or mobility. In practice, the most useful checks are visual:
Stable core: Helices and sheets that overlap across samples are more likely to represent a consistent fold.
Flexible loops and termini: Disordered ends and solvent-exposed loops often spread widely. That spread is expected and should not be treated as a modeling failure by itself.
Domain motion: If two domains remain internally stable but shift relative to each other, the ensemble may suggest hinge-like motion.
Binding-site exposure: A pocket that appears in only some samples can be useful for hypothesis generation, especially before docking or mutagenesis planning.
Outlier samples: A single highly distorted sample should not outweigh the rest of the ensemble. Inspect whether the same movement appears repeatedly.
Quantitative RMSD analysis is useful after the job, but RMSD needs a reference choice. Whole-protein RMSD can be dominated by flexible tails. For many proteins, aligning the stable core first and then measuring loop or domain movement gives a clearer interpretation.
How AlphaFlow works
AlphaFlow trains structure predictors as flow-matching models. During training, the model sees an interpolation between a noisy protein backbone representation and a target structure:
xt=(1−t)⋅x0+t⋅x
Here, x0 is sampled from a harmonic prior, x1 is the target structure, and t marks progress along the denoising path. At inference time, the model starts from a noisy configuration and repeatedly predicts a cleaner structure along the schedule.
ESMFlow applies the same idea to ESMFold. Because ESMFold is a single-sequence model, ESMFlow can run without MSA generation. That makes it practical for fast web execution and for proteins where homologous sequence coverage is weak.
AlphaFlow mode uses AlphaFold-style MSA features. A supplied .a3m file is used directly after validation. Without an upload, ProteinIQ generates the alignment first, then writes it into the directory layout expected by the AlphaFlow inference code before sampling structures. That extra MSA step is the main reason AlphaFlow mode is slower than ESMFlow mode.
Training data behind the model variants
Training set
What it captures
Interpretation consequence
MD
Molecular dynamics trajectories from ATLAS at 300K
Better for local thermal motion, loop mobility, and dynamic fluctuations around an existing fold.
PDB
Experimental structures deposited under different conditions
Better for alternative crystallographic or cryo-EM states, ligand-bound differences, and broader experimental heterogeneity.
The distinction matters. An MD-trained model may emphasize physically local fluctuations. A PDB-trained model may sample larger state changes if similar state diversity appears in experimental structures.
Measuring flexibility from an existing molecular dynamics trajectory
Works from simulated trajectory data instead of generating conformations from sequence.
AlphaFlow fits best between structure prediction and simulation. It can suggest flexible regions worth checking before setting up molecular dynamics, docking, mutagenesis, or construct design. It should not replace experimental dynamics data when NMR, HDX-MS, cryo-EM heterogeneity, or high-quality MD trajectories are available.
Practical limits
MSA generation can dominate runtime: AlphaFlow modes require MSA features. Uploaded .a3m files make runs more reproducible; automatic MMseqs2 generation depends on external search availability.
No ligand or multimer conditioning: The input is a protein sequence. Ligands, cofactors, membranes, partner chains, and DNA or RNA binding partners are not included in generation.
Backbone-centered ensemble interpretation: The method is most useful for backbone conformational diversity. Side-chain packing and small binding-site rearrangements need follow-up validation.
Long sequences cost more: Memory and runtime increase sharply with sequence length. ProteinIQ accepts up to 1600 residues, but shorter constructs are usually easier to interpret.
Samples are hypotheses: Repeated motion across several samples is more informative than a single unusual conformation. Experimental or simulation evidence is still needed before claiming a functional state.
