Matteo Cagiada
🇮🇹, Computational biophysicist, NNF Postdoc in #OPIG at University of Oxford | previously PhD and PostDoc at University of Copenhagen (KLL group)
- My first full contribution from my time in @opig.stats.ox.ac.uk is now out! Together with @fspoendlin.bsky.social (and with contributions from King Ifashe), we created FlAbDab and FTCRDab: two large-scale, open molecular dynamics datasets to study flexibility in immune receptors.
- Reposted by Matteo Cagiada[Not loaded yet]
- Reposted by Matteo Cagiada[Not loaded yet]
- Reposted by Matteo Cagiada[Not loaded yet]
- Reposted by Matteo Cagiada[Not loaded yet]
- Reposted by Matteo Cagiada[Not loaded yet]
- Backbone predictions are great - but what about side chains? Me and @emilthomasen.bsky.social are happy to present AF2χ, a tool for predicting side-chain heterogeneity in protein structures!. If you want to read more about it, check out our preprint and localColabFold implementation!
- AlphaFold is amazing but gives you static structures 🧊 In a fantastic teamwork, @mcagiada.bsky.social and @emilthomasen.bsky.social developed AF2χ to generate conformational ensembles representing side-chain dynamics using AF2 💃 Code: github.com/KULL-Centre/... Colab: github.com/matteo-cagia...
- Reposted by Matteo CagiadaAlphaFold is amazing but gives you static structures 🧊 In a fantastic teamwork, @mcagiada.bsky.social and @emilthomasen.bsky.social developed AF2χ to generate conformational ensembles representing side-chain dynamics using AF2 💃 Code: github.com/KULL-Centre/... Colab: github.com/matteo-cagia...
- AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2 biorxiv.org/content/10.1101/202…
- Reposted by Matteo Cagiada[Not loaded yet]
- Delighted to announce that our paper "Predicting absolute protein folding stability using generative models"(lnkd.in/dZJMiY4r) has been awarded the Protein Science BEST PAPER 2024 by @proteinsociety.bsky.social.
- I am happy to share our latest review, which discusses the challenges of predicting unbound antibody structures using deep learning. Special thanks to Alexander Greenshields-Watson for leading and coordinating this work! 🧬💻 doi.org/10.1016/j.sb... #AntibodyEngineering #DeepLearning
- Reposted by Matteo Cagiada[Not loaded yet]
