ArticleProceedings of the National Academy of Sciences of the United States of America2025
ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Leveraging latent space models for enzyme discovery and sampling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Generative models for antimicrobial peptide design: auto-encoders and beyond.BioData mining · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- PHA synthase variant design using a conditional variational autoencoder.PLoS computational biology · 2026Article
- Review
- From latent dynamics to data geometry: Nonlinear diffusion modelling for protein structures.Computational and structural biotechnology journal · 2026Article
- Machine learning in chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- DeepProtein: deep learning library and benchmark for protein sequence learning.Bioinformatics (Oxford, England) · 2025Article
- Generative Landscapes and Dynamics to Design Functional Multidomain Artificial Transmembrane Transporters.ACS central science · 2025Article
- Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder.Nature communications · 2025Article
- Article
- Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation.Journal of chemical information and modeling · 2025Article
- SyntheticmicroPublication biology · 2025Article
- ProT-GFDM: A generative fractional diffusion model for protein generation.Computational and structural biotechnology journal · 2025Article
- A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.Briefings in bioinformatics · 2024Article
- AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.Briefings in bioinformatics · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.ACS synthetic biology · 2023Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
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18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep generative models have demonstrated success in learning the protein sequence to function relationship and designing synthetic sequences with engineered functionality. We introduce the Protein Transformer Variational AutoEncoder (ProT-VAE) as an accurate, generative, fast, and transferable model for data-driven protein design that blends the merits of variational autoencoders to learn interpretable, low-dimensional latent embeddings for conditional sequence design with the expressive, alignment-free featurization offered by transformer-based protein language models. We implement the model using NVIDIA's BioNeMo framework and validate its performance in retrospective functional prediction and prospective functional design. The model identifies a phenylalanine hydroxylase enzyme with 2.5× catalytic activity over wild-type, and a
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