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Does your designed active site already exist in nature? Is an uncharacterized protein hiding a catalytic site or a pocket? Folddisco answers both, searching millions of structures for a 3D motif in seconds. @natbiotech.nature.com ๐Ÿงฌ ๐Ÿ“„ www.nature.com/articles/s41... ๐Ÿงต1/7๐Ÿ‘‡
Now published in NSMB! Paper: doi.org/10.1038/s415... Full PDF: rdcu.be/fhBtI Overview of additions since the preprint๐Ÿ‘‡ (1/5)
Folddisco enables protein structural motif search in million scale databases.
www.nature.com
Structural motif search across the protein universe with Folddisco - Nature Biotechnology
4d
1mo
doi.org
This work introduces the Runs Nโ€™ Poses dataset for benchmarking deep learning methods on the proteinโ€“ligand complex prediction task. It shows that current methods rely on memorization, challenging the...
Evaluating generalization in proteinโ€“ligand cofolding methods - Nature Structural & Molecular Biology
Peter ล krinjar
Excited to share our latest preprint evaluating AlphaFold3, Boltz-1, Chai-1 and Protenix for predicting protein-ligand interactions, featuring our newly introduced benchmark dataset ๐ŸŒนRuns Nโ€™ Poses๐ŸŒน! www.biorxiv.org/content/10.1... ๐Ÿงต๐Ÿ‘‡ (1/n)
Feb 8, 2025
Peter ล krinjar
Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with other molecules, especially small mo...
www.biorxiv.org
Have protein-ligand co-folding methods moved beyond memorisation?
AlphaFold database has entered the era of complexes. Together with NVIDIA, DeepMind and EBI, we use ColabFold, OpenFold and MMseqs2-GPU to predict ~31 million complexes (homo & hetro-dimers) resulting in 1.8 million high-quality predictions ๐Ÿ“„ research.nvidia.com/labs/dbr/ass... ๐ŸŒ alphafold.ebi.ac.uk
2mo
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Martin Steinegger ๐Ÿ‡บ๐Ÿ‡ฆ
Introducing nail - a Rust implementation of profile HMM sequence alignment for proteins. Near-HMMER sensitivity, but a lot faster: www.biorxiv.org/content/10.1... github.com/TravisWheele...
11d
Microbial GAIN domains undergo autoproteolysis and enable release of diverse cell surface associated proteins https://www.biorxiv.org/content/10.64898/2026.05.12.724683v1
My latest work! We found that the autoproteolytic GAIN domain which mediates force responsive signaling in adhesion GPCRs is not unique to eukaryotes. The microbial counterparts anchor diverse adhesion, enzymatic, and toxin domains to the cell surface, enabling release by likely mechanical stimuli.