ArticleNature methods2011
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment.
Article in Nature methods, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,136 papers, 1 of them a synthesis that pooled it.
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Who cites it
1,136 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.Briefings in bioinformatics · 2026Article
- Structural mining and engineering of metagenome-derived Cas12a orthologs expands the CRISPR genome editing and multiplex diagnostics toolkit.Molecular therapy : the journal of the American Society of Gene Therapy · 2026Article
- Incorporating Surfaced-Induced Dissociation Mass Spectrometry Data into an AlphaFold-derived deep learning network improves protein structure prediction.bioRxiv : the preprint server for biology · 2026Article
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1,076 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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