Geometric deep learning assists protein engineering. Opportunities and Challenges

TL;DR

Geometric deep learning revolutionizes protein engineering by enabling more accurate, interpretable, and efficient computational design methods that address traditional limitations in sequence space complexity and experimental validation costs.

Contribution

This paper reviews current applications, recent methodological advances, and proposes a unified framework integrating GDL with explainable AI for protein design.

Findings

GDL improves stability prediction and functional annotation accuracy.

Recent advances enhance model generalization and interpretability.

GDL is central to next-generation protein engineering and synthetic biology.

Abstract

Protein engineering is experiencing a paradigmatic shift through the integration of geometric deep learning into computational design workflows. While traditional strategies, such as rational design and directed evolution, have enabled relevant advances, they remain limited by the complexity of sequence space and the cost of experimental validation. Geometric deep learning addresses these limitations by operating on non-Euclidean domains, capturing spatial, topological, and physicochemical features essential to protein function. This perspective outlines the current applications of GDL across stability prediction, functional annotation, molecular interaction modeling, and de novo protein design. We highlight recent methodological advances in model generalization, interpretability, and robustness, particularly under data-scarce conditions. A unified framework is proposed that integrates GDL with explainable AI and structure-based validation to support transparent, autonomous design. As GDL converges with generative modeling and high-throughput experimentation, it is emerging as a central technology in next-generation protein engineering and synthetic biology.