From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

2025-01-05

AI Summary

Protein de novo design has long faced challenges in jointly optimizing structural stability and functional specificity. This work introduces the first E(3)-equivariant diffusion generative framework tailored for autonomous protein engineering, unifying denoising diffusion probabilistic models (DDPMs) and score-matching paradigms in protein conformational space. By integrating thermodynamic constraints with 3D geometric priors, it enables joint backbone–sidechain modeling and coordinate-conditioned generation. Crucially, it guarantees physical rigid-body invariance of generated structures and supports diverse tasks—including peptide design and protein–ligand co-generation. Experiments demonstrate substantial improvements: a 12% increase in predicted local distance difference test (pLDDT) scores, a 35% reduction in predicted folding free energy error, and enhanced structural validity. These advances significantly advance the reliability and practical deployment of generative AI in biomacromolecular design.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation.

Problem

• Protein Design
• Stability
• Three-Dimensional Structure

Innovation

• Diffusion Models
• Protein Design
• Generative AI