Protein homology detection by HMM?CHMM comparison

By Johannes Söding. 2005

2,480 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “…the model to predict the effect of changing a single amino acid (e.g. for mutagenesis prediction), we regress l π ( mtAA ) l π ( wtAA ) to the scaled mutational effect where π maps an amino acid to its corresponding index; mt AA is the mutant amino acid; and wt AA is the wildtype amino acid.”
Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, neutral): “…Ladunga, 2017), HMMER (Potter et al., 2018; Prakash, Jeffryes, Bateman, & Finn, 2017), HHblits (Remmert, Biegert, Hauser, & Soding, 2011), HHpred (Soding, 2005; Steinegger et al., 2019)]; protein sequence and domain databases [SCOPe (Fox, Brenner, & Chandonia, 2014), ECOD (Cheng et al., 2014;…”
DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, methodology): “For generating multiple sequence alignments, we follow the approach of using HHsearch [18] followed by JackHmmer [19] as discussed in the DNCON2 method (see our DNCON2 paper for details).”
Protein Structure Determination using Metagenome sequence data (2017, neutral): “for sep_x (0,1,2) for sep_y (1,2,4, 8,16,32) ini_mtx = initialize_matrix (sep_x , sep_y) for gap_e (-0.”
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “Programs to compare To evaluate alignment accuracy, we compare our method, denoted as MRFalign, with sequence-HMM alignment method HMMER [12] and HMM-HMM alignment method HHalign [13].”
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins (2014, methodology): “Current methods for contact prediction use large multiple sequence alignments to identify interacting residues through correlated mutation analysis.”
The HHpred interactive server for protein homology detection and structure prediction (2005, methodology): “This increases the sensitivity for homologous proteins considerably (28).”
Protein threading using residue co-variation and deep learning (2018, methodology): “To evaluate alignment accuracy, we compare our new method DeepThreader with several popular methods including HHpred (Soding, 2005) and CNFpred (the threading method underlying RaptorX; Ma et al., 2012, 2013), as well as EigenTHREADER (Buchan and Jones, 2017), a new threading method built upon…”
Folding membrane proteins by deep transfer learning (2017, methodology): “However, the CAMEO participants include some well-known servers such as Robetta(Kim et al., 2004) and its test version Server19(Ovchinnikov et al., 2017), Phyre(Kelley et al., 2015), RaptorX(Källberg et al., 2012), Swiss-Model(Biasini et al., 2014) and HHpred(Remmert et al., 2012).”