HH-suite3 for fast remote homology detection and deep protein annotation

By Martin Steinegger, Markus Meier, Milot Mirdita, Harald Voehringer, Stephan J. Haunsberger, J. Soeding. 2019

1,015 citations recorded by the source corpus.

HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite. The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Accurate prediction of protein structures and interactions using a 3-track neural network (2021, positive): “WeusedRoseTTAFoldtopredictthep101G bg binding domain (GBD) structure in a hetero-dimeric PI3K g complex.”
Multi-State Modeling of G-protein Coupled Receptors at Experimental Accuracy (2021, methodology): “Protein structure prediction for a state was guided by the input database for the state, as a replacement of the PDB70 database.(30) Databases were used in two ways.”
Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “For the mammalian proteins from Negatome, seven out of 1733 single chains were redundant according to Uniprot (C4ZQ83, I0LJR4, I0LL25, K4CRX6, P62988, Q8NI70, Q8T3B2), 34 had no matching species in the MSA pairing, 106 produced out of memory exceptions during prediction using a GPU with 40 Gb RAM, 3”
Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, methodology): “In many such cases, the more sensitive sequence search method HHpred (Steinegger et al., 2019), which is based on the comparison of profile hidden Markov models (HMMs), is able to establish connections to remotely homologous, characterized proteins.”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “We first searched homologous protein sequences of a given protein using hhblits available in the hh-suite [57] .”
Modeling aspects of the language of life through transfer-learning protein sequences (2019, background): “Analyzing data sets involving millions of proteins, i.e. samples of the human gut microbiota or metagenomic samples, have already become a major challenge [35].”
Learning inverse folding from millions of predicted structures (2022, methodology): “Then, we constructed our MSAs using hhblits (Steinegger et al., 2019) on this filtered version of UniRef100.”
Fold Evolution before LUCA: Common Ancestry of SH3 Domains and OB Domains (2021, methodology): “The MSAs were compared by computing the HHalign probability of homology.”
CAMEO - Perspectives on the future of fully automated evaluation of structure prediction methods (2021, neutral): “0 (Steinegger et al. 2019) against Uniclust30 (2018_08) (Mirdita et al.”
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, methodology): “, 2012) from the HH-suite package (Steinegger et al., 2019) is used to search against the UniClust30 database (Mirdita et al.”