基于蛋白质语言模型的 KaML-ESM 预测蛋白质电离态与 pKa

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• Published: 24 October 2026

Nature Communications (2026) Cite this article

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Abstract

Ionization states are crucial to protein function, yet predicting their equilibrium constants remains challenging. Here we present KaML-ESMs, sequence-based neural networks built on the ESM protein language models and trained on the GAINES-augmented PKAD-3r dataset. KaML-ESM2/ESMC outperform alternative approaches across four benchmarks, achieving root-mean-square errors of about 0.5 units for Asp/Glu/His/Cys/Tyr/Lys residues in native proteins. Performance degrades on 89 buried engineered mutations lacking evolutionary support, revealing a limitation that supervised training may help overcome. These results suggest that protein sequence, through its evolutionary context, encodes not only structure and function but also, indirectly, electrostatic characteristics. Applying KaML-ESM2 to the entire human proteome, we show that predicted pK_a values can identify functional sites and infer catalytic mechanisms. We offer KaML, a sequence-based pK_a prediction platform supporting biological exploration, protein engineering, and biomolecular simulation. With further development, GAINES could provide a general framework for addressing data scarcity in machine learning.

Acknowledgements

We thank Marius Wiggert (Biohub, formerly EvolutionaryScale, PBC, New York, NY, USA) for advice and for facilitating the use of ESM3 and ESMC. We also thank Biohub for providing computational credits to run the ESMC forge.

Funding

Financial support by the National Institutes of Health (R35GM148261 and R01CA256557) is acknowledged.

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Author notes

1. These authors contributed equally: Mingzhe Shen, Guy W. Dayhoff II.

Authors and Affiliations

1. Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, USA

   Mingzhe Shen, Guy W. Dayhoff II, Daniel Kortzak & Jana Shen

Authors

1. Mingzhe Shen
2. Guy W. Dayhoff II
3. Daniel Kortzak
4. Jana Shen

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Correspondence to Jana Shen.

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Shen, M., Dayhoff II, G.W., Kortzak, D. et al. Predicting protein electrostatics with protein language models. Nat Commun (2026). https://doi.org/10.1038/s41467-026-78406-y

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• Received: 07 March 2026

• Accepted: 21 September 2026

• Published: 24 October 2026

• DOI: https://doi.org/10.1038/s41467-026-78406-y

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