Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications

Jing, Zhifeng; Liu, Chengwen; Cheng, Sara Y.; Qi, Rui; Walker, Brandon; Piquemal, Jean‐Philip; Ren, Pengyu

doi:10.1146/annurev-biophys-070317-033349

Cited by 350 publications

(373 citation statements)

References 153 publications

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“…We expect continued improvement from other methods, particularly multiple efforts to improve classical force fields, [99][100][101][102] inclusion of polarizable atomic charges, [103][104][105][106][107][108][109][110] novel force fields from experimental data, density functional and other quantum methods, [111][112][113][114][115][116] and continued development of approximate semiempirical quantum methods. 37 At present, we can highly recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies.…”

Section: Dipole Moment Rangesmentioning

confidence: 99%

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Folmsbee¹,

Hutchison²

2020

Preprint

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We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

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Section: Dipole Moment Rangesmentioning

confidence: 99%

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Folmsbee¹,

Hutchison²

2020

Preprint

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“…Among the major limitations is the accuracy of force fields used to calculate interatomic interactions and the tractable sampling of the ensemble discussed above. The quality of traditionally applied force fields is intrinsically limited by numerous approximations like the lack of particular interaction types [32], neglect of electronic polarizability [33], and fixed protonation states of titrable residues [34]. At the When we consider the reliable treatment of protein dynamics as an essential component of a successful protein design, it is natural to resort to the molecular dynamics (MD) simulation technique as a golden standard to investigate the conformational behavior of a protein.…”

Section: Introductionmentioning

confidence: 99%

“…Among the major limitations is the accuracy of force fields used to calculate interatomic interactions and the tractable sampling of the ensemble discussed above. The quality of traditionally applied force fields is intrinsically limited by numerous approximations like the lack of particular interaction types [32], neglect of electronic polarizability [33], and fixed protonation states of titrable residues [34]. At the expense of increased computational demands, some of those limitations can be partially overcome by improving potential models [35], resorting to polarizable force fields [36], and constant pH simulations [37].…”

Section: Introductionmentioning

confidence: 99%

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Surpeta

Sequeiros-Borja

Brezovský

2020

IJMS

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Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

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“…Classical water force fields (FFs) play central roles in computer molecular modeling as many chemical and biophysical processes happen in aqueous solutions . Depending on whether or not the polarization effect is explicitly considered, most water FFs can be divided into two classes.…”

Section: Introductionmentioning

confidence: 99%

“…As the polarization effect is highly environmental dependent, it is highly realized that the fixed‐charge models fitting only to bulk water properties are not well suited for solutions especially for highly charged biological molecules . The AMOEBA is one of the popular polarizable FFs, where many‐body effects are explicitly taken into account via a linear polarization supposition that can closely reproduce the high‐level quantum potential energy surface.…”

Section: Introductionmentioning

confidence: 99%

Three‐site and five‐site fixed‐charge water models compatible with AMOEBA force field

et al. 2020

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In a typical biomolecular simulation using Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field, the vast majority molecules in the simulation box consist of water, and these water molecules consume the most CPU power due to the explicit mutual induction effect. To improve the computational efficiency, we here develop two new nonpolarizable water models (with flexible bonds and fixed charges) that are compatible with AMOEBA solute: the 3‐site AW3C and 5‐site AW5C. To derive the force‐field parameters for AW3C and AW5C, we fit to six experimental liquid thermodynamic properties: liquid density, enthalpy of vaporization, dielectric constant, isobaric heat capacity, isothermal compressibility and thermal expansion coefficient, at a broad range of temperatures from 261.15 to 353.15 K under 1.0 atm pressure. We further validate our AW3C and AW5C water models by showing that they can well reproduce the radial distribution function g(r), self‐diffusion constant D, and hydration free energy from the AMOEBA03 water model and the experimental observations. Furthermore, we show that our AW3C and AW5C water models can greatly accelerate (>5 times) the bulk water as well as biomolecular simulations when compared to AMOEBA water. Specifically, we demonstrate that the applications of AW3C and AW5C water models to simulate a DNA duplex lead to a threefold acceleration, and in the meanwhile well maintain the structural properties as the fully polarizable AMOEBA water. We expect that our AW3C and AW5C water models hold great promise to be widely applied to simulate complex bio‐molecules using the AMOEBA force field.

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Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications

Cited by 350 publications

References 153 publications

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Three‐site and five‐site fixed‐charge water models compatible with AMOEBA force field

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