ArticleNature communications2023
Latent generative landscapes as maps of functional diversity in protein sequence space.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A structure-aware generative AI framework for revealing functional relationships in protein families.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Leveraging latent space models for enzyme discovery and sampling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Decoding cryptic defluorinases through a latent generative sequence landscape.Chemical science · 2026Article
- AUKAT: Conditional VAE-Driven Augmentation and Neural Modeling of Enzyme Turnover Numbers.Biomolecules · 2026Article
- Classification of SARS-CoV-2 Variants Through the Epistatic Circos Plots with Convolutional Neural Networks.Journal of molecular evolution · 2026Article
- FireProtASR 2.0: evolution-guided Design of Protein Ancestors and Successors with phylogenetics and machine learning.Briefings in bioinformatics · 2026Article
- INBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-DependentACS synthetic biology · 2025Article
- Generative Landscapes and Dynamics to Design Functional Multidomain Artificial Transmembrane Transporters.ACS central science · 2025Article
- Integrating experimental feedback improves generative models for biological sequences.Nucleic acids research · 2025Article
- Considering Metabolic Context in Enzyme Evolution and Design.Biochemistry · 2025Review
- Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance.bioRxiv : the preprint server for biology · 2025Article
- Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis.Journal of chemical theory and computation · 2025Article
- Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills.PLoS computational biology · 2025Article
- Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics.JACS Au · 2025Article
- Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025Review
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
- How deep can we decipher protein evolution with deep learning models.Patterns (New York, N.Y.) · 2024Article
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.
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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.