ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design - Emre Sevgen; Joshua Moller; Adrian Lange; John Parker; Sean Quigley; Jeff Mayer; Poonam Srivastava; Sitaram Gayatri; David Hosfield; Clayton Dilks; Claire Buchanan; Thomas Speltz; et al. (Proceedings of the National Academy of Sciences, 2025)
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 γ -carbonic anhydrase enzyme with a melting temperature elevation of ΔT m = +61 ° C relative to the most thermostable sequence reported to date and activity in 23% v/v methyl diethanolamine at pH 11.25 and 93 ° C corresponding to industrially relevant conditions for enzymatic carbon capture technologies. The ProT-VAE model presents a powerful and experimentally validated platform for machine learning-guided directed evolution campaigns to discover synthetic proteins with engineered function.