🧬 🔨 Residue-level insights powered by AI. Explore insight for specific amino acids from primary literature #TextMining 🔍 Check out the ⭐ Text Annotations (AI) ⭐ tab on the new #PDBeBeta pages! 🔗 wwwdev.ebi.ac.uk/pdbe/entry/pdb… #AIinBiology #Biology #Science #ARISE #IUCr

Explore 🧭 a residue’s structural environment in the context of its linked annotations. Colour-coding 🖍️residues in the sequence points you to places of interest, go and discover. Try yourself: wwwdev.ebi.ac.uk/pdbe/entry/pdb… #PDBeBeta #ResidueLevelInsights #PDBeValidation #ARISE #IUCr

🌹Meet #ROSIE, our new AI that predicts spatial multi-protein expression straight from routine H&E images. Built on a large co‑staining dataset (>1k samples, 16M+ cells). It enables cell phenotyping & tissue structure insights w/o extra assays.

I will certainly not miss this. Will you? See you at UCL! #CATH #30Years #UCL

🧬💻BSC creates a computational method that reveals previously hidden connections between diseases Researchers observed positive interactions, in which one disease favours the onset of another, such as between asthma and Parkinson's disease; and negative interactions, in which

New Research: COCαDA - a fast and scalable algorithm for interatomic contact detection in proteins using Cα distance matrices frontiersin.org/articles/10.33… #FrontiersIn #Bioinformatics

After months of buildup, it’s finally real! 🎉 The Evolutionary Systems Biophysics Group (ESBG) is officially alive at BSC-CNS. Proud to start this new adventure as a Ramon y Cajal Junior Group Leader🧬 Thanks to all who have been part of this process! tinyurl.com/4r9vf4zx

🚨 My PhD paper is officially published in Genome Biology! 📜🚀 We quantified the efficiency of a key cellular mRNA QC pathway (NMD) 🧬across thousands of human tumors & healthy tissues, revealing its activity is highly variable! 🧵1/8 👇 🔗genomebiology.biomedcentral.com/articles/10.11…

⚙️We developed 2 methods to measure individual-level NMD efficiency (iNMDeff) in TCGA & GTex datasets. This was done using allele-specific expression (ASE) of germline variants & transcript expression of endogenous NMD targets. 📊 2/8

🧠 Nervous system & reproductive tissues: ⬇️ Lower NMD activity. 🩸Gastrointestinal tissues: ⬆️ Higher NMD activity, especially in hypermutated (MSI) tumors. 3/8

What drives this variability? 🤔 1️⃣ Somatic alterations in tumors. A common chromosome 1q gain (affecting NMD genes like SMG5 and SMG7) is linked to⬇️ lower NMD efficiency in ~25% of cancers. 4/8

2️⃣👶Our analysis of inherited rare variants points to the chromatin modifier KDM6B as a key gene that can tune an individual's NMD efficiency. 🌡️ 5/8

Variable NMD efficiency shapes tumor evolution by influencing selective pressures on TSGs like TP53 (⬆️ positive selection in driver nonsense mutations for turmors with ⬆️ NMD activity). 🧬 6/8

Clinically, this is huge: ⬇️ Lower NMD efficiency correlated with ⬆️better survival 👍, higher CD8+ T-cell infiltration🥷, and improved predictive models of immunotherapy response 🛡️🤺! 7/8

In short: individual NMD efficiency is a key factor in health & disease. 🔑 Huge thanks to Fran Supek & the Genome Data Science lab at IRB Barcelona Parc Científic de Barcelona! 🙏 #NMD #Bioinformatics #CancerResearch #Genomics #PhD #tumorevolution 8/8