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.

Chemical proteomics decrypts the kinases that shape the dynamic human phosphoproteome Mass-spectrometry-based phosphoproteomics enables the analysis of thousands of protein phosphorylation events across the human proteome. However, there is a lack of scalable, hypothesis-free, and stat...

Did you ever come across a phosphosite in your proteomics data for which nothing was known? - I bet so!

We have developed a new strategy termed "potency coherence analysis" that leverages the drug potency dimension in decryptM to decode the kinases that shape the human phosphoproteome.

Read more:

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...

The human proteome with direct physical access to DNA Zero-distance photo-crosslinking reveals direct protein-DNA interactions in living cells, enabling quantitative analysis of the DNA-interacting proteome on a timescale of minutes with single-amino-aci...

Excited our paper is out in Cell @cp-cell.bsky.social!
🧬⚡ DNA photo-crosslinking proteomics in living cells
🎯 Pinpoints protein-DNA interactions to single amino acids
🌎 Globally quantifies DNA binding for >1800 proteins at a timescale of minutes
🔗 www.cell.com/cell/fulltex...
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