Leveraging biological experimental mutation and functional data to validate an AI-based protein design method

Abstract

Protein mutation design is a pivotal technology for precise regulation of protein functions, with significant applications in biomedicine and industrial enzyme engineering. Traditional experimental methods face limitations such as lengthy cycles, high costs, and low mutation-site hit rates. The emergence of artificial intelligence (AI) offers innovative solutions to these challenges. This study systematically analyzes ProtSSN, an AI-based protein mutation design software developed by Professor Hong Liang’s team at Shanghai Jiao Tong University. Using public datasets, the research verifies the algorithm’s predictive accuracy and investigates its structure-sensing mechanism, elucidating its advantages and limitations in practical applications. The study validates ProtSSN’s performance through quantitative experiments and innovatively explores the correlation between protein secondary structures, Solvent Accessible Surface Area (SASA), and mutation effects. Findings reveal that ProtSSN integrates protein sequence semantics and 3D structural topology via a dual-modal pre-training framework. Leveraging Equivariant Graph Neural Networks (EGNN), it quantifies structural features (e.g., hydrophobic cores in α-helices, hydrogen bonds in β-sheets) to analyze mutation-induced perturbations. ProtSSN’s lightweight architecture overcomes computational bottlenecks of traditional molecular simulations, enhancing wet-lab mutant screening efficiency for industrial enzyme optimization and antibody affinity maturation. However, the model’s handling of dynamic irregular loops requires improvement, suggesting future integration of molecular dynamics or expanded training data for specialized proteins. A multi-dimensional evaluation framework confirms ProtSSN’s efficacy in structure-driven mutation design, establishes sequence-structure-function correlations, and provides a reusable methodology for AI protein tool development. This work advances protein engineering from empirical trial-and-error toward a computational paradigm, with potential applications in enzyme catalyst design and therapeutic antibody development.