Fast and accurate automatic structure prediction with HHpred

By Andrea Hildebrand, Michael Remmert, Andreas Biegert, Johannes Söding. 2009

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In Molecular Biology

Citations in the corpus

Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XII (2017, methodology): “Much of the progress is from the development of methods that use multiple sequence information to improve alignments, for example, 43 and contact prediction and methods that use multiple structure information to choose fragments and substructures for particular regions.”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, neutral): “These 18 servers are BAKER- ROSETTASERVER (Raman et al., 2009), chunk-TASSER (Zhou et al., 2009), chuo-fams (Kanou et al., 2009), CLEF-Server (Shao et al., 2011), FAMSD (Kanou et al., 2009), gws (Joo et al., 2009), HHpredA (Hildebrand et al., 2009), Jiang_Assembly (Hu et al., 2011), MULTICOM-CLUSTER”
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019, neutral): “Predictors generally start by querying public resources 46,50,51 or their own, for structures of protein homologs that can be used as templates for modeling the structure of the target protein.”
The challenge of modeling protein assemblies: the CASP12‐CAPRI experiment (2018, methodology): “These targets were not retained for Round 37 by the CAPRI management, because they comprised complexes for which no adequate templates could be identified using resources such as HHSEARCH 35,36 for at least one of the individual subunits.”
GOMoDo: A GPCRs Online Modeling and Docking Webserver (2013, methodology): “A hidden Markov model (HMM) is then built and calibrated from the multiple alignment using HHmake [19], matched to suitable templates of known structure with HHsearch [19] using the database of HMMs available at ftp:// toolkit.”
Automatic structure prediction of oligomeric assemblies using Robetta in CASP12 (2017, methodology): “In RosettaCM — as well as in this pipeline — modeling begins by using 3 sequence alignment methods (Raptor, 4 Sparks, 5 and HHpred 14 ) to identify related template structures over a wide range of sequence identities.”
Unified framework for modeling multivariate distributions in biological sequences (2019, methodology): “The redundancy was reduced by selecting one random member per connected component, where the edges of the graph were deﬁned using a 1E-10 e-value threshold, computed by HMM-HMM alignment (Hildebrand et al., 2009).”
A structural model of the active ribosome-bound membrane protein insertase YidC (2014, unknown): “coli YidC) was generated using HHPred (Hildebrand et al., 2009).”
A conditional neural fields model for protein threading (2012, background): “Note that when alignment position i corresponds to a pair of residue indices x and y , the alignment position i-1 may correspond to one of the three possible residue index pairs (x-1,y-1) , (x,y-1) or (x-1,y) , depending on the alignment state at position i .”
Protein Modelling: What Happened to the “Protein Structure Gap”? (2013, unknown): “…(Arnold et al., 2009; Haas et al., 2013) Model Archive http://modelarchive.org HHpred http://toolkit.tuebingen.mpg.de/hhpred (Hildebrand et al., 2009) IMP http://www.salilab.org/imp (Russel et al., 2012; Yang et al., 2012) IntFOLD http://www.reading.ac.uk/bioinf/IntFOLD/…”
COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, methodology): “In the absence of an experimentally solved structure, the user can use models generated by the online structure prediction tools, such as I-TASSER (33,34), QUARK (35), Rosetta (36), HHpred (37) or Phyre2 (38).”
Challenging the state-of-the-art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10 (2014, background): “27 OrfYcontains 216 residues yielding a molecular weight of 24 kDa.”