Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 12066-12077· 0 citations· 11 references
Abstract
Protein inverse folding 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 framework that improves functional preservation by fine-tuning models to favor function-preserving sequences over function-disrupting alternatives, avoiding the need for explicit function optimization. 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. The resulting framework guides protein sequence design models toward generating sequences that better preserve functional integrity, while remaining compatible with existing inverse folding pipelines such as ProteinMPNN and ESM-IF. Extensive experiments on protein design benchmarks and enzyme datasets with established wet-lab validation show that our fine-tuned models consistently outperform pretrained counterparts in preserving functional integrity during protein sequence design. The code is available at https://github.com/EvaFlower/Function-aware-Protein-Inverse-Folding
This work introduces a symmetric dual-path architecture that both leverages PLMs for pretrained sequence evolution knowledge and MPLMs for pretrained structural knowledge to iteratively guide protein sequence generation.
Han-Dong Wang, Jiaxin Qi, Baisheng Lai et al.· 0 citations
TTS-Design is proposed, a test-time compute scaling framework that enhances protein sequence design without retraining models or relying on larger training datasets, and can consistently improve sequence recovery and structural reliability across different backbone models, without retraining or increasing model size.
Zizhe Jin, Yi Zheng, Huan Yee Koh et al.· Proceedings of the Thirty-Fi...· 0 citations
EnerBridge-DPO, an energy-aware inverse folding framework that integrates Markov bridge sequence generation with preference optimization for protein complex design, suggests that incorporating energy-related preferences into Markov bridge inverse folding can improve computationally predicted energetic profiles.
Dingyi Rong, Haotian Lu, Xupeng Zhang et al.· Journal of Chemical Informat...· 0 citations
It is shown that blending IF models with a physics-based coarse-grained potential improves global correlation with experimental ΔΔG and, crucially, reduces IF model bias at functional sites, and is found that disease gain-of-function variants show a distinct functional signature from loss-of-function variants.
Ezequiel A. Galpern, Xavier Soler Sanchis, Charles W. J. Pugh et al.· bioRxiv· 0 citations
Inverse FoldDir is a structure-conditioned protein redesign method that combines structural recovery, user control, experimental validation, and a natural route toward future property-guided sampling that performs iterative denoising on the amino acid probability simplex.
Alp Tartici, M. Stojkovic, An-Ru Tian et al.· bioRxiv· 0 citations
Applications to thioredoxins, visual opsins, and Tara Oceans environmental diatom cold-shock proteins show that PLMView can move from interpretable residue-level determinants in well-studied protein families to large-scale environmental functional discovery, linking molecular specialization to ecological distribution and transcriptional deployment across the global ocean.
