scholarly journals Direct coupling analysis of epistasis in allosteric materials

2020 ◽  
Vol 16 (3) ◽  
pp. e1007630
Author(s):  
Barbara Bravi ◽  
Riccardo Ravasio ◽  
Carolina Brito ◽  
Matthieu Wyart
Keyword(s):  
Direct Coupling ◽  
Coupling Analysis ◽  
Direct Coupling Analysis

scholarly journals Estimation of Residue-Residue Coevolution using Direct Coupling Analysis Identifies Many Native Contacts Across a Large Number of Domain Families

2012 ◽  
Vol 102 (3) ◽  
pp. 250a ◽  
Author(s):  
Faruck Morcos ◽  
Andrea Pagnini ◽  
Bryan Lunt ◽  
Arianna Bertolino ◽  
Debora Marks ◽  
...  
Keyword(s):  
Direct Coupling ◽  
Coupling Analysis ◽  
Direct Coupling Analysis

scholarly journals Accurate contact-based modelling of repeat proteins predicts the structure of new repeats protein families

2021 ◽  
Vol 17 (4) ◽  
pp. e1008798
Author(s):  
Claudio Bassot ◽  
Arne Elofsson
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
High Accuracy ◽  
Unique Sequence ◽  
Direct Coupling ◽  
Protein Families ◽  
Coupling Analysis ◽  
Repeat Proteins ◽  
Eukaryotic Proteomes ◽  
Direct Coupling Analysis

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.

scholarly journals FilterDCA: interpretable supervised contact prediction using inter-domain coevolution

2019 ◽  
Author(s):  
Maureen Muscat ◽  
Giancarlo Croce ◽  
Edoardo Sarti ◽  
Martin Weigt
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Structures ◽  
Direct Coupling ◽  
Sequence Information ◽  
Coupling Analysis ◽  
Contact Patterns ◽  
Direct Coupling Analysis ◽  
Training Sets

AbstractPredicting three-dimensional protein structure and assembling protein complexes using sequence information belongs to the most prominent tasks in computational biology. Recently substantial progress has been obtained in the case of single proteins using a combination of unsupervised coevolutionary sequence analysis with structurally supervised deep learning. While reaching impressive accuracies in predicting residue-residue contacts, deep learning has a number of disadvantages. The need for large structural training sets limits the applicability to multi-protein complexes; and their deep architecture makes the interpretability of the convolutional neural networks intrinsically hard. Here we introduce FilterDCA, a simpler supervised predictor for inter-domain and inter-protein contacts. It is based on the fact that contact maps of proteins show typical contact patterns, which results from secondary structure and are reflected by patterns in coevolutionary analysis. We explicitly integrate averaged contacts patterns with coevolutionary scores derived by Direct Coupling Analysis, reaching results comparable to more complex deep-learning approaches, while remaining fully transparent and interpretable. The FilterDCA code is available at http://gitlab.lcqb.upmc.fr/muscat/FilterDCA.Author summaryThe de novo prediction of tertiary and quaternary protein structures has recently seen important advances, by combining unsupervised, purely sequence-based coevolutionary analyses with structure-based supervision using deep learning for contact-map prediction. While showing impressive performance, deep-learning methods require large training sets and pose severe obstacles for their interpretability. Here we construct a simple, transparent and therefore fully interpretable inter-domain contact predictor, which uses the results of coevolutionary Direct Coupling Analysis in combination with explicitly constructed filters reflecting typical contact patterns in a training set of known protein structures, and which improves the accuracy of predicted contacts significantly. Our approach thereby sheds light on the question how contact information is encoded in coevolutionary signals.

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