Predicting protein electrostatics with protein language models

• Article
• Open access
• Published: 24 October 2026

Nature Communications (2026) Cite this article

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

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.

Author information

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

Corresponding author

Correspondence to Jana Shen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

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

Download citation

• Received: 07 March 2026

• Accepted: 21 September 2026

• Published: 24 October 2026

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

注意转载授权 · 阅读原文