Excited to share our new Prosit-PTM paper is out! A deep learning model that predicts MS2 spectra and Retention time for modified peptides, using chemically informed encodings and data augmentation based on amino acid substitution to learn general rules for how PTMs affect spectra.

Hello Toronto!🍁The Terrific TUM Team is happy to be attending #HUPO2025. Kusterlab, Wilhelmlab and Leelab have a diverse set of presentations for you that we can't wait to share 🤓.
We're excited to spend the next days reuniting with old friends and making new connections. See you there!

Koina: Democratizing machine learning for proteomics research - Nature Communications Koina is an open-source, online platform that simplifies access to machine learning models in proteomics, enabling easier integration into analysis tools and helping researchers adopt and reuse ML mod...

Exited to share our latest work! Out now in @natcomms.nature.com

Koina aims to transform how #proteomics uses machine learning. You no longer need to be a tech wizard to use ML and now can easily run #ML models. Integrated with FragPipe, Skyline and EncyclopeDIA!

www.nature.com/articles/s41...