Salient

RegimeFormer: A Large Protein Model of Global Perturbation Regimes

Ma, S., Chai, Y., Wu, Y., Zhang, Q., Yuan, Y., Zhao, K., Chen, Z., Wang, H., Cao, S., Yu, X., et al.
10.64898/2026.08.26.747182 · was preprinted
method development benchmarked
Surfaced because: benchmarked against baselines.
relevance 0.40 openness 0.25 novelty 0.32

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.

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