ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.

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

SUBMITTER: Sevgen E 

PROVIDER: S-EPMC12541330 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature