RegimeFormer: A Large Protein Model of Global Perturbation Regimes

Authors

Ma, S., Chai, Y., Wu, Y., Zhang, Q., Yuan, Y., Zhao, K., Chen, Z., Wang, H., Cao, S., Yu, X., Han, X., Liu, Y., Liu, Y., Zhu, T., Tao, D.

Abstract

Protein language models organize sequence and structure at scale, but a global representation of how proteins respond to mutation remains lacking. We present RegimeFormer, a large protein perturbation model coupled to RegimeAtlas, constructed by harmonizing and indexing 202,556,313 non-redundant protein sequences across the tree of life. A diversity-preserving one-million-protein subset provides the high-resolution training and inference layer, with 995,995 proteins yielding residue-level summaries across 407,048,356 residues and substitution-specific predictions available on demand. Across experimental deep mutational scanning, molecular benchmarks, structural confidence and evolutionary constraint, RegimeFormer identifies reproducible protein-level perturbation regimes that organize residue fragility, adaptability and predictive uncertainty. Regime conditioning improves substitution-specific prediction, with the largest relative gains under unseen-protein, unseen-family and low-homology evaluation. RegimeFormer-derived molecular priors further improve downstream transcriptomic and drug-response modelling. Together, RegimeFormer and RegimeAtlas provide a scalable framework for mapping, predicting and querying protein perturbation landscapes across global sequence space.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 31 Aug 2026.

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