LatentDE: Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design

Thanh V. T. Tran, Nhat Khang Ngo, Viet Thanh Duy Nguyen, Hy Truong Son

Submission Track: Short Paper

Submission Category: AI-Guided Design

Keywords: Protein design, Directed Evolution, Latent-based optimization, Protein language model

TL;DR: We propose a novel latent-based Directed Evolution algorithm accelerated by Gradient Ascent for efficiently finding new protein sequences with desirable properties.

Abstract: Directed evolution is a powerful but resource-intensive method for optimizing protein functionalities by screening a vast range of mutations. Recent advances in machine learning aim to streamline this process by using surrogate sequence-function models. We propose Latent-based Directed Evolution (LDE), an evolutionary algorithm that efficiently explores high-fitness mutants in the latent space. Built on a regularized variational autoencoder (VAE) and the state-of-the-art Protein Language Model ESM-2, LDE creates a meaningful latent representation of sequences and combines gradient-based methods with directed evolution for effective fitness landscape traversal. Our experimental results on eight protein design tasks show that LDE outperforms existing baseline algorithms. Our source code is publicly available at https://github.com/HySonLab/LatentDE

AI4Mat Journal Track: Yes

Submission Number: 17