Evolutionary Algorithm in the Optimization of a Coarse-Grained Force Field

Leonarski, Filip; Trovato, Fabio; Tozzini, Valentina; Leś, Andrzej; Trylska, Joanna

doi:10.1021/ct4005036

Cited by 29 publications

(28 citation statements)

References 83 publications

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“…[29][30][31][32][33] GAs have been successfully used in force field development, including fitting of dihedral angle 34,35 and van der Waals 17,25 parameters, atomic polarizabilities, 16 parametrization of coarse-grained 36 and reactive 37,38 force fields, and applied in numerous ad hoc force field parameter optimizations. [39][40][41][42][43] Interestingly, although the assignment of the fixed point charges is a critical part of many force fields, the application of GAs and other evolutionary/stochastic optimization techniques to the MEP pointcharge fitting has not been explored, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

2015

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Evolutionary methods, such as genetic algorithms (GAs), provide powerful tools for optimization of the force field parameters, especially in the case of simultaneous fitting of the force field terms against extensive reference data. However, GA fitting of the nonbonded interaction parameters that includes point charges has not been explored in the literature, likely due to numerous difficulties with even a simpler problem of the least-squares fitting of the atomic point charges against a reference molecular electrostatic potential (MEP), which often demonstrates an unusually high variation of the fitted charges on buried atoms. Here, we examine the performance of the GA approach for the least-squares MEP point charge fitting, and show that the GA optimizations suffer from a magnified version of the classical buried atom effect, producing highly scattered yet correlated solutions. This effect can be understood in terms of the linearly independent, natural coordinates of the MEP fitting problem defined by the eigenvectors of the least-squares sum Hessian matrix, which are also equivalent to the eigenvectors of the covariance matrix evaluated for the scattered GA solutions. GAs quickly converge with respect to the high-curvature coordinates defined by the eigenvectors related to the leading terms of the multipole expansion, but have difficulty converging with respect to the low-curvature coordinates that mostly depend on the buried atom charges. The performance of the evolutionary techniques dramatically improves when the point charge optimization is performed using the Hessian or covariance matrix eigenvectors, an approach with a significant potential for the evolutionary optimization of the fixed-charge biomolecular force fields.

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Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

2015

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“…Nonisotropic potentials have been used to describe the interactions between crystallins mediated by patches of amino acids (Staneva and Frenkel 2015;Bucciarelli et al 2016). We remark that the success of a force field depends on the choice of complex enough functional forms and the method used for its parameterization (Trovato and Tozzini 2008, 2012Trovato et al 2013;Leonarski et al 2013).…”

Section: System Representation Force Field Parameterization and Dynmentioning

confidence: 99%

“…They have also advanced computational methods to parameterize automatically realistic models, such as the evolutionary algorithms (Trovato and Tozzini 2012;Leonarski et al 2013;Seffens 2017) or to access equilibrium and dynamical properties of both the solute and the solvent (Ayton et al 2007;McGuffee and Elcock 2010;Mereghetti and Wade 2012;Dama et al 2013;Elcock 2013;Zhou 2013, 2016;Trovato et al 2013;Ciccotti and Ferrario 2013;Trovato and Tozzini 2014;Ozer et al 2015;Wang and Brady 2016).…”

Section: Conclusion and Future Challengesmentioning

confidence: 99%

“…As highlighted in this review, the input quantities of the latter methods can be experimentally measured or calculated from detailed particle-based simulations. The second challenge will likely trigger the development of accurate force fields (Leonarski et al 2013;Ivani et al 2016;Song et al 2017;Wang et al 2017a) that are transferable to different cell conditions (Rebelo et al 2013;Parry et al 2014;Joyner et al 2016;Munder et al 2016;Sun and Fang 2016), as well as diffusion-reaction schemes (Schöneberg et al 2014;Michalski and Loew 2016;Epstein and Xu 2016) and agent-based simulations (Azimi et al 2011).…”

Section: Conclusion and Future Challengesmentioning

confidence: 99%

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Molecular simulations of cellular processes

Trovato

Fumagalli

2017

Biophys Rev

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It is, nowadays, possible to simulate biological processes in conditions that mimic the different cellular compartments. Several groups have performed these calculations using molecular models that vary in performance and accuracy. In many cases, the atomistic degrees of freedom have been eliminated, sacrificing both structural complexity and chemical specificity to be able to explore slow processes. In this review, we will discuss the insights gained from computer simulations on macromolecule diffusion, nuclear body formation, and processes involving the genetic material inside cell-mimicking spaces. We will also discuss the challenges to generate new models suitable for the simulations of biological processes on a cell scale and for cellcycle-long times, including non-equilibrium events such as the co-translational folding, misfolding, and aggregation of proteins. A prominent role will be played by the wise choice of the structural simplifications and, simultaneously, of a relatively complex energetic description. These challenging tasks will rely on the integration of experimental and computational methods, achieved through the application of efficient algorithms.

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“…[77][78][79] The bead may represent only a few atoms or a large group of atoms depending on the resolution of the model; see Fig. 1.…”

Section: Coarse-grained Modelmentioning

confidence: 99%

RNA structure prediction: Progress and perspective

Shi¹,

Wu²,

Wang³

et al. 2014

Chinese Phys. B

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Many recent exciting discoveries have revealed the versatility of RNAs and their impo rtance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three dimensional (3D) structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.

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Evolutionary Algorithm in the Optimization of a Coarse-Grained Force Field

Cited by 29 publications

References 83 publications

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

Molecular simulations of cellular processes

RNA structure prediction: Progress and perspective

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