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Baylor Scientists Release DeepMVP, an AI That Predicts Protein Modifications and Variant Effects

The model was trained on a curated atlas of 397,524 PTM sites reprocessed from 241 public proteomics datasets.

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Overview

  • Nature Methods published the study on August 26, 2025, reporting superior performance versus eight existing tools.
  • On a curated set of 235 mutation–PTM pairs, DeepMVP predicted the affected site in 81% of cases and the direction of change in 97%.
  • Predictions span six common post-translational modifications across human proteins, with support extending to viral proteins such as SARS‑CoV‑2.
  • The PTMAtlas resource aggregates nearly 400,000 annotated sites across thousands of proteins to provide high-quality training and benchmarking data.
  • The DeepMVP web tool and PTMAtlas are freely available to researchers at deepmvp.ptmax.org.