Protein sequence comparison and fold recognition: progress and good-practice benchmarking.

By Johannes Söding, Michael Remmert. 2011

81 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, methodology): “Following standard practices (45), we exclude Rossmann-like folds (c.”
Computational Methods in Drug Discovery (2014, methodology): “More sophisticated fold recognition methods are available if PDB-BLAST does not yield any hits (Kelley and Sternberg, 2009; Soding and Remmert, 2011).”
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment (2011, methodology): “Profile-profile and HMM-HMM alignment methods are the most sensitive class of sequence search methods and are the methods of choice for identifying and aligning templates for 3D homology modeling[4].”
Fast and accurate protein structure search with Foldseek (2022, background): “…ignored all cross-fold hits within the four-bladed to eight-bladed β -propeller superfamilies (SCOPe b.66-b.70) and within https://doi.org/10.1038/s41587-023-01773-0 the Rossman-like folds (c.2–c.5, c.27, c.28, c.30 and c.31) because of the extensive cross-fold homologies within these groups 37 .”
Discriminative modelling of context-specific amino acid substitution probabilities (2012, methodology): “In So¨ding and Remmert (2011), it was argued that it is important to weight down the contribution of FP and TP pairs from large superfamilies to avoid large super-families from dominating the ROC plot.”
rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction obsolete (2018, methodology): “While most secondary structure and solvent accessibility prediction methods have been using 25-30% sequence identity as threshold to separate testing and training sets [51–54], this practice has been discouraged as it has been shown that it is not sufficient to avoid information leakage [55].”
EigenTHREADER: analogous protein fold recognition by efficient contact map threading (2017, background): “If one or more templates can be identified, the 3D structure or structures can then be used as the basis for homology modelling which will result in a predicted structure (So¨ding and Remmert, 2011).”