Multi-state Protein Design with DynamicMPNN. arxiv.org/abs/2507.21938

Proteins aren't static; why should our designs be? 🔄 🧬 DynamicMPNN trained on pairs of structures, crafting sequences that snap between states (+13% RMSD >ProteinMPNN on 94‑proteins) What's the most exciting application? metamorphic enzymes, transporters, or hinges?? #AI4Bio

Multi-state Protein Design with DynamicMPNN 1. DynamicMPNN is a groundbreaking inverse folding model that generates protein sequences compatible with multiple conformations. It outperforms ProteinMPNN by up to 13% on structure-normalized RMSD, demonstrating significant

🚨 Protein designers: we keep saying we're getting really good at making static, rock-like proteins... Introducing Dynamic MPNN - our first step towards truly multi-state and 'programmable' protein design! Great collab with KnowlesLab Cambridge, led my Alex Abrudan and @rumbacarumba

📢 I ’m thrilled to share the first pre-print of my PhD, co-authored with Sebastian Pujalte! Our work presents the preliminary results on "Multi-state Protein Design with DynamicMPNN". 📄 Paper: lnkd.in/dfs4v3Qt 💻 Github: lnkd.in/dgntqbKR This  project has been

DynamicMPNN SE(3)等変性を保持したGeometric Vector Perceptionを採用した、タンパク質のmulti-stateを同時に満たす配列設計手法の提案 Multi-state Protein Design with DynamicMPNN arxiv.org/abs/2507.21938 実装 github.com/Alex-Abrudan/D…

Model Agnostic Conditioning of Boltzmann Generators for Peptide Cyclization biorxiv.org/content/10.110… #biorxiv_biophys

🧬Check out our new preprint “Model Agnostic Conditioning of Boltzmann Generators for Peptide Cyclization,” introducing CycLOPS (a Cyclic Loss for the Optimization of Peptide Structures) 📝Preprint: biorxiv.org/content/10.110… Code (coming soon): github.com/benjfdup/cyclo… (🧵 1/7)

Introducing gRNAde: our own little "AlphaGo Moment" for RNA design! 🧬🚀 📝: tinyurl.com/gRNAde-paper Unlike proteins, RNA design has long relied on "wisdom of the crowd" (human experts) or the slow crawl of directed evolution — gRNAde changes that! 🧵👇