Molecular dynamics simulation of the proline conformational equilibrium and dynamics in antamanide using the CHARMM force field

Schmidt, J.; Brueschweiler, Rafael; Ernst, R. R.; Dunbrack, Roland L.; Joseph, Diane; Karplus, Martin

doi:10.1021/ja00072a030

Cited by 57 publications

(40 citation statements)

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“…The graphs are visualized using Chimera. 38 As an important and special feature of proline, the pyrrolidine ring puckering has been studied in detail, 4,[8][9][10][11][12][13][14][15][16][17] and proline's two preferred puckering states have been characterized very well. Usually, the pyrrolidine ring puckering is symbolized with two parameters-the pseudorotation phase angle and amplitude-that can be calculated either by the five ring-atom positions 18 or by the five endocyclic torsion angles χ j .…”

Section: Introductionmentioning

confidence: 99%

“…Karplus and co-workers have utilized the Charmm force field to study proline isomerization 21 and the proline puckering of a cyclic decapeptide. 10 McCammon, Hamelberg, and co-workers have utilized the Amber force field combined with the accelerated molecular dynamics simulations to study proline isomerization and the enzymatic prolyl isomerization process. 22 Doshi and Hamelberg have also modified the Amber force field parameters to study proline isomerization so that the simulation results compared well with the experimental values.…”

Section: Introductionmentioning

confidence: 99%

“…We choose Charmm22 force field because the proline puckering motion had been studied using it and the results were compared well with the experimental values. 10 In this study, we provide a full comparison of the proline structural data at the different states under different conditions (trans and cis conformations, in the gas phase and in water) calculated by the simulation method with those calculated by the different QM studies. Because the pyrrolidine ring puckering depends on both φ and ψ, these comparisons provide a clear view of the relations among φ, ψ and χ j at the different states.…”

Section: Introductionmentioning

confidence: 99%

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The puckering free-energy surface of proline

2013

AIP Advances

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Proline has two preferred puckering states, which are often characterized by the pseudorotation phase angle and amplitude. Although proline's five endocyclic torsion angles can be utilized to calculate the phase angle and amplitude, it is not clear if there is any direct correlation between each torsion angle and the proline-puckering pathway. Here we have designed five proline puckering pathways utilizing each torsion angle χj (j = 1∼5) as the reaction coordinate. By examining the free-energy surfaces of the five puckering pathways, we find they can be categorized into two groups. The χ2 pathway (χ2 is about the Cβ—Cγ bond) is especially meaningful in describing proline puckering: it changes linearly with the puckering amplitude and symmetrically with the phase angle. Our results show that this conclusion applies to both trans and cis proline conformations. We have also analyzed the correlations of proline puckering and its backbone torsion angles ϕ and ψ. We show proline has preferred puckering states at the specific regions of ϕ, ψ angles. Interestingly, the shapes of ψ-χ2 free-energy surfaces are similar among the trans proline in water, cis proline in water and cis proline in the gas phase, but they differ substantially from that of the trans proline in the gas phase. Our calculations are conducted using molecular simulations; we also verify our results using the proline conformations selected from the Protein Data Bank. In addition, we have compared our results with those calculated by the quantum mechanical methods

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The puckering free-energy surface of proline

2013

AIP Advances

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“…We have also examined proline ring puckering based on the observed NOE. Two puckered ring conformations, C-~-endo/C-y exo (UP) and C-~-exo/C-y-endo (DOWN), are usually observed (48,51,53). In ROESY spectra strong B-y and a medium a.-y cross peaks were observed.…”

Section: Discussionmentioning

confidence: 99%

NMR Structures of a Nonapeptide from DNA Binding Domain of Human Polymerase-α Determined by Iterative Complete-Relaxation-Matrix Approach

Bose¹,

Li²,

Yang³

et al. 1999

Journal of Biomolecular Structure and Dynamics

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Nuclear magnetic resonance structures of a nonapeptide, ERFKCPCPT, selected from the DNA binding domain of human polymerase-alpha, were determined by complete relaxation matrix analysis of transverse NOE data. The structures exhibit a type III turn with residues KCPC, and the remaining residues exhibit non-ordered structures. The turn was confirmed by alpha, N (i,i+3) connectivity, a low temperature coefficient of NH chemical shift (-3.1 x 10(-3)) of the fourth residue, 3J(NHalpha) coupling constants, and characteristic CD peaks at 228 and 200 nm. Furthermore, phi and psi dihedral angles for the i + 1, and i + 2 residues of the turn are found to be -80 and -41 and -60 and -40 degrees. The first proline residue is trans- while the second exists in both cis- and trans- configurations, with trans- being more than 80% populated. The trans-configuration was established from C5alpha-P6alpha correlation and phi and psi angles of the proline. The five-membered proline ring is in DOWN puckered (C-beta-exo/C-gamma-endo) conformation. The structure of the peptide reveals that the two cysteine thiols are approximately 5 A(o) apart and appropriately positioned to covalently bind cis-diamminedichloroplatinum(II), a widely used anti-cancer drug.

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“…The four Phe and four Pro residues show significant variability in their chemical shifts due to structural and dynamic differences. [14][15][16][17][18] The residue that overlaps most severely is F9: its a, b, and b' proton signals fully or partially overlap with those of F6, and its H N proton signal overlaps with that of F10. Nonetheless, the DemixC protocol succeeds in finding a representative trace of this residue.…”

Section: Angewandte Chemiementioning

confidence: 99%

Robust Deconvolution of Complex Mixtures by Covariance TOCSY Spectroscopy

Zhang

Brüschweiler

2007

Angew Chem Int Ed

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A fundamental problem in many areas of chemistry is the identification of components in chemical mixtures, such as different solutes in a solution. The recent advent of metabolomics has generated a critical demand for powerful analysis methods for fluid mixtures in the food and life sciences. [1][2][3] While important progress is being made in potentially laborious and costly hyphenated methods, [4] spectroscopic methods have the power to circumvent or reduce the need for hyphenation prior to analysis. [5] Most compounds contain multiple NMR-active spins that are J-coupled and allow the identification of spin-spin coupling networks for discrimination between components, as well as their subsequent identification by screening against a database. Particularly useful in this regard is the 2D NMR 1 H-1 H TOCSY experiment, [6] which monitors multiple relay transfers of spin magnetization within a spin system to provide a wealth of information on scalar spin-spin coupling connectivity with high sensitivity. Because experimental efficiency is a prerequisite for high-throughput applications, TOCSY is combined here with covariance NMR, [7][8][9] which produces high-resolution spectra largely independent of the number of increments along the indirect time domain t 1 .A recently proposed unsupervised deconvolution method [10] uses principal-component analysis (PCA) of the covariance TOCSY spectrum of a mixture. In the absence of significant spectral overlap, the dominant PCA eigenmodes approximate well the 1D spectra of the individual components. For increasing degrees of spectral overlap between components, however, "mixed modes" emerge whose assignment to known compounds can pose a significant challenge. [10] The method presented here, which is termed DemixC (C stands for clustering), overcomes this limitation by identifying for each component characteristic traces that are essentially free of overlap and can be identified and assigned with high confidence.The DemixC method is demonstrated for three samples of differing complexity. Sample I consists of three amino acids (Glu, Lys, Val) dissolved in D 2 O. Sample II contains four amino acids (Glu, Leu, Lys, Val) in D 2 O. The amino acid concentration of samples I and II is 7 mm. Sample III contains the cyclic decapeptide antamanide [11] [-Val-Pro-Pro-Ala-PhePhe-Pro-Pro-Phe-Phe-] dissolved in deuterated chloroform at a concentration of 1 mm. While the dissolved peptide of sample III is not an actual mixture, in terms of its proton NMR properties it behaves like a mixture of 10 amino acids at 1 mm concentration each. The low variability of the amino acid composition (four phenylalanine and four proline residues) leads to significant resonance overlap [12] providing a rigorous test case for the performance of the proposed method.Two-dimensional TOCSY experiments for samples I and II were performed at 600 MHz with mixing times t m of 97 and 62 ms with 2048 complex points in t 2 and 1024 points in t 1 in TPPI mode. The TOCSY spectra of sample III were recorded at 800 MHz with m...

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Molecular dynamics simulation of the proline conformational equilibrium and dynamics in antamanide using the CHARMM force field

Cited by 57 publications

References 0 publications

The puckering free-energy surface of proline

The puckering free-energy surface of proline

NMR Structures of a Nonapeptide from DNA Binding Domain of Human Polymerase-α Determined by Iterative Complete-Relaxation-Matrix Approach

Robust Deconvolution of Complex Mixtures by Covariance TOCSY Spectroscopy

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