ID-correspondence: a measure for detecting evolutionary coupling

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Manishankar Mondal ◽  
Banani Roy ◽  
Chanchal K. Roy ◽  
Kevin A. Schneider
Keyword(s):  
Evolutionary Coupling

scholarly journals Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning

2015 ◽  
Vol 31 (21) ◽  
pp. 3506-3513 ◽  
Author(s):  
Jianzhu Ma ◽  
Sheng Wang ◽  
Zhiyong Wang ◽  
Jinbo Xu
Keyword(s):  
Supervised Learning ◽  
Coupling Analysis ◽  
Contact Prediction ◽  
Evolutionary Coupling

scholarly journals Evolutionary coupling saturation mutagenesis: Coevolution‐guided identification of distant sites influencing Bacillus naganoensis pullulanase activity

FEBS Letters ◽  
2019 ◽  
Vol 594 (5) ◽  
pp. 799-812 ◽  
Author(s):  
Xinye Wang ◽  
Xiaoran Jing ◽  
Yi Deng ◽  
Yao Nie ◽  
Fei Xu ◽  
...  
Keyword(s):  
Saturation Mutagenesis ◽  
Evolutionary Coupling

scholarly journals Evolutionary coupling measurement: Making sense of the current chaos

2017 ◽  
Vol 135 ◽  
pp. 4-19 ◽  
Author(s):  
Serkan Kirbas ◽  
Tracy Hall ◽  
Alper Sen
Keyword(s):  
Making Sense ◽  
Evolutionary Coupling

Experimental Evidence for an Eco-Evolutionary Coupling between Local Adaptation and Intraspecific Competition

2016 ◽  
Vol 187 (4) ◽  
pp. 447-456 ◽  
Author(s):  
Adam M. Siepielski ◽  
Alex Nemirov ◽  
Matthew Cattivera ◽  
Avery Nickerson
Keyword(s):  
Experimental Evidence ◽  
Local Adaptation ◽  
Intraspecific Competition ◽  
Evolutionary Coupling

scholarly journals AlignmentViewer: Sequence Analysis of Large Protein Families

2018 ◽  
Author(s):  
Roc Reguant ◽  
Yevgeniy Antipin ◽  
Rob Sheridan ◽  
Augustin Luna ◽  
Chris Sander
Keyword(s):  
Open Source Software ◽  
Source Code ◽  
Web Browsers ◽  
Protein Families ◽  
Large Protein ◽  
Multiple Sequence ◽  
Internet Connection ◽  
Visualization Analysis ◽  
Link Type ◽  
Evolutionary Coupling

AbstractSummaryAlignmentViewer is multiple sequence alignment viewer for protein families with flexible visualization, analysis tools and links to protein family databases. It is directly accessible in web browsers without the need for software installation, as it is implemented in JavaScript, and does not require an internet connection to function. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, facilitating the computation of protein 3D structures and the detection of functionally constrained interactions.Availability and ImplementationAlignmentViewer is open source software under the MIT license. The viewer is at http://alignmentviewer.org and the source code, documentation and issue tracking, for co-development, are at https://github.com/dfci/[email protected], reaches all authors

scholarly journals Evolutionary couplings detect side-chain interactions

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7280 ◽  
Author(s):  
Adam J. Hockenberry ◽  
Claus O. Wilke
Keyword(s):  
De Novo ◽  
Protein Structures ◽  
Side Chain ◽  
Sequence Alignments ◽  
Practical Applications ◽  
Residue Contacts ◽  
Coupling Algorithms ◽  
Backbone Atoms ◽  
Evolutionary Coupling ◽  
Binding Interfaces

Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue–residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10–15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue–residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.

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