MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models

Shrey Goel, Vishrut Thoutam, Edgar Mariano Marroquin, Aaron Gokaslan, Arash Firouzbakht, Sophia Vincoff, Volodymyr Kuleshov, Huong T. Kratochvil, Pranam Chatterjee

Keywords: Membrane Protein Design, Discrete Diffusion

TL;DR: We generate de novo membrane proteins by training a masked discrete diffusion protein language model.

Abstract: Masked Diffusion Language Models (MDLMs) have recently emerged as a strong class of generative models, paralleling state-of-the-art (SOTA) autoregressive (AR) performance across natural language modeling domains. While there have been advances in AR as well as both latent and discrete diffusion-based approaches for protein sequence design, masked diffusion language modeling with protein language models (pLMs) is unexplored. In this work, we introduce MeMDLM, an MDLM tailored for membrane protein design, harnessing the SOTA pLM ESM-2 to de novo generate realistic membrane proteins for downstream experimental applications. Our evaluations demonstrate that MeMDLM-generated proteins exceed AR-based methods by generating sequences with greater transmembrane (TM) character. We further apply our design framework to scaffold soluble and TM motifs, demonstrating that MeMDLM-reconstructed sequences achieve greater biological similarity to their original counterparts compared to SOTA inpainting methods. Finally, we show that MeMDLM captures physicochemical membrane protein properties with similar fidelity as SOTA pLMs, paving the way for experimental applications. In total, our pipeline motivates future exploration of MDLM-based pLMs for protein design.

Submission Number: 19