ArticleJournal of chemical theory and computation2024
Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- DreamFold: A World Model to efficiently generate protein folding pathways in the latent space.Protein science : a publication of the Protein Society · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Combining cutting edge computational and experimental methods for targeting KRAS mutations in non-small cell lung cancer.Expert opinion on drug discovery · 2026Review
- Controllable protein design via autoregressive direct coupling analysis conditioned on principal components.PLoS computational biology · 2026Article
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- Reinforced molecular dynamics: Physics-infused generative machine learning model simulates protein motion.PNAS nexus · 2026Article
- Alternative Conformation Prediction Using Deep Learning With Multi-MSA Strategy and Structural Clustering in CASP16.Proteins · 2026Article
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints.Communications biology · 2025Article
- Recent advances in the inference of deep viral evolutionary history.Journal of virology · 2025Review
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-guided design of dynamic proteins.Science (New York, N.Y.) · 2025Article
- From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning.Structural dynamics (Melville, N.Y.) · 2025Article
- Article
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
- Engineering immunogens that select for specific mutations in HIV broadly neutralizing antibodies.Nature communications · 2024Article
- Multi-Objective Design of DNA-Stabilized Nanoclusters Using Variational Autoencoders With Automatic Feature Extraction.ACS nano · 2024Article
- AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles.Annual review of biomedical data science · 2024Review
- Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase.Journal of chemical theory and computation · 2024Article
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5 authors.
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Abstract
Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 Å of held-out crystal structures, with a consistency higher than that of MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.