Exploring the Energy Landscape of Small Peptides and Proteins by Molecular Dynamics Simulations

Stock, Gerhard; Jain, Abhinav; Riccardi, Laura; Nguyen, Phuong H.

doi:10.1002/9781118183373.ch2

Cited by 15 publications

(19 citation statements)

References 65 publications

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“…While a suitable representation of the energy landscape should (at least) reproduce the correct number, energy, and location of the metastable states and barriers, these basic quantities often get lost when the energy landscape is projected on a low-dimensional subspace. 21,22 As a solution of this problem, we have recently suggested to use a combination of systematic dimensionality reduction methods [23][24][25][26][27][28][29] (that identify in a controlled manner adequate system degrees of freedom) and a multidimensional dLE 16,30,31 (that accounts for dimensionality of the collective coordinate). The resulting dLE model is able to quantitatively reproduce dynamical observables (such as time correlation functions and first passage times) that can be directly compared to the original MD data.…”

Section: Introductionmentioning

confidence: 99%

Global Langevin model of multidimensional biomolecular dynamics

Schaudinnus

Lickert

Biswas

et al. 2016

The Journal of Chemical Physics

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Molecular dynamics simulations of biomolecular processes are often discussed in terms of diffusive motion on a low-dimensional free energy landscape F(𝒙). To provide a theoretical basis for this interpretation, one may invoke the system-bath ansatz á la Zwanzig. That is, by assuming a time scale separation between the slow motion along the system coordinate x and the fast fluctuations of the bath, a memory-free Langevin equation can be derived that describes the system's motion on the free energy landscape F(𝒙), which is damped by a friction field and driven by a stochastic force that is related to the friction via the fluctuation-dissipation theorem. While the theoretical formulation of Zwanzig typically assumes a highly idealized form of the bath Hamiltonian and the system-bath coupling, one would like to extend the approach to realistic data-based biomolecular systems. Here a practical method is proposed to construct an analytically defined global model of structural dynamics. Given a molecular dynamics simulation and adequate collective coordinates, the approach employs an "empirical valence bond"-type model which is suitable to represent multidimensional free energy landscapes as well as an approximate description of the friction field. Adopting alanine dipeptide and a three-dimensional model of heptaalanine as simple examples, the resulting Langevin model is shown to reproduce the results of the underlying all-atom simulations. Because the Langevin equation can also be shown to satisfy the underlying assumptions of the theory (such as a delta-correlated Gaussian-distributed noise), the global model provides a correct, albeit empirical, realization of Zwanzig's formulation. As an application, the model can be used to investigate the dependence of the system on parameter changes and to predict the effect of site-selective mutations on the dynamics.

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Section: Introductionmentioning

confidence: 99%

Global Langevin model of multidimensional biomolecular dynamics

Schaudinnus

Lickert

Biswas

et al. 2016

The Journal of Chemical Physics

Self Cite

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“…Notwithstanding this, the application of PCA to biological structural data, also known as essential dynamics analysis (EDA), has been used to identify collective, large‐scale modes of protein motion. The insights gained from these analyses have been coherent with data from other analytical techniques, and have thus contributed to the elucidation of the functional mechanics of the studied proteins …”

Section: Introductionmentioning

confidence: 63%

“…The insights gained from these analyses have been coherent with data from other analytical techniques, and have thus contributed to the elucidation of the functional mechanics of the studied proteins. [21][22][23][24][25][26][27] Classically, in PCA of protein structural data, the variances in the Cartesian coordinates of a macromolecular system are analyzed (hereafter referred to as cPCA). 21-23 Other bases of data, such as the fluctuations of the dihedral angles of the protein backbone may also be transformed with PCA.…”

Section: Theoretical Basismentioning

confidence: 99%

“…Nevertheless, through the representation of each dihedral angle as a sine/cosine pair, dihedral angle principal component analysis (dPCA) as introduced by Stock et al has demonstrated that it can capture the potential energy surfaces visited in peptide folding simulations with intriguing detail. [24][25][26][27] The challenges inherent to the use of dihedral angles in PCA have also been tackled in an alternative approach, employing principal component geodesics, named GeoPCA. 28 Finally, while PCA is commonly applied to a trajectory of consecutive structural snapshots of a protein or proteinligand complex from molecular dynamics (MD) simulations, we here apply PCA to an ensemble of structures derived from X-ray crystallography, which serves as a pseudo-trajectory.…”

Section: Theoretical Basismentioning

confidence: 99%

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Structural review of PPARγ in complex with ligands: Cartesian- and dihedral angle principal component analyses of X-ray crystallographic data

et al. 2017

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Two decades of research into the ligand-dependent modulation of the activity of the peroxisome proliferator-activated receptor γ (PPARγ) have demonstrated the heterogeneous modes of action of PPARγ ligands, in terms of their interaction surfaces in the ligand-binding pocket, binding stoichiometry and ability to interact with functionally important parts of the receptor, through both direct and allosteric mechanisms. These findings signal the complex mechanistic bases of the distinct biological effects of different classes of PPARγ ligands. Today, the development of PPARγ ligands focuses on partial- and non-agonists as opposed to classical agonists, due to the severe side effects observed with PPARγ classical agonists as therapeutic agents. To aid this development, we performed principal component analyses of the atomic (Cartesian) coordinates (cPCA) and dihedral angles (dPCA) of the structures of human PPARγ from X-ray crystallography, available in the public domain, seeking to reveal ligand-induced trends. In the cPCA, projections of the structures along the principal components (PCs) demonstrated a moderate correlation between cPC1 and structural parameters related to the stabilization of helix 12, which is central to the transcriptional activation by PPARγ classical agonists. Consequently, the presented cPCA mapping of the PPARγ-ligand complexes may guide in silico drug discovery programs seeking to avoid stabilization of helix 12 in their development of partial- and non-agonistic PPARγ ligands. Notably, while the dPCA could identify key regions of dihedral fluctuation in the structural ensemble, the distributions along dPC1 - 2 could not be classified according to the same parameters as the distribution along cPC1. Proteins 2017; 85:1684-1698. © 2017 Wiley Periodicals, Inc.

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“…C3-293 and C3-363 have one and two conformational regions, respectively. At the same time, we compare their metastable conformational states (Stock, Jain, & Riccardi, 2012) in the conformational regions with the crystal structure. A few differences are found in the regions such as residues 1-10 of C3-293, residues 1-18 of C3-363, but most of residues and overall structure have no obvious differences.…”

Section: Conformational Changes Under Different Temperaturesmentioning

confidence: 99%

Insight into the impact of environments on structure of chimera C3 of human β-defensins 2 and 3 from molecular dynamics simulations

Zhao

et al. 2014

Journal of Biomolecular Structure and Dynamics

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C3 is a chimera from human β-defensins 2 and 3 and possesses higher antimicrobial activity compared with its parental molecules, so it is an attractive candidate for clinical application of antimicrobial peptides. In continuation with the previous studies, molecular dynamics (MD) simulations were carried out for further investigating the effect of ambient environments (temperature and bacterial membrane) on C3 dynamics. Our results reveal that C3 has higher flexibility, larger intensity of motion, and more relevant secondary structural changes at 363 K to adapt the high temperature and maintain its antimicrobial activity, comparison with it at 293 K; when C3 molecule associates with the bacterial membrane, it slightly fluctuates and undergoes local conformational changes; in summary, C3 molecule demonstrates stable conformations under these environments. Furthermore, MD results analysis show that the hydrophobic contacts, the hydrogen bonds, and disulfide bonds in the peptide are responsible for maintaining its stable conformation. In addition, our simulation shows that C3 peptides can make anionic lipids clustered in the bacterial membrane; it means that positive charges and pronounced regional cationic charge density of C3 are most key factors for its antimicrobial activity.

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Exploring the Energy Landscape of Small Peptides and Proteins by Molecular Dynamics Simulations

Cited by 15 publications

References 65 publications

Global Langevin model of multidimensional biomolecular dynamics

Global Langevin model of multidimensional biomolecular dynamics

Structural review of PPARγ in complex with ligands: Cartesian- and dihedral angle principal component analyses of X-ray crystallographic data

Insight into the impact of environments on structure of chimera C3 of human β-defensins 2 and 3 from molecular dynamics simulations

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