ArticleNature communications2019
Deciphering protein evolution and fitness landscapes with latent space models.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- Functional alignment of protein language models via reinforcement learning.Nature communications · 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
- FireProtASR 2.0: evolution-guided Design of Protein Ancestors and Successors with phylogenetics and machine learning.Briefings in bioinformatics · 2026Article
- Heuristic multi-site optimization for protein sequence design using Masked Protein Language Models.PLoS computational biology · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Peptide turnover prediction using transformer architectures on large-scale time-series proteomic data.BMC genomics · 2026Article
- Review
- A Structural Landscape Depiction of Dynamic Stability Centers of Local Structure in Protein Thermostability Engineering.Research (Washington, D.C.) · 2026Article
- Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models.bioRxiv : the preprint server for biology · 2025Article
- ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Generative Landscapes and Dynamics to Design Functional Multidomain Artificial Transmembrane Transporters.ACS central science · 2025Article
- Exploring the space of self-reproducing ribozymes using generative models.Nature communications · 2025Article
- Network analysis of multivariate time series data in biological systems: methods and applications.Briefings in bioinformatics · 2025Article
- Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics.JACS Au · 2025Article
- A metric and its derived protein network for evaluation of ortholog database inconsistency.BMC bioinformatics · 2025Article
- Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025Review
- Fast protein structure searching using structure graph embeddings.Bioinformatics advances · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Protein sequences contain rich information about protein evolution, fitness landscapes, and stability. Here we investigate how latent space models trained using variational auto-encoders can infer these properties from sequences. Using both simulated and real sequences, we show that the low dimensional latent space representation of sequences, calculated using the encoder model, captures both evolutionary and ancestral relationships between sequences. Together with experimental fitness data and Gaussian process regression, the latent space representation also enables learning the protein fitness landscape in a continuous low dimensional space. Moreover, the model is also useful in predicting protein mutational stability landscapes and quantifying the importance of stability in shaping protein evolution. Overall, we illustrate that the latent space models learned using variational auto-encoders provide a mechanism for exploration of the rich data contained in protein sequences regarding evolution, fitness and stability and hence are well-suited to help guide protein engineering efforts.
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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.