用多目标对齐方法让蛋白质设计兼顾结构准确与可开发性。
Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment
- 通过偏好对齐框架微调预训练模型，同时优化多个开发属性。
- 在多种任务中提升可溶性、稳定性等属性，且不损失结构保真度。
- 适合需要高效设计高开发性蛋白的科研与制药人员使用。
蛋白质序列设计需在可设计性（即恢复目标骨架的能力）与多种常冲突的开发属性（如溶解性、热稳定性、表达量）间取得平衡。现有方法依赖事后突变、推理时偏置或针对特定属性重新训练，但具有目标依赖性，需大量领域知识或精细调参。本文提出ProtAlign，一种多目标偏好对齐框架，通过半在线直接偏好优化与灵活偏好边界，微调预训练逆折叠模型，在保持结构保真度的同时满足多样开发目标。该框架利用体外属性预测器构建偏好对，应用于广泛使用的ProteinMPNN骨架，得到MoMPNN模型，在CATH 4.3晶体结构、从头生成骨架及真实结合剂设计任务中均提升开发性，且不牺牲可设计性，为实际蛋白质序列设计提供了实用框架。
原文摘要 · Abstract (English)
Protein sequence design must balance designability, defined as the ability to recover a target backbone, with multiple, often competing, developability properties such as solubility, thermostability, and expression. Existing approaches address these properties through post hoc mutation, inference-time biasing, or retraining on property-specific subsets, yet they are target dependent and demand substantial domain expertise or careful hyperparameter tuning. In this paper, we introduce ProtAlign, a multi-objective preference alignment framework that fine-tunes pretrained inverse folding models to satisfy diverse developability objectives while preserving structural fidelity. ProtAlign employs a semi-online Direct Preference Optimization strategy with a flexible preference margin to mitigate conflicts among competing objectives and constructs preference pairs using in silico property predictors. Applied to the widely used ProteinMPNN backbone, the resulting model MoMPNN enhances developability without compromising designability across tasks including sequence design for CATH 4.3 crystal structures, de novo generated backbones, and real-world binder design scenarios, making it an appealing framework for practical protein sequence design.
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