Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot): A Framework for Multi-Objective Protein Optimization via Iterative Sequence Generation and Evaluation

Abstract

Proteins play essential roles in nature, from catalyzing biochemical reactions to binding specific targets. Advances in protein engineering have the potential to revolutionize biotechnology and healthcare by designing proteins with tailored properties. Machine learning and generative models have transformed protein design by enabling the exploration of vast sequence-function landscapes. Here, we introduce Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot), a framework that iteratively combines autoregressive protein generation with quantitative structure-property relationship models for fine-tuned optimization. By integrating diverse protein descriptors, SAGE-Prot enhances key properties, including binding affinity, thermal stability, enzymatic activity, and solubility. We demonstrate its effectiveness by optimizing GB1 for binding affinity and thermal stability and TEM-1 for enzymatic activity and solubility. Leveraging curriculum learning, SAGE-Prot adapts rapidly to increasingly complex design objectives, building on past successes. Experimental validation demonstrated that SAGE-Prot-generated proteins substantially outperformed their wild-type counterparts, achieving up to a 17-fold increase in beta-lactamase activity, underscoring SAGE-Prot's potential to tackle critical challenges in protein engineering. As generative models continue to evolve, approaches like SAGE-Prot will be indispensable for advancing rational protein design.