iiinova — where your digital self keeps evolving

Reposted by Sooyoung Cha

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 26/08/2026

Foldseek-Interface enables fast search/clustering of the protein interface universe! We clustered 3.1M PDB dimers into 77,167 groups and found new interfaces keep appearing even as fold discovery plateaus. 🧵 📄 www.biorxiv.org/content/10.6... 🔎 search.foldseek.com/interface 🌐 interface.foldseek.com

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Reposted by Sooyoung Cha

RSG Korea @rsg-korea.bsky.social · 11/08/2026

🎉 RSG-Korea 2026 1st Community Meetup Bioinformatics & computational biology students/ECRs, join us for networking, career talks & food! 🍪 📅 Aug 28, 13:00–18:00 📍 Yonsei University 🎤 Postdoc Talk Junhyung Cha (Harvard) Dongwook Kim (Lausanne) 👉 Register here: docs.google.com/forms/d/e/1F...

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Reposted by Sooyoung Cha

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17/03/2026

Kieran et al. trained a generative protein binder design model. The training is based on Teddymer, a dataset developed by @sooyoung-cha.bsky.social. By treating monomer domains as multimers and clustering them with Foldseek, she created a set that allowed Complexa to learn. 💾 teddymer.foldseek.com

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Reposted by Sooyoung Cha

Kieran Didi @kdidi.bsky.social · 17/03/2026

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

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Reposted by Sooyoung Cha

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17/03/2026

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 📄 research.nvidia.com/labs/dbr/ass... 🌐 alphafold.ebi.ac.uk

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Reposted by Sooyoung Cha

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 21/09/2025

MMseqs2-GPU sets new standards in single query search speed, allows near instant search of big databases, scales to multiple GPUs and is fast beyond VRAM. It enables ColabFold MSA generation in seconds and sub-second Foldseek search against AFDB50. 1/n 📄 www.nature.com/articles/s41... 💿 mmseqs.com

nature.com

GPU-accelerated homology search with MMseqs2 - Nature Methods

Graphics processing unit-accelerated MMseqs2 offers tremendous speedups for homology retrieval from metagenomic databases, query-centered multiple sequence alignment generation for structure predictio...

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Reposted by Sooyoung Cha

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 20/01/2025

Foldseek 10 with 4-27x (1-8 GPUs) faster search through MMseqs2-GPU. Faster ProstT5 protein search w/o structure prediction through multi-GPU/Apple Metal, new BFVD/BFMD databases and multimer clustering (preview). 💾 github.com/steineggerla... 📄 www.biorxiv.org/content/10.1... 🐍 available in bioconda

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Reposted by Sooyoung Cha

Milot Mirdita @milot.bsky.social · 27/11/2024

MMseqs2 Release 16 Highlights: GPU-accelerated search📄, ORF or new 6-frame translated search modes, contig taxonomy always keeps the longest ORF, bug fixes (reduced memory and higher sensitivity) and relicensed as MIT 📄 biorxiv.org/content/10.1... 💾 mmseqs.com and 🐍Bioconda 🖥️🧬🧶

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