How can we identify cryptic binding sites that are not visible in the structure of a protein in its ground state? 🧐 In our new Sci Adv paper, we used ML and fine-tuned a Protein language model to identify and predict these cryptic sites directly from sequences. www.science.org/doi/10.1126/...

New #preprint alert! 🧬 MERGE-RNA: a physics-based model to predict #RNA secondary structure ensembles with chemical probing, lead by Giuseppe Sacco, in collaboration with Guido Sanguinetti and Redmond Smyth's lab arxiv.org/abs/2512.20581

Our latest News & Views article with @gervasiolab.bsky.social published in Nature Computational Science. We discuss machine learning of committor-consistent transition pathways. Read our piece here: www.nature.com/articles/s43... And the research article here: www.nature.com/articles/s43...

📢 Paper on the RBP Footprint Grand Challenge—launched at RNA2021 — is out in @rnajournal.bsky.social doi.org/10.1261/rna....! A community-driven benchmark of methods for predicting RBP binding sites, with contributions from @tfroehlking.bsky.social, Mattia Bernetti, and @bussigio.bsky.social

Work done by Ivan Gilardoni, with help from @piompons.bsky.social @tfroehlking.bsky.social and @bussigio.bsky.social Code is available on GitHub github.com/bussilab/MDR... . Play with it using `pip install MDRefine` pypi.org/project/MDRe...

📢 New #preprint describing our community effort to share #plumed tutorials arxiv.org/abs/2412.03595 ! Explore the tutorials at www.plumed-tutorials.org