HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

By Michael Remmert, Andreas Biegert, Andreas Hauser, Johannes Söding. 2011

2,261 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Yang Zhang
2. Jinbo Xu
3. Sheng Wang
4. David T. Jones
5. David Baker
6. Jianlin Cheng
7. Robin Pearce
8. Torsten Schwede

In Spectroscopy

1. Jinbo Xu
2. Yang Zhang
3. Andriy Kryshtafovych
4. David Baker
5. Sheng Wang
6. Robin Pearce
7. Mohammed Alquraishi
8. Torsten Schwede

In Computational Biology & Bioinformatics

1. Jinbo Xu
2. Yang Zhang
3. Sheng Wang
4. David T. Jones
5. Arne Elofsson
6. David Baker
7. Robin Pearce
8. Sergey Ovchinnikov

Citations in the corpus

• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “Efficient sequence search methods have been the computational foundation for extracting biological information from sequences (Altschul et al., 1990; Altschul & Koonin, 1998; Eddy, 1998; Remmert et al., 2011).”
• Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, positive): “For building these MSAs, by default the HHpred server uses three iterations of HHblits over UniRef30 or allows using PSI-BLAST over nr70.”
• SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “The features have been separated into groups provided by the following programs: PSI-BLAST (PSSM), HHblits (HHblits), and SPOT-1D (SPOT-1D).”
• Improved protein structure prediction using predicted interresidue orientations (2019, background): “3) (28) with default parameters at four different e-value cutoffs: 1E-40, 1E-10, 1E-3, and 1.”
• SWISS-MODEL: homology modelling of protein structures and complexes (2018, positive): “For each target, a template search has been performed us-ing HHblits against the SMTL at the time of the CAMEO submission.”
• Uniclust databases of clustered and deeply annotated protein sequences and alignments (2016, methodology): “Hence, we compute HMMs for Uniclust30 clusters from the corresponding Uniboost10 MSAs and search the Pfam-A, SCOP, and pdb70 databases of the HH-suite (16).”
• The SWISS-MODEL Repository—new features and functionality (2016, methodology): “The SMTL library of amino acid sequences is searchable by BLAST (20) and HHBlits (16).”
• Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “Consequently, methods as fast as PSI-BLAST [33] have to be replaced by faster solutions such as HHblits [34].”
• Distance-based protein folding powered by deep learning (2018, methodology): “Two types of input features are used: position specific scoring matrix (PSSM) generated by HHblits 34 and primary sequence represented as a 20×L binary matrix where L is sequence length.”
• NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, methodology): “first exploits the HH-suite software (23), that runs quickly on individual sequences, while the second uses the MMseqs2 software (24), that is optimized for searches on large data sets.”