2014
DOI: 10.1002/prot.24570
|View full text |Cite
|
Sign up to set email alerts
|

Comparative analysis of sequence covariation methods to mine evolutionary hubs: Examples from selected GPCR families

Abstract: Covariation between positions in a multiple sequence alignment may reflect structural, functional, and/or phylogenetic constraints and can be analyzed by a wide variety of methods. We explored several of these methods for their ability to identify covarying positions related to the divergence of a protein family at different hierarchical levels. Specifically, we compared seven methods on a model system composed of three nested sets of G-protein-coupled receptors (GPCRs) in which a divergence event occurred. Th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
34
0

Year Published

2014
2014
2020
2020

Publication Types

Select...
7

Relationship

1
6

Authors

Journals

citations
Cited by 13 publications
(35 citation statements)
references
References 67 publications
(151 reference statements)
1
34
0
Order By: Relevance
“…MI can reveal residue pairs and networks with functional importance, such as protein-protein interaction interfaces and catalytic sites [49-53]. For example, a structure-based correlated mutation analysis (SCMA) approach, using MI to score coevolving residue pairs, identified the retinal binding site residues in the G-protein coupled receptor rhodopsin [54].…”
Section: Identification Of Functional Residuesmentioning
confidence: 99%
“…MI can reveal residue pairs and networks with functional importance, such as protein-protein interaction interfaces and catalytic sites [49-53]. For example, a structure-based correlated mutation analysis (SCMA) approach, using MI to score coevolving residue pairs, identified the retinal binding site residues in the G-protein coupled receptor rhodopsin [54].…”
Section: Identification Of Functional Residuesmentioning
confidence: 99%
“…From aligned sequences, covariation between pairs of S ‐gene aa was calculated using McLauchlan‐Based Substitution Correlation (McBASC) method from the “Bios2cor” package in R (v3.4.1, R Foundation for Statistical Computing, Vienna, Austria). This method was chosen over other covariation estimation methods due to the suspected low connectivity of aa pairs and lack of a central residue . Highly covarying aa pairs were identified as having a McBASC ≥|1| and were further used to examine networks of mutations using Cytoscape v3.6.1 .…”
Section: Methodsmentioning
confidence: 99%
“…If an aa at a given position was different than the consensus sequence, a mutation was considered present at this position. Antiviral resistance mutations on the pol-gene were defined in function of associated agent: LAM (rtV173L, rtL180M, rtM204V/I); adefovir (rtA181T/V, rtN236T); en- 21 Highly covarying aa pairs were identified as having a McBASC ≥|1| and were further used to examine networks of mutations using Cytoscape v3.6.1. 22 Clusters of highly correlated mutation networks were derived using the clusterMaker2 app v1.2.1 available in Cytoscape.…”
Section: Detection Of Hbv Mutationsmentioning
confidence: 99%
“…Recently, covarying residues that are related to the diversification of the chemokine receptor family have been identified by analyzing a multiple sequence alignment of the human chemokine receptors [56]. The residue at 2.49, Ala or Ser, is a central residue working as a "hub" in the network of covarying residues.…”
Section: And Cys647 Micro-switchesmentioning
confidence: 99%
“…The residue at 2.49 thus differentiates the chemokine receptors into 2 groups. The residue covaries with 9 residues with high connectivity, 6 of them residing in TM-III [56], which plays a key role in the structural rearrangements of the helix bundle during the receptor activation [57]. Although Cys6.47 and Trp6.48 are not included in the 9 residues, both residues are among the network containing the 9 covarying residues.…”
Section: And Cys647 Micro-switchesmentioning
confidence: 99%