MMseqs2 desktop and local web server app for fast, interactive sequence searches

By Milot Mirdita, Martin Steinegger, Johannes Söding. 2018

524 citations recorded by the source corpus.

The MMseqs2 desktop and web server app facilitates interactive sequence searches through custom protein sequence and profile databases on personal workstations. By eliminating MMseqs2’s runtime overhead, we reduced response times to a few seconds at sensitivities close to BLAST. Availability and implementation The app is easy to install for non-experts. Source code, prebuilt desktop app packages for Windows, macOS and Linux, Docker images for the web server application, and a demo web server are available at https://search.mmseqs.com. Contact martin.steinegger@mpibpc.mpg.de or soeding@mpibpc.mpg.de

Subfields: Ecology, Molecular Biology

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Citations in the corpus

ColabFold: making protein folding accessible to all (2022, background): “Mirdita et al. 11 improved it to also support fast single-against-many searches.”
Design of a specific peptide against phenolic glycolipid-1 from Mycobacterium leprae and its implications in leprosy bacilli entry (2022, methodology): “,(26) which differs from the original implementation from Deepmind(27) by replacing the homology detection of AlphaFold2 with MMseqs2 (many-against-many sequence searching).(28) This 3D model was subjected to a minimisation stage using the GROMACS package(29) for 50000 steps using a steep descent al”
A taxonomic note on the genus Lactobacillus: Description of 23 novel genera, emended description of the genus Lactobacillus Beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. (2020, methodology): “3 [37] with MMseqs2 version d36de [38] as the sequence search program.”
NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning (2022, methodology): “In fact, when comparing this dataset to our training and validation sets using the MMseqs2 easy-cluster tool (10), no hit with >20% sequence identity and 50% coverage was found.”
Fast and accurate protein structure search with Foldseek (2022, methodology): “The Foldseek webserver is based on the MMseqs2 webserver 43 .”
Using machine learning to study protein–protein interactions: From the uromodulin polymer to egg zona pellucida filaments (2021, methodology): “This notebook (https:// github.com/sokrypton/ColabFold) uses multiple sequence alignments produced by MMseqs2 (Mirdita et al., 2019) as input for template ‐ free structure prediction by AlphaFold2.”
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, methodology): “The MMseqs2 method [43] was selected on ColabFold to generate the MSAs, and Amber relaxation of models was disabled.”
PDBe: improved findability of macromolecular structure data in the PDB (2019, methodology): “PDBe have implemented a sequence-clustering process, using the MMseqs software suite (30), that groups structures based on sequence identity at various thresholds: 100%, 95%, 90%, 70%, 50%, 40% and 30%.”