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Probing the Allosteric Mechanism in Pyrrolysyl-tRNA Synthetase Using Energy-Weighted Network Formalism

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    Probing the Allosteric Mechanism in Pyrrolysyl-tRNA Synthetase Using Energy-Weighted Network Formalism
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    Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India
    Phone: +91-80-22932611. Fax: +91-80-23600535. E-mail: [email protected]
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    Biochemistry

    Cite this: Biochemistry 2011, 50, 28, 6225–6236
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    https://doi.org/10.1021/bi200306u
    Published June 8, 2011
    Copyright © 2011 American Chemical Society
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    Abstract

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    Pyrrolysyl-tRNA synthetase (PylRS) is an atypical enzyme responsible for charging tRNAPyl with pyrrolysine, despite lacking precise tRNA anticodon recognition. This dimeric protein exhibits allosteric regulation of function, like any other tRNA synthetases. In this study we examine the paths of allosteric communication at the atomic level, through energy-weighted networks of Desulfitobacterium hafniense PylRS (DhPylRS) and its complexes with tRNAPyl and activated pyrrolysine. We performed molecular dynamics simulations of the structures of these complexes to obtain an ensemble conformation–population perspective. Weighted graph parameters relevant to identifying key players and ties in the context of social networks such as edge/node betweenness, closeness index, and the concept of funneling are explored in identifying key residues and interactions leading to shortest paths of communication in the structure networks of DhPylRS. Further, the changes in the status of important residues and connections and the costs of communication due to ligand induced perturbations are evaluated. The optimal, suboptimal, and preexisting paths are also investigated. Many of these parameters have exhibited an enhanced asymmetry between the two subunits of the dimeric protein, especially in the pretransfer complex, leading us to conclude that encoding of function goes beyond the sequence/structure of proteins. The local and global perturbations mediated by appropriate ligands and their influence on the equilibrium ensemble of conformations also have a significant role to play in the functioning of proteins. Taking a comprehensive view of these observations, we propose that the origin of many functional aspects (allostery and half-sites reactivity in the case of DhPylRS) lies in subtle rearrangements of interactions and dynamics at a global level.

    Copyright © 2011 American Chemical Society

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    Supporting figures (Figures S1–S18), supporting tables (Tables S1–S5), and Supplemental Methods. This material is available free of charge via the Internet at http://pubs.acs.org.

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    This article is cited by 46 publications.

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    10. Lilian Hernández Alvarez, Diego Enry Barreto Gomes, Jorge Enrique Hernández González, Pedro Geraldo Pascutti, . Dissecting a novel allosteric mechanism of cruzain: A computer-aided approach. PLOS ONE 2019, 14 (1) , e0211227. https://doi.org/10.1371/journal.pone.0211227
    11. Dmitrii Shcherbinin, Alexander Veselovsky. Analysis of Protein Structures Using Residue Interaction Networks. 2019, 55-69. https://doi.org/10.1007/978-3-030-05282-9_3
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    14. Onur Serçinoğlu, Pemra Ozbek. gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations. Nucleic Acids Research 2018, 46 (W1) , W554-W562. https://doi.org/10.1093/nar/gky381
    15. Gennady M. Verkhivker. Computational Modeling of the Hsp90 Interactions with Cochaperones and Small-Molecule Inhibitors. 2018, 253-273. https://doi.org/10.1007/978-1-4939-7477-1_19
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    28. Alexandr P. Kornev, Susan S. Taylor. Dynamics-Driven Allostery in Protein Kinases. Trends in Biochemical Sciences 2015, 40 (11) , 628-647. https://doi.org/10.1016/j.tibs.2015.09.002
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    33. Gennady M. Verkhivker. Computational Studies of Allosteric Regulation in the Hsp90 Molecular Chaperone: From Functional Dynamics and Protein Structure Networks to Allosteric Communications and Targeted Anti-Cancer Modulators. Israel Journal of Chemistry 2014, 54 (8-9) , 1052-1064. https://doi.org/10.1002/ijch.201300143
    34. Kristin Blacklock, Gennady M. Verkhivker, . Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications. PLoS Computational Biology 2014, 10 (6) , e1003679. https://doi.org/10.1371/journal.pcbi.1003679
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    36. Kristin Blacklock, Gennady M. Verkhivker, . Allosteric Regulation of the Hsp90 Dynamics and Stability by Client Recruiter Cochaperones: Protein Structure Network Modeling. PLoS ONE 2014, 9 (1) , e86547. https://doi.org/10.1371/journal.pone.0086547
    37. Soma Ghosh, Saraswathi Vishveshwara. Ranking the quality of protein structure models using sidechain based network properties. F1000Research 2014, 3 , 17. https://doi.org/10.12688/f1000research.3-17.v1
    38. Moitrayee Bhattacharyya, Chanda R. Bhat, Saraswathi Vishveshwara. An automated approach to network features of protein structure ensembles. Protein Science 2013, 22 (10) , 1399-1416. https://doi.org/10.1002/pro.2333
    39. Simona Mariani, Daniele Dell'Orco, Angelo Felline, Francesco Raimondi, Francesca Fanelli, . Network and Atomistic Simulations Unveil the Structural Determinants of Mutations Linked to Retinal Diseases. PLoS Computational Biology 2013, 9 (8) , e1003207. https://doi.org/10.1371/journal.pcbi.1003207
    40. Kristin Blacklock, Gennady M. Verkhivker, . Differential Modulation of Functional Dynamics and Allosteric Interactions in the Hsp90-Cochaperone Complexes with p23 and Aha1: A Computational Study. PLoS ONE 2013, 8 (8) , e71936. https://doi.org/10.1371/journal.pone.0071936
    41. Richard Giegé, Mathias Springer, . Aminoacyl-tRNA Synthetases in the Bacterial World. EcoSal Plus 2012, 5 (1) https://doi.org/10.1128/ecosalplus.4.2.1
    42. John Eargle, Zaida Luthey-Schulten. NetworkView : 3D display and analysis of protein·RNA interaction networks. Bioinformatics 2012, 28 (22) , 3000-3001. https://doi.org/10.1093/bioinformatics/bts546
    43. A. R. Atilgan, C. Atilgan. Local motifs in proteins combine to generate global functional moves. Briefings in Functional Genomics 2012, 11 (6) , 479-488. https://doi.org/10.1093/bfgp/els027
    44. M. S. Vijayabaskar, Saraswathi Vishveshwara, . Insights into the Fold Organization of TIM Barrel from Interaction Energy Based Structure Networks. PLoS Computational Biology 2012, 8 (5) , e1002505. https://doi.org/10.1371/journal.pcbi.1002505
    45. Micol De Ruvo, Alessandro Giuliani, Paola Paci, Daniele Santoni, Luisa Di Paola. Shedding light on protein–ligand binding by graph theory: The topological nature of allostery. Biophysical Chemistry 2012, 165-166 , 21-29. https://doi.org/10.1016/j.bpc.2012.03.001
    46. Tyler J. Glembo, Daniel W. Farrell, Z. Nevin Gerek, M. F. Thorpe, S. Banu Ozkan, . Collective Dynamics Differentiates Functional Divergence in Protein Evolution. PLoS Computational Biology 2012, 8 (3) , e1002428. https://doi.org/10.1371/journal.pcbi.1002428
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    Biochemistry

    Cite this: Biochemistry 2011, 50, 28, 6225–6236
    Click to copy citationCitation copied!
    https://doi.org/10.1021/bi200306u
    Published June 8, 2011
    Copyright © 2011 American Chemical Society
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