2021
DOI: 10.1371/journal.pcbi.1008798
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Accurate contact-based modelling of repeat proteins predicts the structure of new repeats protein families

Abstract: Repeat proteins are abundant in eukaryotic proteomes. They are involved in many eukaryotic specific functions, including signalling. For many of these proteins, the structure is not known, as they are difficult to crystallise. Today, using direct coupling analysis and deep learning it is often possible to predict a protein’s structure. However, the unique sequence features present in repeat proteins have been a challenge to use direct coupling analysis for predicting contacts. Here, we show that deep learning-… Show more

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Cited by 9 publications
(5 citation statements)
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References 46 publications
(71 reference statements)
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“…We modeled the Spike-hACE2 binding using trRosetta, the current state-of-the-art in rapid and reliable de novo prediction of protein structure. This tool has been used to add more than 6000 protein structures from 41 protein families listed in the pfam database, and it has shown an accuracy over 90% in fidelity with ground truth experimental structures [56,57]. Our results suggest that the protein generated by the proposed recombination event involving ancestral strains of SARS-CoV/ SARS-CoV-2 increased affinity to hACE2.…”
Section: Discussionmentioning
confidence: 82%
“…We modeled the Spike-hACE2 binding using trRosetta, the current state-of-the-art in rapid and reliable de novo prediction of protein structure. This tool has been used to add more than 6000 protein structures from 41 protein families listed in the pfam database, and it has shown an accuracy over 90% in fidelity with ground truth experimental structures [56,57]. Our results suggest that the protein generated by the proposed recombination event involving ancestral strains of SARS-CoV/ SARS-CoV-2 increased affinity to hACE2.…”
Section: Discussionmentioning
confidence: 82%
“…To extract only true contacts from correlated mutations, state-of-the-art contact map predictors employ deep networks ( 21 ). Herein, we used four contact maps predictors, which were identified as accurate ( 21 24 ): RaptorX ( 18 ), TripletRes ( 25 ), trRosetta ( 26 ), and DeepMetaPSICOV ( 27 ).…”
Section: Resultsmentioning
confidence: 99%
“…Accurate prediction of contact maps requires multiple sequence alignments of many diverse and homologous sequences ( 4 , 18 , 24 ); when only a few homologs are available, predictions tend to be of low quality. For example, 148 effective homologs were needed by RaptorX to reach an accuracy of 0.55 (in a 0-through-1 scale) in the top L/5 medium-range contacts in membrane proteins (L being sequence length) ( 18 ).…”
Section: Resultsmentioning
confidence: 99%
“…This study employs deep learning modelling methods to argue for a revised topology for Oca2. Deep learning methods such as DMPfold (Greener, Kandathil, & Jones, 2019), trRosetta (Bassot & Elofsson, 2021) and AlphaFold2 (Jumper et al, 2021) build predicted protein structures by predicting inter residue distances, main chain hydrogen bond network and torsion angles and utilizing these as restraints in the model building process. Benchmarking these methods have demonstrated that they work just as well for membrane proteins as they do for soluble proteins (Greener et al, 2019; Hegedűs, Geisler, Lukács, & Farkas, 2022).…”
Section: Introductionmentioning
confidence: 99%