Introduction
For two decades, the central goal of structural biology was to determine the structure of a protein — a single set of coordinates. AlphaFold2 and its successors have substantially expanded access to predicted protein structures, although confidence and accuracy vary across proteins and regions. But proteins are not statues. They breathe, flex, and cycle between functionally distinct conformational states — open/closed enzymes, active/inactive receptors, inward/outward-facing transporters, folded/disordered regions. Function is motion. A single static coordinate set captures, at best, one snapshot of a distribution, and can actively mislead when the biologically relevant state is a minor or transient one. This survey organizes the field by approach family — from static baselines through physics-based simulation, generative machine-learning ensemble models, cryo-EM heterogeneity analysis, and integrative multi-state modeling of disordered systems — giving for each what it is, how it works, key tools, evidence, strengths, and limitations, closing with an comparison of practical strengths and limitations.
Static structure prediction as the baseline
End-to-end deep-learning models that map a protein sequence (plus, usually, an MSA) to a single 3D structure — the modern baseline every dynamics method builds on or reacts against.
How the method works
AlphaFold2 uses an attention-based "Evoformer" over MSA and pairwise residue representations feeding a structure module that produces atomic coordinates end-to-end. RoseTTAFold uses a three-track architecture coupling 1D/2D/3D reasoning. ESMFold replaces the MSA with a protein language model (ESM-2), predicting structure from a single sequence at metagenomic scale. AlphaFold3 generalizes to a diffusion-based generative head over all-atom coordinates, extending to protein–ligand, protein–nucleic-acid, and multi-chain complexes.
Key tools. AlphaFold2; AlphaFold3; RoseTTAFold / RoseTTAFold All-Atom; ESMFold (ESM-2); open re-implementations (OpenFold; Boltz-1/2).
Evidence. AlphaFold2 achieved median backbone accuracy near experimental error at CASP14; ESMFold folded hundreds of millions of metagenomic sequences; AlphaFold3 extended high accuracy to biomolecular interactions.
Limitations — why a single structure is insufficient for dynamics. These models are trained to reproduce the one deposited PDB structure and by default return a single high-confidence conformation. They do not report populations, do not sample alternative functional states, and give no free energies or kinetics. For allosteric enzymes, GPCRs, transporters, and disordered regions — where the distribution of states is the biology — a single prediction is at best incomplete. This gap motivates every family below.
Citations. Jumper et al. 2021, Nature — 10.1038/s41586-021-03819-2 (PMID 34265844); Abramson et al. 2024, Nature (AlphaFold 3) — 10.1038/s41586-024-07487-w (PMID 38718835); Baek et al. 2021, Science (RoseTTAFold) — 10.1126/science.abj8754; Lin et al. 2023, Science (ESMFold) — 10.1126/science.ade2574.
Physics-based molecular dynamics & enhanced sampling
Simulation of protein motion by numerically integrating Newton's equations under a molecular force field. MD is the only family that produces genuine time-ordered trajectories — real "movies" — with physically grounded free energies and kinetics.
How the method works
An empirical force field (AMBER, CHARMM, OPLS) assigns energies to bonds/angles/torsions/non-bonded terms; atoms are propagated in femtosecond steps. Because functional transitions occur on µs–s timescales, the field relies on enhanced sampling: metadynamics adds history-dependent bias along collective variables; replica-exchange MD (REMD) swaps temperature replicas to cross barriers; accelerated MD raises low-energy regions to speed rare events. Markov State Models (MSMs) stitch many short trajectories into a kinetic network of metastable states. AlphaFold-seeded MD uses predicted structures as diverse starting points. Special-purpose hardware (Anton, D. E. Shaw) reaches millisecond all-atom trajectories directly.
Key tools. Engines: GROMACS, AMBER, CHARMM, NAMD, OpenMM. Force fields: ff14SB, CHARMM36. Enhanced sampling: metadynamics (PLUMED), REMD, accelerated MD, weighted-ensemble/string methods. MSMs: PyEMMA, MSMBuilder. Hardware: Anton.
Evidence. Anton produced the first atomic-level millisecond folding and conformational-change trajectories; MSMs matured into a rigorous statistical framework; metadynamics/REMD/aMD are standard, heavily validated free-energy and rare-event methods.
Strengths. Physically principled; yields free energies, kinetics, and mechanism — the gold standard for how a protein moves, transition pathways, and coupling (e.g., ion/substrate coupling in transporters, ligand-induced allostery).
Limitations. Expensive (large systems / long timescales); force-field inaccuracies (especially IDPs and some ion interactions); enhanced sampling needs well-chosen collective variables; large collective transitions can remain under-sampled. This cost/timescale wall is what generative ML tries to leapfrog.
Citations. Laio & Parrinello 2002, PNAS (metadynamics) — 10.1073/pnas.202427399; Sugita & Okamoto 1999, Chem Phys Lett (REMD) — 10.1016/S0009-2614(99)01123-9; Hamelberg et al. 2004, JCP (aMD) — 10.1063/1.1755656; Husic & Pande 2018, JACS (MSM) — 10.1021/jacs.7b12191; Shaw et al. 2010, Science (Anton) — 10.1126/science.1187409; Abraham et al. 2015, SoftwareX (GROMACS) — 10.1016/j.softx.2015.06.001; Maier et al. 2015, JCTC (ff14SB) — 10.1021/acs.jctc.5b00255.
Generative / ML conformational ensemble models
Deep generative models that sample many conformations for a sequence, aiming to approximate the equilibrium (Boltzmann) distribution — populations included — orders of magnitude faster than MD. Crucial caveat: most are equilibrium-ensemble generators (states and populations, no time axis), not true trajectory/"movie" models. Only a few (e.g., MDGen) generate time-ordered dynamics, and those remain small-scale.
How the method works
Sub-strategies: (a) MSA manipulation of AlphaFold — subsampling/clustering the input MSA induces AF2 to output alternative functional states (AF-Cluster; subsampling recovers transporter/receptor endpoints and approximate populations). (b) Repurposing folding networks as samplers — AlphaFlow/ESMFlow retrain under a flow-matching objective; EigenFold and Str2Str use diffusion/score-based sampling. (c) Learned Boltzmann samplers — Boltzmann Generators (normalizing flows with exact reweighting); DiG (Distributional Graphormer) predicts equilibrium distributions; BioEmu emulates equilibrium ensembles at scale. (d) Force-guided diffusion — ConfDiff injects force guidance into SE(3) diffusion. (e) Trajectory generation — MDGen models MD trajectories directly (true dynamics, tetrapeptide-scale).
Key tools. AF-Cluster; MSA-subsampling (del Alamo; AFsample2; subsampled AF2); AlphaFlow/ESMFlow; EigenFold; Str2Str; ConfDiff; Boltzmann Generators; DiG; idpGAN; BioEmu; MDGen.
Evidence. MSA-subsampled AF2 recovers both outward- and inward-facing transporter states across many transporters and GPCRs. BioEmu reports thousands of conformers per GPU-hour, capturing cryptic pockets and domain motions at accuracy approaching long MD but ~10⁵× faster. Boltzmann Generators demonstrated correctly reweighted equilibrium sampling. AF-Cluster resolved a known fold-switching protein's two states.
Strengths. Dramatically cheaper than MD; produce diverse conformers and (for the best methods) calibrated populations; excellent for cryptic-pocket discovery and structural-prior/hypothesis generation.
Limitations. Most give no kinetics or time ordering; population calibration is uneven; large collective transitions (full transporter alternating-access) are systematically under-sampled by equilibrium generators; many are single-chain (BioEmu is single-chain, Cα-level); accuracy is bounded by PDB training bias toward stable states.
Citations. Wayment-Steele et al. 2024, Nature (AF-Cluster) — 10.1038/s41586-023-06832-9; del Alamo et al. 2022, eLife (MSA subsampling) — 10.7554/eLife.75751; Kalakoti & Wallner 2025, Commun Biol (AFsample2) — 10.1038/s42003-025-07791-9; Monteiro da Silva et al. 2024, Nat Commun — 10.1038/s41467-024-46715-9; Zheng et al. 2024, Nat Mach Intell (DiG) — 10.1038/s42256-024-00837-3; Noé et al. 2019, Science (Boltzmann Generators) — 10.1126/science.aaw1147; Janson et al. 2023, Nat Commun (idpGAN) — 10.1038/s41467-023-36443-x; Jing et al. 2023 (EigenFold) — arXiv:2304.02198; Lu et al. 2024 (Str2Str) — arXiv:2306.03117; Jing et al. 2024 (AlphaFlow/ESMFlow) — arXiv:2402.04845; Wang et al. 2024 (ConfDiff) — arXiv:2403.14088; Lewis et al. 2025, Science (BioEmu) — 10.1126/science.adv9817; Jing et al. 2024 (MDGen) — arXiv:2409.17808.
Cryo-EM conformational heterogeneity
Methods that extract multiple conformational states — or continuous motion — from a single cryo-EM dataset, treating structural heterogeneity as signal. Each of the hundreds of thousands to millions of imaged particles is a frozen snapshot of a distinct conformation; the task is to recover the distribution.
How the method works
cryoDRGN trains a VAE that learns a continuous low-dimensional latent space of conformations; cryoDRGN-AI extends this to neural ab initio reconstruction. 3D Variability Analysis (3DVA) in cryoSPARC solves for linear variability components (continuous flexibility + discrete states). 3DFlex models the map as a deformable canonical density plus a learned flow field. ManifoldEM places particle snapshots on a manifold to retrieve functional pathways and energy landscapes. RECOVAR uses regularized covariance estimation with kernel regression. DynaMight estimates per-particle deformations.
Key tools. cryoSPARC (+ 3DVA + 3DFlex); cryoDRGN / cryoDRGN-AI; ManifoldEM; RECOVAR; DynaMight; RELION (classification-based discrete heterogeneity).
Evidence. cryoDRGN resolved compositional and conformational heterogeneity in ribosome and spliceosome datasets; 3DFlex resolved continuous membrane-protein motion (e.g., TRPV1); ManifoldEM recovered functional energy landscapes from experimental snapshots.
Strengths. Grounded in real experimental data (not a model prior); resolves both discrete states and continuous motion; the definitive way to observe the conformational distribution a specific sample actually populates; extends to native context via cryo-ET.
Limitations. Requires a good particle stack; states below the reconstruction floor (rare conformers, or those lost to vitrification / air-water-interface bias) never enter the data; independent comparisons show learned deformation densities can diverge from ground truth, so orthogonal validation (smFRET, HDX-MS) is advisable.
Citations. Zhong et al. 2021, Nat Methods (cryoDRGN) — 10.1038/s41592-020-01049-4; Levy et al. 2025, Nat Methods (cryoDRGN-AI) — 10.1038/s41592-025-02720-4; Punjani & Fleet 2021, J Struct Biol (3DVA) — 10.1016/j.jsb.2021.107702; Punjani & Fleet 2023, Nat Methods (3DFlex) — 10.1038/s41592-023-01853-8; Dashti et al. 2020, Nat Commun (ManifoldEM) — 10.1038/s41467-020-18403-x; Gilles & Singer 2025, PNAS (RECOVAR) — 10.1073/pnas.2419140122; Schwab et al. 2024, Nat Methods (DynaMight) — 10.1038/s41592-024-02377-5; Punjani et al. 2017, Nat Methods (cryoSPARC) — 10.1038/nmeth.4169.
Intrinsically disordered proteins & integrative multi-state modeling
Methods for proteins (or regions) with no single folded structure — intrinsically disordered proteins (IDPs) — and integrative approaches combining sparse experimental measurements into an ensemble or multi-state model. Here the "structure" is fundamentally a distribution.
How the method works
IDP ensembles are built by generating large candidate-conformer pools then selecting/reweighting a sub-ensemble whose averaged observables match experiment. Flexible-meccano generates disordered pools from residue-specific backbone statistics; the Ensemble Optimization Method (EOM) selects a sub-ensemble to fit SAXS; ENSEMBLE optimizes against combined NMR (chemical shifts, RDCs, PREs) and SAXS restraints. More broadly, the Integrative Modeling Platform (IMP) formalizes Bayesian combination of heterogeneous data (EM, cross-linking MS, SAXS, FRET, prior structures) to determine multi-state structures of large assemblies. ML generators (idpGAN) now emit disordered ensembles directly.
Key tools. Flexible-meccano; EOM (SAXS); ENSEMBLE (NMR+SAXS); IMP (integrative/Bayesian); idpGAN; NMR/SAXS-restrained MD.
Evidence. SAXS-based EOM and NMR-restrained ensembles are the established route to quantitative IDP descriptions; IMP underlies landmark integrative structures of large assemblies (e.g., the nuclear pore complex).
Strengths. Directly experiment-restrained; principled treatment of proteins with no single structure; IMP scales to very large heterogeneous assemblies; naturally multi-state.
Limitations. Under-determined — many ensembles fit the same averaged data, so results need maximum-entropy/regularization and are not unique; heavily dependent on data quality/quantity; labor-intensive; force-field and pool-generation biases propagate.
Citations. Russel et al. 2012, PLoS Biol (IMP) — 10.1371/journal.pbio.1001244; Bernadó et al. 2007, JACS (EOM) — 10.1021/ja069124n; Krzeminski et al. 2013, Bioinformatics (ENSEMBLE) — 10.1093/bioinformatics/bts701; Ozenne et al. 2012, Bioinformatics (Flexible-meccano) — 10.1093/bioinformatics/bts172; Jensen et al. 2014, Chem Rev — 10.1021/cr400688u.
Choosing an approach
- No single method wins; the field is converging on hybrids. The most reliable multi-state models in 2026 combine families: a fast generative or MSA-subsampled prior proposes states, physics-based MD/enhanced sampling gives mechanism and free energies, and experimental data (cryo-EM heterogeneity, NMR/SAXS, smFRET, HDX-MS) validates and anchors. Any purely computational ensemble is a hypothesis until experimentally checked.
- Static prediction and dynamics answer different questions. A predicted structure provides a starting hypothesis; it does not by itself establish conformational populations, kinetics or alternative functional states.
- Generative ensemble models are the fastest-moving frontier — with real caveats. BioEmu, DiG, AlphaFlow, MSA-subsampling deliver conformers at a tiny fraction of MD cost and are excellent for cryptic-pocket discovery. But most produce equilibrium ensembles, not trajectories; population calibration is uneven; large collective transitions are under-sampled. "Protein movie" claims are frequently over-stated — true trajectory generators (MDGen) remain peptide-scale.
- MD remains the gold standard for mechanism — the only family giving time-ordered pathways, kinetics, and free energies — at a cost generative models race to undercut.
- Cryo-EM heterogeneity is the strongest experimental grounding — cryoDRGN(-AI), 3DVA/3DFlex, RECOVAR, DynaMight recover multi-state distributions directly from data, but only for states that enter the particle stack, and learned densities need orthogonal validation.
Applying the methods with BioMate
BioMate operates here as an execution platform, not a new algorithm. On its AWS-Batch compute layer it provides indexed, production cryo-EM workflows — including single-particle processing and conformational-heterogeneity analysis (the cryoSPARC / cryoDRGN-family methods described in the cryo-EM section) — letting users resolve multiple states and continuous motion directly from their own particle data. It additionally offers a generative conformational-ensemble capability (generative equilibrium ensemble generation) producing many candidate conformers for a sequence, useful for cryptic-pocket discovery and as structural priors. BioMate's contribution is making these published methods runnable end-to-end at scale with live progress and downloadable results — orchestration and accessibility of the state of the art, not a proprietary replacement for it. The methodological limits described throughout (equilibrium ensembles are not trajectories; computational ensembles need experimental validation; large collective transitions are hard) apply equally to results produced on the platform.