From Structure to Function: Preference Alignment for Function-Aware Protein Inverse Folding

Tamatgar Nilufer, Soobin Park, Yinghua Yao, Xixian Chen, Yuangang Pan

Keywords: Protein Inverse Folding, Preference Alignment, Function-Aware Protein Design, Preference Optimization

Abstract: Protein inverse folding (IF) models conditioned on structure achieve high sequence recovery but often fail to preserve biological function due to the lack of functional supervision. We propose a Function-aware Preference Alignment (FPA) framework that avoids explicit function optimization by fine-tuning IF models to prefer function-preserving sequences over function-disrupting alternatives. Our approach constructs reliable preference pairs in silico using hypothesis-driven perturbations of critical residues and model-consistent likelihood constraints, enabling scalable supervision without additional wet-lab measurements. Our FPA framework guides protein sequence design models toward generating sequences that better preserve functional integrity, while remaining model-agnostic and compatible with existing inverse folding pipelines such as ProteinMPNN and ESM-IF. Extensive experiments on protein design benchmarks show that our fine-tuned models significantly outperform pretrained counterparts in preserving functional integrity during protein sequence design.

Submission Number: 85