ArticlePLoS computational biology2021
Accurate contact-based modelling of repeat proteins predicts the structure of new repeats protein families.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 12 citations in OpenAlex.
- A Proposal for a Consolidated Structural Model of the CagY Protein ofInternational journal of molecular sciences · 2023Article
- Article
- Recombination and lineage-specific mutations linked to the emergence of SARS-CoV-2.Genome medicine · 2021Article
- Gram-negative outer-membrane proteins with multiple β-barrel domains.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- TRAL 2.0: Tandem Repeat Detection With Circular Profile Hidden Markov Models and Evolutionary Aligner.Frontiers in bioinformatics · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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-based methods (trRosetta, DeepMetaPsicov (DMP) and PconsC4) overcomes this problem and can predict intra- and inter-unit contacts in repeat proteins. In a benchmark dataset of 815 repeat proteins, about 90% can be correctly modelled. Further, among 48 PFAM families lacking a protein structure, we produce models of forty-one families with estimated high accuracy.
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