MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
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The dataset was clustered using MMseqs2 at minimum 30% sequence identity to avoid bias towards a certain class in the test set.
“All individual chains in each category were then clustered using MMseqs2(16) using a minimum sequence identity of 30%, and the centerpoints of each cluster were selected.This was to avoid bias towards a certain class in the test set.”
From Reliable Identification of Homodimers Using AlphaFold (2025) · cites this paper as [16]
ColabFold's homology search is 40-60-fold faster than AlphaFold2's.
“ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit.”
From ColabFold - Making protein folding accessible to all (2021) · cites this paper as [10]
The first clustering step used MMseqs2 to cluster 214,684,311 structures into 52,327,413 sequence clusters at 50% sequence identity and 90% sequence alignment overlap.
“The first step involved using MMseqs2 (ref. 23) to cluster the database on the basis of 50% sequence identity and a 90% sequence alignment overlap of both sequences, resulting in 52,327,413 clusters.”
From Clustering predicted structures at the scale of the known protein universe (2023) · cites this paper as [23]
Sequence searching is four to five orders of magnitude faster: an all-versus-all comparison of 100 million sequences would take MMseqs2 only around a week on the same cluster.
“Sequence searching is four to five orders of magnitude faster: an all-versus-all comparison of 100 million sequences would take MMseqs2 (ref. 6) only around a week on the same cluster.”
From Fast and accurate protein structure search with Foldseek (2024) · cites this paper as [6]
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- Authors
- Steinegger, M., & Söding, J.
- Affiliations
- Max Planck Institute for Biophysical Chemistry (DE)
- Corresponding author
- Söding, Johannes
- Venue
- Nature Biotechnology
- Published
- 2017-10-16
- Volume
- 35
- Issue
- 11
- Pages
- 1026-1028
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2950954328
- Cited by
- 4,840
- References
- 24
- Citations by year
- 2026: 1165 · 2025: 1239 · 2024: 966 · 2023: 591 · 2022: 400 · 2021: 238 · 2020: 141 · 2019: 64 · 2018: 26 · 2017: 8 · 2012: 2
- FWCI
- 26.71
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung
- Seoul National University
- Horizon 2020 Framework Programme
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- MMseqs2, sequence clustering, big data, bioinformatics software
- MeSH terms
- Algorithms, Database Management Systems, Software, Sequence Alignment, Sequence Analysis, Protein, Databases, Protein
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