Validating lipid force fields against experimental data: Progress, challenges and perspectives

Poger, David; Caron, Bertrand; Mark, Alan E.

doi:10.1016/j.bbamem.2016.01.029

Cited by 81 publications

(113 citation statements)

References 170 publications

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“…It is promising to see that over the past few years much progress has been made on that front. There are now force field parameters for a wider range of lipids including sterols, ceramides, sphingomyelin, and cardiolipins enabling simulations of more complex membranes . In particular the presence of cholesterol, which is known to increase the rigidity of membranes, will likely affect the membrane‐binding properties of peptides.…”

Section: Discussionmentioning

confidence: 99%

“…The majority of simulation use membranes composed of one or more type of phospholipid. The specific lipid composition is mostly guided by the biological system of interest, the aim to match conditions of “wet‐lab” experiments and the availability of validated force field parameters . Until very recently, parameters were only available for a small set of lipids and simulations of peptide‐membrane systems were limited to model membranes consisting mainly of phospholipids.…”

Section: Computational Approaches To Study Venom Peptide—membrane Intmentioning

confidence: 99%

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Molecular simulations of venom peptide‐membrane interactions: Progress and challenges

Deplazes

2018

Peptide Science

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Because of their wide range of biological activities venom peptides are a valuable source of lead molecules for the development of pharmaceuticals, pharmacological tools and insecticides. Many venom peptides work by modulating the activity of ion channels and receptors or by irreversibly damaging cell membranes. In many cases, the mechanism of action is intrinsically linked to the ability of the peptide to bind to or partition into membranes. Thus, understanding the biological activity of these venom peptides requires characterizing their membrane binding properties. This review presents an overview of the recent developments and challenges in using biomolecular simulations to study venom peptide‐membrane interactions. The review is focused on (i) gating modifier peptides that target voltage‐gated ion channels, (ii) venom peptides that inhibit mechanosensitive ion channels, and (iii) pore‐forming venom peptides. The methods and approaches used to study venom peptide‐membrane interactions are discussed with a particular focus on the challenges specific to these systems and the type of questions that can (and cannot) be addressed using state‐of‐the‐art simulation techniques. The review concludes with an outlook on future aims and directions in the field.

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Section: Discussionmentioning

confidence: 99%

Section: Computational Approaches To Study Venom Peptide—membrane Intmentioning

confidence: 99%

Molecular simulations of venom peptide‐membrane interactions: Progress and challenges

Deplazes

2018

Peptide Science

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“…[7,104] ForM Ds imulations,t he convergence of time series of various single-molecule or system properties should be considered. [107]. When applying Hamiltonian or temperature replica-exchange simulation, it should be checked whether all replicas actually exchange and sufficiently often to ensure Boltzmann sampling.…”

Section: Validation Based On Popularity?mentioning

confidence: 99%

Validation of Molecular Simulation: An Overview of Issues

et al. 2017

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Computer simulation of molecular systems enables structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic, or supra-molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Q of experimentally observable quantities Q. One must consider 1) the accuracy of Q , 2) the accuracy of the function Q(r ) used to calculate a Q-value based on a molecular configuration r of N particles, 3) the sensitivity of the function Q(r ) to the configuration r , 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed.

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“…The values are also close to the majority of the values obtained experimentally (0.57-0.717 nm 2 ). 130 To test that the use of a twinrange approach in which the short-and long-range interactions were calculated separately did not affect the outcome, the simulations were repeated with r1 = 0.8 nm and r2 = 1.4 nm but with the pairlist updated every step and long-range forces applied instantaneously. The same value of AL (within statistical uncertainty) was obtained using versions 4.0.7, 4.6.3, 4.6.7 and 5.1.2 (Table 4.1, rows 3-6).…”

Section: Discussionmentioning

confidence: 99%

“…It is often used as an indicator of the level of fluidity in a lipid bilayer. 130 To establish a baseline against which the alternate time-saving integration schemes could be referenced, the system was simulated using a single cutoff r1 = r2 = 1.4 nm with the pairlist updated every step. As can be seen from rows 1-2 of Table 4.1, the same value of AL was obtained (within statistical uncertainty) using versions 4.0.7 and 4.5.5 (0.624 and 0.624 nm 2 , respectively).…”

Section: Discussionmentioning

confidence: 99%

Improving the accuracy of molecular dynamics simulations: parameterisation of interaction potentials for small molecules

Stroet¹

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The ability to accurately describe molecular systems with classical molecular dynamics (MD) is critically dependent on an understanding of the sources of error in the design, evaluation and analysis of the underlying model. One significant source of error is in the degree to which the equations and parameters used to represent atomic interaction can reproduce the experimental properties relevant to a particular application. While highly optimised and well-validated interaction parameters are available for common biomolecules (amino acids, sugars, lipids etc.), this is not the case for heterogeneous small molecules such as co-factors, substrates and drugs. The enormity of the chemical space covered by small molecules necessitates the development of automated approaches to parameterising and validating interaction parameters. Several tools are available that can be used to generate interaction parameters for small molecules compatible with a particular existing (bio)molecular force field. Despite their popularity, many are poorly validated, while some have been shown to be inappropriate for many applications. In other cases, validation studies have demonstrated reasonable performance, however, they also suggest significant improvements can be made. One such tool is the Automated Topology Builder (ATB, http://atb.uq.edu.au/) which provides interaction parameters for small molecules compatible with the GROMOS biomolecular force field.As part of this work, a fully automated protocol for the calculation of solvation energy by thermodynamic integration has been developed and applied to the large-scale validation of the ATB against experimental solvation data in water and hexane. It is shown that the largest errors are due to the Lennard-Jones parameters used for functional groups not present in common biomolecules. Other sources of error included the atomic charges assigned by the ATB for the united atom carbons and incompatibilities between GROMOS Lennard-Jones parameters and the ATB charge model. Significant sources of error were also identified in the evaluation of MD models, both for the calculation of solvation energy as well as for MD simulations of lipid membranes. In particular, the use of multiple-time-step algorithms as a time-saving technique. For the calculation of solvation energy by TI, the twin-range cutoff scheme-commonly used in simulations with the GROMOS force field-was found to introduce a systematic error of about 1 kJ/mol. While for lipid systems, various III changes made to the integration algorithm of the GROMACS simulation code were found to significantly alter the properties of lipid membranes when simulated with identical force fields.Finally, a method for parameterising transferrable interaction potentials with respect to a wide range of target properties is presented. The method is based on an analysis of surfaces corresponding to the difference between calculated and target data as a function of alternative combinations of parameters.The consideration of surfaces in parameter space...

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Validating lipid force fields against experimental data: Progress, challenges and perspectives

Cited by 81 publications

References 170 publications

Molecular simulations of venom peptide‐membrane interactions: Progress and challenges

Molecular simulations of venom peptide‐membrane interactions: Progress and challenges

Validation of Molecular Simulation: An Overview of Issues

Improving the accuracy of molecular dynamics simulations: parameterisation of interaction potentials for small molecules

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