Method for free‐energy calculations using iterative techniques

Kumar, Shankar; Payne, Philip W.; Vásquez, Maximiliano

doi:10.1002/(sici)1096-987x(19960730)17:10<1269::aid-jcc7>3.0.co;2-m

Cited by 113 publications

(92 citation statements)

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“…The common analysis is based on projecting MD (MC) trajectories onto a small number of coordinates using principal component analysis or calculating the populations along one or two physically significant reaction coordinates. 23,24 Differences, ∆F mn , are commonly calculated by thermodynamic integration (TI) over physical quantities such as the energy, temperature, and the specific heat 25,26 as well as nonphysical parameters [13][14][15][16][17][18][19][27][28][29][30][31][32][33][34] (free energy perturbation methods and umbrella and histogram analysis methods [35][36][37] are also included in this category, see ref 19 and references cited therein). This is a robust and highly versatile approach, which is used successfully for calculating the difference in the free energy of binding of two ligands to the active site of an enzyme.…”

Section: I1 the Role Of Free Energy In Structural Biologymentioning

confidence: 99%

Stability of the Free and Bound Microstates of a Mobile Loop of α-Amylase Obtained from the Absolute Entropy and Free Energy

Cheluvaraja

Meirovitch

2007

J. Chem. Theory Comput.

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Abstract:The hypothetical scanning molecular dynamics (HSMD) method is a relatively new technique for calculating the absolute entropy, S, and free energy, F, from a given sample generated by any simulation procedure. Thus, each sample conformation, i, is reconstructed by calculating transition probabilities that their product leads to the probability of i, hence to the entropy. HSMD is an exact method where all interactions are considered, and the only approximation is due to insufficient sampling. In previous studies HSMD (and HS Monte Carlo -HSMC) has been applied very successfully to liquid argon, TIP3P water, self-avoiding walks, and peptides in a R-helix, extended, and hairpin microstates. In this paper HSMD is developed further as applied to the flexible 7-residue surface loop, 304-310 (Gly-His-Gly-Ala-Gly-GlySer) of the enzyme porcine pancreatic R-amylase. We are mainly interested in entropy and free energy differences ∆S ) S free -S bound (and ∆F)F free -F bound ) between the free and bound microstates of the loop, which are obtained from two separate MD samples of these microstates without the need to carry out thermodynamic integration. As for peptides, we find that relatively large systematic errors in S free and S bound (and F free and F bound ) are cancelled in ∆S (∆F) which is thus obtained efficiently with high accuracy, i.e., with a statistical error of 0.1-0.2 kcal/mol (T)300 K) using the AMBER force field and AMBER with the implicit solvation GB/SA. We provide theoretical arguments in support of this cancellation, discuss in detail the problems involved in the computational definition of a microstate in conformational space, suggest potential ways for enhancing efficiency further, and describe the next development where explicit water will replace implicit solvation.

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Section: I1 the Role Of Free Energy In Structural Biologymentioning

confidence: 99%

Stability of the Free and Bound Microstates of a Mobile Loop of α-Amylase Obtained from the Absolute Entropy and Free Energy

Cheluvaraja

Meirovitch

2007

J. Chem. Theory Comput.

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“…This can significantly increase the rate of achieving convergence. Kumar et al devised a weighted histogram analysis method (WHAM) [25][26][27][28][29] that allowed optimal weighting of previous simulation data by recasting the Ferrenberg-Swendsen multiple histogram equations [30]. The histograms for differently biased simulations were used and combined into an unbiased distribution by applying the correct weights calculated from the WHAM equations.…”

Section: Ferrenberg-swendsen Multiple Histogram Equationsmentioning

confidence: 99%

“…At each step in the iterative process several biased simulations are run and on completing each set of biased simulations all the biased population distributions are combined with the current and previous iterations using computed weighting factors to yield the best unbiased probability distribution, p k . This procedure is commonly known as the Weighted Histogram Analysis Method (WHAM) [28,29,32] where…”

Section: Ferrenberg-swendsen Multiple Histogram Equationsmentioning

confidence: 99%

FEARCF a multidimensional free energy method for investigating conformational landscapes and chemical reaction mechanisms

Naidoo

2011

Sci. China Chem.

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The development and implementation of a computational method able to produce free energies in multiple dimensions, descriptively named the free energies from adaptive reaction coordinate forces (FEARCF) method is described in this paper. While the method can be used to calculate free energies of association, conformation and reactivity here it is shown in the context of chemical reaction landscapes. A reaction free energy surface for the Claisen rearrangement of chorismate to prephenate is used as an illustration of the method's efficient convergence. FEARCF simulations are shown to achieve flat histograms for complex multidimensional free energy volumes. The sampling efficiency by which it produces multidimensional free energies is demonstrated on the complex puckering of a pyranose ring, that is described by a three dimensional W( 1 ,  2 ,  3 ) potential of mean force. free energy calculations, reaction dynamics, reaction surfaces, multidimensional free energy surfaces

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“…The main problem with these algorithms, however, is the non-trivial determination of the different multicanonical weight factors by an iterative process involving short trial simulations. For complex systems this procedure can be very tedious and attempts have been made to accelerate convergence of the iterative process (Berg and Celik 1992;Kumar et al 1996;Smith and Bruce 1996;Hansmann 1997;Bartels and Karplus 1998).…”

Section: Replica Exchangementioning

confidence: 99%

Protein Dynamics: From Structure to Function

Kubitzki

Groot

Seeliger

2009

From Protein Structure to Function With Bioinformatics

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Understanding protein function requires detailed knowledge about protein dynamics, i.e. the different conformational states the system can adopt. Despite substantial experimental progress, simulation techniques such as molecular dynamics (MD) currently provide the only routine means to obtain dynamical information at an atomic level on timescales of nano-to microseconds. Even with the current development of computational power, sampling techniques beyond MD are necessary to enhance conformational sampling of large proteins and assemblies thereof. The use of collective coordinates has proven to be a promising means in this respect, either as a tool for analysis or as part of new sampling algorithms. Starting from MD simulations, several enhanced sampling algorithms for biomolecular simulations are reviewed in this chapter. Examples are given throughout illustrating how consideration of the dynamic properties of a protein sheds light on its function. Molecular Dynamics SimulationsOver the last decades, experimental techniques have made substantial progress in revealing the three-dimensional structure of proteins, in particular X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy. Going beyond the static picture of single protein structures has proven to be more challenging, although, a number of techniques such as NMR relaxation, fluorescence spectroscopy or time-resolved X-ray crystallography have emerged

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Method for free‐energy calculations using iterative techniques

Cited by 113 publications

References 0 publications

Stability of the Free and Bound Microstates of a Mobile Loop of α-Amylase Obtained from the Absolute Entropy and Free Energy

Stability of the Free and Bound Microstates of a Mobile Loop of α-Amylase Obtained from the Absolute Entropy and Free Energy

FEARCF a multidimensional free energy method for investigating conformational landscapes and chemical reaction mechanisms

Protein Dynamics: From Structure to Function

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