Literature DB >> 27585313

Can Simulations and Modeling Decipher NMR Data for Conformational Equilibria? Arginine-Vasopressin.

Elke Haensele1, Noureldin Saleh1, Christopher M Read, Lee Banting, David C Whitley, Timothy Clark1.   

Abstract

Arginine vasopressin (AVP) has been suggested by molecular-dynamics (MD) simulations to exist as a mixture of conformations in solution. The (1)H and (13)C NMR chemical shifts of AVP in solution have been calculated for this conformational ensemble of ring conformations (identified from a 23 μs molecular-dynamics simulation). The relative free energies of these conformations were calculated using classical metadynamics simulations in explicit water. Chemical shifts for representative conformations were calculated using density-functional theory. Comparison with experiment and analysis of the results suggests that the (1)H chemical shifts are most useful for assigning equilibrium concentrations of the conformations in this case. (13)C chemical shifts distinguish less clearly between conformations, and the distances calculated from the nuclear Overhauser effect do not allow the conformations to be assigned clearly. The (1)H chemical shifts can be reproduced with a standard error of less than 0.24 ppm (<2.2 ppm for (13)C). The combined experimental and theoretical results suggest that AVP exists in an equilibrium of approximately 70% saddlelike and 30% clinched open conformations. Both newly introduced statistical metrics designed to judge the significance of the results and Smith and Goodman's DP4 probabilities are presented.

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Year:  2016        PMID: 27585313     DOI: 10.1021/acs.jcim.6b00344

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  3 in total

1.  Chiral separation of diastereomers of the cyclic nonapeptides vasopressin and desmopressin by uniform field ion mobility mass spectrometry.

Authors:  Shawn T Phillips; James N Dodds; Berkley M Ellis; Jody C May; John A McLean
Journal:  Chem Commun (Camb)       Date:  2018-08-21       Impact factor: 6.222

Review 2.  Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations.

Authors:  Jovan Damjanovic; Jiayuan Miao; He Huang; Yu-Shan Lin
Journal:  Chem Rev       Date:  2021-01-11       Impact factor: 60.622

3.  Structure prediction of cyclic peptides by molecular dynamics + machine learning.

Authors:  Jiayuan Miao; Marc L Descoteaux; Yu-Shan Lin
Journal:  Chem Sci       Date:  2021-11-05       Impact factor: 9.969

  3 in total

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