A Statistical Journey through the Topological Determinants of the β2 Adrenergic Receptor Dynamics

Paola, Luisa Di; Poudel, Humanath; Parise, Mauro; Giuliani, Alessandro; Leitner, David M.

doi:10.3390/e24070998

Cited by 7 publications

(3 citation statements)

References 60 publications

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“…We have also examined energy transport in the inactive and two active states of GLP-1R, by computing energy exchange networks (EENs). Earlier calculations of EENs have shown how changes in the EENs with ligand binding reveal structural changes involved in activation, including a study of the β 2 adrenergic receptor, a rhodopsin-like GPCR. , Similarly, for GLP-1R, significant changes in the EENs are found among residues belonging to motifs, where structural changes important for activation occur. We have also examined how changes in the EEN upon activation reveal dynamic differences in the inactive and active states.…”

Section: Discussionmentioning

confidence: 99%

Energy Transport in Class B GPCRs: Role of Protein–Water Dynamics and Activation

Poudel

Leitner

2022

J. Phys. Chem. B

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We compute energy exchange networks (EENs) through glucagon-like peptide-1 receptor (GLP-1R), a class B Gprotein-coupled receptor (GPCR), in inactive and two active states, one activated by a peptide ligand and the other by a small molecule agonist, from results of molecular dynamics simulations. The reorganized network upon activation contains contributions from structural as well as from dynamic changes and corresponding entropic contributions to the free energy of activation, which are estimated in terms of the change in rates of energy transfer across non-covalent contacts. The role of water in the EENs and in activation of GLP-1R is also investigated. The dynamics of water in contact with the central polar network of the transmembrane region is found to be significantly slower for both activated states compared to the inactive state. This result is consistent with the contribution of water molecules to activation of GLP-1R previously suggested and resembles water dynamics in parts of the transmembrane region found in earlier studies of rhodopsin-like GPCRs.

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Section: Discussionmentioning

confidence: 99%

Energy Transport in Class B GPCRs: Role of Protein–Water Dynamics and Activation

Poudel

Leitner

2022

J. Phys. Chem. B

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“…In this regard, many of the best-known metrics for graph analyses, i.e. clustering techniques and centrality measures ( Doncheva et al 2011 , Brysbaert and Lensink 2021 ), found valid application in structural biology, with proven relevance in protein allosteric, dynamic, and interaction analyses ( Liu and Nussinov 2016 , Das et al 2021 , Dubanevics and McLeish 2021 , Di Paola et al 2022 , Guzzi et al 2023 ).…”

Section: Introductionmentioning

confidence: 99%

PyPCN: protein contact networks in PyMOL

Rosignoli,

di Paola,

Paiardini

2023

Bioinformatics

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Motivation Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations. Results PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs. Availability and implementation https://github.com/pcnproject/PyPCN.

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“…The authors found that BC was significantly more effective in detecting functionally important residues than degree or CLs. Several other works concluded that BC was a good metric to study allosteric signaling ( 16 , 29 – 31 ). A study of allostery in imidazole glycerol phosphate synthase showed that eigenvector centrality (EG) can detect regions responsible for allosteric communication ( 22 ).…”

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confidence: 99%

Calculation of centralities in protein kinase A

Kornev

Aoto

Taylor

2022

Proc. Natl. Acad. Sci. U.S.A.

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Topological analysis of protein residue networks (PRNs) is a common method that can help to understand the roles of individual residues. Here, we used protein kinase A as a study object and asked what already known functionally important residues can be detected by network analysis. Along several traditional approaches to weight edges in PRNs we used local spatial pattern (LSP) alignment that assigns high weights to edges only if CαCβ vectors for the corresponding residues retain their mutual positions and orientation. Our results show that even short molecular dynamic simulations of 10 to 20 ns can give convergent values for betweenness and degree centralities calculated from the LSP-based PRNs. Using these centralities, we were able to clearly distinguish a group of residues that are highly conserved in protein kinases and play important functional and regulatory roles. In comparison, traditional methods based on cross-correlation and linear mutual information were much less efficient for this particular task. These results call for reevaluation of the current methods to generate PRNs.

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A Statistical Journey through the Topological Determinants of the β2 Adrenergic Receptor Dynamics

Cited by 7 publications

References 60 publications

Energy Transport in Class B GPCRs: Role of Protein–Water Dynamics and Activation

Energy Transport in Class B GPCRs: Role of Protein–Water Dynamics and Activation

PyPCN: protein contact networks in PyMOL

Calculation of centralities in protein kinase A

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