HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Open accessAbstract
Sequence-based protein function and structure prediction depends crucially on sequence-search sensitivity and accuracy of the resulting sequence alignments. We present an open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM-based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/). Compared to the sequence-search tool PSI-BLAST, HHblits is faster owing to its discretized-profile prefilter, has 50-100% higher sensitivity and generates more accurate alignments.
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The availability of predicted structures may help identify meaningful evolutionary relationships.
“Although some advanced sequence-based methods can already assist in this task39–41, the availability of predicted structures may help identify meaningful evolutionary relationships.”
From Clustering predicted structures at the scale of the known protein universe (2023) · cites this paper as [41]
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“We performed MSA analysis using HHblits from the HH-suite3 software suite ( Suzek et al., 2007 ; Suzek et al., 2015 ; Remmert et al., 2012 ; Steinegger et al., 2019 ), a widely used open-source toolkit known for its sensitivity in detecting sequence similarities and identifying protein folds.”
Scalable emulation of protein equilibrium ensembles with generative deep learning
Lewis, S., Hempel, T., Jiménez Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., García Satorras, V., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.05.626885 · Cited by 349 · see citing works
- Authors
- Remmert, M., Biegert, A., Hauser, A., & Söding, J.
- Affiliations
- Center for Integrated Protein Science Munich (DE)
- Max Planck Society (DE)
- Corresponding author
- Söding, Johannes
- Venue
- Nature Methods
- Published
- 2011-12-25
- Volume
- 9
- Issue
- 2
- Pages
- 173-175
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2051210555
- Cited by
- 2,288
- References
- 24
- Citations by year
- 2026: 110 · 2025: 178 · 2024: 202 · 2023: 213 · 2022: 209 · 2021: 231 · 2020: 211 · 2019: 211 · 2018: 151 · 2017: 148 · 2016: 144 · 2015: 117 · 2014: 72 · 2013: 62 · 2012: 25
- FWCI
- 20.66
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Max Planck Digital Library (submittedVersion, other-oa)
- Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein sequence search, profile hidden Markov models, sequence alignment, protein function prediction, protein structure prediction
- MeSH terms
- Markov Chains, Proteins, Sequence Alignment
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