Uniclust databases of clustered and deeply annotated protein sequences and alignments

By Milot Mirdita, Lars von den Driesch, Clovis Galiez, Maria Jesus Martin, Johannes Söding, Martin Steinegger. 2016

687 citations recorded by the source corpus.

Abstract We present three clustered protein sequence databases, Uniclust90, Uniclust50, Uniclust30 and three databases of multiple sequence alignments (MSAs), Uniboost10, Uniboost20 and Uniboost30, as a resource for protein sequence analysis, function prediction and sequence searches. The Uniclust databases cluster UniProtKB sequences at the level of 90%, 50% and 30% pairwise sequence identity. Uniclust90 and Uniclust50 clusters showed better consistency of functional annotation than those of UniRef90 and UniRef50, owing to an optimised clustering pipeline that runs with our MMseqs2 software for fast and sensitive protein sequence searching and clustering. Uniclust sequences are annotated with matches to Pfam, SCOP domains, and proteins in the PDB, using our HHblits homology detection tool. Due to its high sensitivity, Uniclust contains 17% more Pfam domain annotations than UniProt. Uniboost MSAs of three diversities are built by enriching the Uniclust30 MSAs with local sequence matches from MMseqs2 profile searches through Uniclust30. All databases can be downloaded from the Uniclust server at uniclust.mmseqs.com. Users can search clusters by keywords and explore their MSAs, taxonomic representation, and annotations. Uniclust is updated every two months with the new UniProt release.

Subfields: Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “MSAs for the baselines are generated from the UniClust30 (Mirdita et al., 2017) database using 3 iterations of search by Hhblits.”
AFsample: improving multimer prediction with AlphaFold using massive sampling (2023, neutral): “• Uniclust30 (Mirdita et al. 2017) version: UniRef30_2021_03.”
Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, methodology): “Database HMMs are built using three iterations of HHblits over UniRef30 (Mirdita et al., 2017), which is a version of the UniRef sequence database (Suzek et al., 2015) clustered into groups of similar sequences at a length coverage of at least 80% and a maximum pair-wise sequence identity of 30%.”
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “0.3 with the UniProt sequence proﬁle database from Oct 2017 [39].”
DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, background): “These contacts can be predicted with relatively high precision for protein sequences that have hundreds to thousands of matches in protein sequence databases such as UNICLUST30 [6] and Uniref100 [7].”
Distance-based protein folding powered by deep learning (2018, methodology): “For CASP13, we generated MSAs (and other sequence features) using the UniClust30 library (46) created in October 2017 and the UniRef sequence database (47) created early in 2018.”
Clustering huge protein sequence sets in linear time (2017, methodology): “For each cluster, we computed the ’worst’ and ’mean’ cluster consistency scores, as described earlier [16].”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “Sequence profiles were generated with HHblits 29 by searching homologous sequences with the default options against Uniclust30, 30 which is a clustered UniProtKB database 31 at the level of 30% pairwise sequence identity, released in September 2016.”