AlphaFold Meets Flow Matching for Generating Protein Ensembles
read the original abstract
The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. We repurpose highly accurate single-state predictors such as AlphaFold and ESMFold and fine-tune them under a custom flow matching framework to obtain sequence-conditoned generative models of protein structure called AlphaFlow and ESMFlow. When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling. When further trained on ensembles from all-atom MD, our method accurately captures conformational flexibility, positional distributions, and higher-order ensemble observables for unseen proteins. Moreover, our method can diversify a static PDB structure with faster wall-clock convergence to certain equilibrium properties than replicate MD trajectories, demonstrating its potential as a proxy for expensive physics-based simulations. Code is available at https://github.com/bjing2016/alphaflow.
This paper has not been read by Pith yet.
Forward citations
Cited by 5 Pith papers
-
ENSEMBITS: an alphabet of protein conformational ensembles
Ensembits creates a discrete vocabulary for protein conformational ensembles that outperforms static tokenizers on dynamics prediction tasks and enables ensemble token prediction from single structures via distillation.
-
ENSEMBITS: an alphabet of protein conformational ensembles
Ensembits is the first tokenizer of protein conformational ensembles that outperforms static tokenizers on RMSF prediction and matches them on function and mutation tasks while using less pretraining data.
-
Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schr\"odinger Samplers
Derives a conditional-marginal entropy-rate objective for bridge-aware discretization that yields U-shaped schedules and improves low-NFE sample quality on 2D, CIFAR-10, and protein tasks.
-
ConforNets: Latents-Based Conformational Control in OpenFold3
ConforNets use channel-wise affine transforms on pre-Pairformer pair latents in OpenFold3 to achieve state-of-the-art unsupervised generation of alternate protein states and supervised conformational transfer across families.
-
Learning residue level protein dynamics with multiscale Gaussians
DynaProt predicts per-residue 3x3 covariance matrices for local flexibility and scalar pairwise covariances for dynamic coupling from static protein structures using an SE(3)-invariant Gaussian framework, achieving hi...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.