Demystify Protein Generation with Hierarchical Conditional Diffusion Models

TL;DR

This paper introduces a hierarchical conditional diffusion model for protein generation that integrates multi-level structural information and proposes a new evaluation metric, Protein-MMD, to assess the quality and functional fidelity of generated proteins.

Contribution

The paper presents a novel multi-level conditional diffusion framework for protein design and introduces Protein-MMD, a new metric for evaluating generated proteins' quality and condition consistency.

Findings

Effective modeling of hierarchical protein structures.

Improved evaluation of generated proteins with Protein-MMD.

Demonstrated success on benchmark datasets.

Abstract

Generating novel and functional protein sequences is critical to a wide range of applications in biology. Recent advancements in conditional diffusion models have shown impressive empirical performance in protein generation tasks. However, reliable generations of protein remain an open research question in de novo protein design, especially when it comes to conditional diffusion models. Considering the biological function of a protein is determined by multi-level structures, we propose a novel multi-level conditional diffusion model that integrates both sequence-based and structure-based information for efficient end-to-end protein design guided by specified functions. By generating representations at different levels simultaneously, our framework can effectively model the inherent hierarchical relations between different levels, resulting in an informative and discriminative representation of the generated protein. We also propose a Protein-MMD, a new reliable evaluation metric, to evaluate the quality of generated protein with conditional diffusion models. Our new metric is able to capture both distributional and functional similarities between real and generated protein sequences while ensuring conditional consistency. We experiment with the benchmark datasets, and the results on conditional protein generation tasks demonstrate the efficacy of the proposed generation framework and evaluation metric.