Back to search

Article

Predicting Protein Electrostatics with Protein Language Models

2025-04-21

Abstract excerpt

Ionization states play crucial roles in protein function, yet predicting protein p K a values remains a formidable challenge despite decades of research. Here we present KaML-ESM2 and KaML-ESMC, neural network task heads built on ESM protein language models (pLMs) and trained on the PKAD-3r experimental dataset augmented via GAINES, a latent-space sampling strategy for addressing data scarcity. KaML-ESM2/ESMC sig...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
c44cc247-8766-5241-b4d7-2edb60381525
DOI
10.1101/2025.04.17.649309
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Predicting Protein Electrostatics with Protein Language ModelsDOI 10.1101/2025.04.17.649309
Select a neighboring publication to make it the new centre.