Discriminative protein sequence modelling with Latent Space Diffusion (2503.18551v1)

Abstract: We explore a framework for protein sequence representation learning that decomposes the task between manifold learning and distributional modelling. Specifically we present a Latent Space Diffusion architecture which combines a protein sequence autoencoder with a denoising diffusion model operating on its latent space. We obtain a one-parameter family of learned representations from the diffusion model, along with the autoencoder's latent representation. We propose and evaluate two autoencoder architectures: a homogeneous model forcing amino acids of the same type to be identically distributed in the latent space, and an inhomogeneous model employing a noise-based variant of masking. As a baseline we take a latent space learned by masked LLMling, and evaluate discriminative capability on a range of protein property prediction tasks. Our finding is twofold: the diffusion models trained on both our proposed variants display higher discriminative power than the one trained on the masked LLM baseline, none of the diffusion representations achieve the performance of the masked LLM embeddings themselves.