Year: 2026
Type: article
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 p K a values can identify functional sites and infer catalytic mechanisms. We offer KaML, a sequence-based p K 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.
Source: Nature Communications
Institution: University of Maryland, Baltimore
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W7223460039
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