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Martin Steinegger 🇺🇦

@martinsteinegger.bsky.social

4.1K followers 520 following 190 posts

Developing data intensive computational methods • PI @ Seoul National University 🇰🇷 • #FirstGen • he/him • Hauptschüler

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · Jul 7

Folddisco finds similar (dis)continuous 3D motifs in large protein structure databases. Its efficient index enables fast uncharacterized active site annotation, protein conformational state analysis and PPI interface comparison. 1/9🧶🧬
📄 www.biorxiv.org/content/10.1...
🌐 search.foldseek.com/folddisco

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Reposted by Martin Steinegger 🇺🇦

Marcel Dickmanns | 🌿❄️🔬 @madic.bsky.social · 1d

You can get MSAs directly from AlphaFold DB now (alphafold.ebi.ac.uk). I also missed the FoldSeek implementation, which lets you search for structurally similar proteins direktly.

This saves some clicking around. Neat!

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Reposted by Martin Steinegger 🇺🇦

Milot Mirdita @milot.bsky.social · 1d

AFDB v6 is already available in Foldseek!

🚀 search.foldseek.com

foldseek databases Alphafold/UniProt50 afdb50 tmp
foldseek easy-search query.cif afdb50 res.m8 tmp --cluster-search 0/1 (Reps only / all 241M)

Thanks @ebi.embl.org and DeepMind for working with us to make it available to everyone.

EMBL-EBI @ebi.embl.org · 1d

We’re renewing our collaboration with Google DeepMind!

We'll keep developing the AlphaFold Database to support protein science worldwide 🎉

To mark the moment we’ve synchronised the database with UniProtKB release 2025_03.

www.ebi.ac.uk/about/news/t...

🖥️🧬 #AlphaFold
@pdbeurope.bsky.social

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 8d

Looking very much forward to it!

Reposted by Martin Steinegger 🇺🇦

RECOMB Conference Series @recombconf.bsky.social · 12d

#RECOMB2026 will be in Thessaloniki, Greece on May 26-29, 2026. Satellites on May 24-25. Save the date!

Το συνέδριο #RECOMB2026 θα πραγματοποιηθεί στη Θεσσαλονίκη, στις 26-29 Μαΐου 2026. Οι δορυφορικές εκδηλώσεις θα διεξαχθούν στις 24-25 Μαΐου 2026. Σημειώστε την ημερομηνία!

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 14d

Thank you Caroline! :)

Reposted by Martin Steinegger 🇺🇦

Yun S. Song @yun-s-song.bsky.social · 16d

We are excited to share GPN-Star, a cost-effective, biologically grounded genomic language modeling framework that achieves state-of-the-art performance across a wide range of variant effect prediction tasks relevant to human genetics.
www.biorxiv.org/content/10.1...
(1/n)

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

Exon finding seems very well suited for GPU acceleration. Worth revisiting exonerate. :)
DP remains very powerful and aligns well with AI approaches, whether via scoring schemes or tokenized data (e.g. Foldseek).

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

Thank you! Yes, it uses DP to compute the maximal ungapped score, followed by a GPU-based Gotoh–Smith–Waterman, so no k-mer index is required. The drawback is that you can’t trade sensitivity for speed, but full DP searches against UniProt in milliseconds open up many exciting applications.

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

Technically yes, but UniProt is highly redundant, so searches against an unclustered database could produce extremely long lists, potentially overwhelming the interface. What's your use-case?

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

One of the shared first authors just joined Bsky. Welcome Alex @achancond.bsky.social

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

This work was only possible through the great work of Felix Kallenborn, Alejandro Chacon, Christian Hundt, Hassan Sirelkhatim, @kdidi.bsky.social, @sooyoung-cha.bsky.social, @machine.learning.bio, @milot.bsky.social, Bertil Schmidt n/n

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

We are currently integrating Grace and Blackwell optimizations and further speeding up the algorithms in MMseqs2-GPU and structure prediction. Below is a sneak peak of our current progress. 5/n
📄 research.nvidia.com/labs/dbr/ass...

research.nvidia.com

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

My first email to Johannes Söding, my later PhD advisor, proposed a GPU-accelerated HHblits. But GPUs in 2012 had many limitations. Now they are widely deployed and massive number crunchers. I am happy that together with @unimainz.bsky.social and NVIDIA we were finally able to build MMseqs2-GPU. 4/n

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

Homology retrieval grounds ML systems to produce reliable predictions. MMseqs2 is already used in Boltz1/2, BioEmu, MSA-Pairformer, Chai-1, BioNeMo, Proteinx, etc. MMseqs2-GPU can enable these and next-gen models to integrate fast homology retrieval for end-to-end GPU inference. 3/n

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

Below we show GPU-accelerated Foldseek, searching 128 structures against AFDB50 (54 million structures). On 128 CPU cores this takes ~120 seconds, whereas a single GPU completes it in ~25 seconds. 2/n

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17d

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

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...

www.nature.com

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Reposted by Martin Steinegger 🇺🇦

Nature Methods @natmethods.nature.com · 19d

GPU-accelerated MMseqs2 offers tremendous speedup for homology retrieval, protein structure prediction with ColabFold, and protein structure search with Foldseek. @martinsteinegger.bsky.social @milot.bsky.social @machine.learning.bio

www.nature.com/articles/s41...

GPU-accelerated homology search with MMseqs2 - Nature Methods

www.nature.com

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Reposted by Martin Steinegger 🇺🇦

BejaLab @bejalab.bsky.social · 21d

EcoFoldDB: Protein Structure-Guided Functional Profiling of Ecologically Relevant Microbial Traits at the Metagenome Scale enviromicro-journals.onlinelibrary.wiley.com/doi/10.1111/...

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Reposted by Martin Steinegger 🇺🇦

Roland Dunbrack 🏳️‍🌈 @rolanddunbrack.bsky.social · 21d

pip install ipsae
from www.linkedin.com/in/ullah-sam...

www.youtube.com/watch?v=A5ph...
PyPI pypi.org/project/ipsae/
His github fork github.com/ullahsamee/I...
My github github.com/DunbrackLab/...
Paper www.biorxiv.org/content/10.1...
For designed protein binders www.biorxiv.org/content/10.1...

Screen binders using ipSAE

YouTube video by ProteinDesignStudio

www.youtube.com

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Reposted by Martin Steinegger 🇺🇦

Kyle Tretina @allthingsapx.bsky.social · 22d

Preprint:
Highly efficient protein structure prediction on NVIDIA RTX Blackwell and Grace-Hopper
nvda.ws/4n4xzz9

Visit the NVIDIA Digital Biology Labs website to find more information like this:
t.co/R9ufEZrGEA

nvda.ws

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Reposted by Martin Steinegger 🇺🇦

Nature Methods @natmethods.nature.com · 22d

Spacedust: a tool for de novo identification of conserved gene clusters from metagenomic data.
@ruoshiz.bsky.social @milot.bsky.social

www.nature.com/articles/s41...

De novo discovery of conserved gene clusters in microbial genomes with Spacedust - Nature Methods

This work presents Spacedust, a tool for de novo identification of conserved gene clusters from metagenomic data.

www.nature.com

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Reposted by Martin Steinegger 🇺🇦

Sofya Garushyants @garushyants.bsky.social · 22d

hey bluesky 👋 visa hurdles mean I’m looking for opportunities outside the US. I’m a computational biologist (bacterial + phage genomics, postdoc in Koonin’s group @ NIH). I am interested in teaming up on funding apps. reach out if this resonates!

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · Sep 6

Was about to write the same.

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · Sep 3

Funny, I had the same question on my mind today.

Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · Sep 3

@pedrobeltrao.bsky.social here you can see the clustering at 90% identity.