Relative entropy as a universal metric for multiscale errors

Chaimovich, Aviel; Shell, M. Scott

doi:10.1103/physreve.81.060104

Cited by 86 publications

(123 citation statements)

References 14 publications

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“…The disadvantage of IMC on the other hand is a high computational cost and problems with numerical stability; for a detailed comparison see Reference [116]. Related to IMC, there are several other recent developments, e.g., a molecular renormalization group approach [85][86][87] or an approach that relies on relative entropies [96][97][98] (which will be discussed in more detail below). While the above structure-based methods by construction reproduce exactly, within the error of the numerical procedure, the local pair structures and thus are well-suited to the reinsertion of atomistic coordinates, it can be expected a priori that they will not be equally well suited to the reproduction of thermodynamic properties (pressure, phase behavior, etc.)…”

Section: Boltzmann-inversion Based Methodsmentioning

confidence: 99%

“…In another group of approaches, one numerically generates CG interaction functions with the aim of reproducing the configurational phase space sampled in an atomistic reference simulation. These approaches may rely on different types of reference properties such as structure functions [77][78][79][80][81][82][83][84][85][86][87][88][89], mean forces [90][91][92][93][94][95] or relative entropies [96][97][98]. In the following subsection, a few basic notions of coarse-graining theory will be introduced, together with examples of the strategies that can be employed to perform the coarse-graining in practice.…”

Section: Coarse-grainingmentioning

confidence: 99%

“…Recently a different coarse-graining strategy has been developed, namely the Relative Entropy method [131][132][133], which relies on a quantitative measure of the loss of information that follows from the description of a system in terms of different interaction potentials and/or different resolution. Remarkably, it is possible to demonstrate that the information function employed in this strategy connects Relative Entropy, IBI and Force Matching together.…”

Section: Relative Entropymentioning

confidence: 99%

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Computer Simulations of Soft Matter: Linking the Scales

Potestio

Peter

Kremer

2014

Entropy

102

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Abstract:In the last few decades, computer simulations have become a fundamental tool in the field of soft matter science, allowing researchers to investigate the properties of a large variety of systems. Nonetheless, even the most powerful computational resources presently available are, in general, sufficient to simulate complex biomolecules only for a few nanoseconds. This limitation is often circumvented by using coarse-grained models, in which only a subset of the system's degrees of freedom is retained; for an effective and insightful use of these simplified models; however, an appropriate parametrization of the interactions is of fundamental importance. Additionally, in many cases the removal of fine-grained details in a specific, small region of the system would destroy relevant features; such cases can be treated using dual-resolution simulation methods, where a subregion of the system is described with high resolution, and a coarse-grained representation is employed in the rest of the simulation domain. In this review we discuss the basic notions of coarse-graining theory, presenting the most common methodologies employed to build low-resolution descriptions of a system and putting particular emphasis on their similarities and differences. The AdResS and H-AdResS adaptive resolution simulation schemes are reported as examples of dual-resolution approaches, especially focusing in particular on their theoretical background.

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Section: Boltzmann-inversion Based Methodsmentioning

confidence: 99%

Section: Coarse-grainingmentioning

confidence: 99%

Section: Relative Entropymentioning

confidence: 99%

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Computer Simulations of Soft Matter: Linking the Scales

Potestio

Peter

Kremer

2014

Entropy

102

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“…46 Our design of CAMELOT is guided by the successes of previous methods that include the force-matching algorithm of Voth and coworkers, [47][48][49][50][51][52][53][54][55][56][57][58] the Yvon-Born-Green formalism of Noid and coworkers, [59][60][61] and the relative entropy method of Shell and coworkers. [62][63][64] The remainder of the text is organized as follows: Section II describes the CAMELOT algorithm. Section III describes the numerical methods we use for all atom and coarsegrained simulations.…”

Section: Introductionmentioning

confidence: 99%

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

Ruff

Harmon

Pappu

2015

The Journal of Chemical Physics

102

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We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarsegrained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences. C 2015 AIP Publishing LLC. [http://dx

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“…[5][6][7] However, forcefields with an even coarser representation of molecular components have arisen from the desire to model larger systems than those that are manageable with an allatom representation. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Similarly, implicit solvent models aim to reduce computational demand by replacing the explicitly sampled solvent degrees of freedom with an approximate, continuum description of the bulk solvent potential of mean force (PMF). Implicit solvent models are commonly used at all levels of molecular granularity, from QM (Refs.…”

Section: Introductionmentioning

confidence: 99%

A smoothly decoupled particle interface: New methods for coupling explicit and implicit solvent

Wagoner

Pande

2011

The Journal of Chemical Physics

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A common theme of studies using molecular simulation is a necessary compromise between computational efficiency and resolution of the forcefield that is used. Significant efforts have been directed at combining multiple levels of granularity within a single simulation in order to maintain the efficiency of coarse-grained models, while using finer resolution in regions where such details are expected to play an important role. A specific example of this paradigm is the development of hybrid solvent models, which explicitly sample the solvent degrees of freedom within a specified domain while utilizing a continuum description elsewhere. Unfortunately, these models are complicated by the presence of structural artifacts at or near the explicit/implicit boundary. The presence of these artifacts significantly complicates the use of such models, both undermining the accuracy obtained and necessitating the parameterization of effective potentials to counteract the artificial interactions. In this work, we introduce a novel hybrid solvent model that employs a smoothly decoupled particle interface (SDPI), a switching region that gradually transitions from fully interacting particles to a continuum solvent. The resulting SDPI model allows for the use of an implicit solvent model based on a simple theory that needs to only reproduce the behavior of bulk solvent rather than the more complex features of local interactions. In this study, the SDPI model is tested on spherical hybrid domains using a coarse-grained representation of water that includes only Lennard-Jones interactions. The results demonstrate that this model is capable of reproducing solvent configurations absent of boundary artifacts, as if they were taken from full explicit simulations.

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Relative entropy as a universal metric for multiscale errors

Cited by 86 publications

References 14 publications

Computer Simulations of Soft Matter: Linking the Scales

Computer Simulations of Soft Matter: Linking the Scales

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

A smoothly decoupled particle interface: New methods for coupling explicit and implicit solvent

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