An Alternative to Conventional λ-Intermediate States in Alchemical Free Energy Calculations: λ-Enveloping Distribution Sampling

König, Gerhard; Glaser, Nina; Schroeder, Benjamin; Kubincová, Alžbeta; Hünenberger, Philippe H.; Riniker, Sereina

doi:10.1021/acs.jcim.0c00520

Cited by 23 publications

(50 citation statements)

References 125 publications

(354 reference statements)

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“…The term,

, at the denominators makes the potential convergent as

, for

. This is useful especially for advanced steps of the decoupling (

) as it improves the sampling at distances close to

from the atoms of the solute [ 32 , 45 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

“…The term, aλ n , at the denominators makes the potential convergent as r → 0, for λ < 1. This is useful especially for advanced steps of the decoupling (0.7 < λ < 1) as it improves the sampling at distances close to σ ij from the atoms of the solute [32,45,46]. To configure the bilayer graphenes we considered two copies of a single layer graphene.…”

Section: Methodsmentioning

confidence: 99%

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On the Consistency of the Exfoliation Free Energy of Graphenes by Molecular Simulations

Gotzias

Tocci

Sapalidis

2021

IJMS

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Monolayer graphene is now produced at significant yields, by liquid phase exfoliation of graphites in solvents. This has increased the interest in molecular simulation studies to give new insights in the field. We use decoupling simulations to compute the exfoliation free energy of graphenes in a liquid environment. Starting from a bilayer graphene configuration, we decouple the Van der Waals interactions of a graphene monolayer in the presence of saline water. Then, we introduce the monolayer back into water by coupling its interactions with water molecules and ions. A different approach to compute the graphene exfoliation free energy is to use umbrella sampling. We apply umbrella sampling after pulling the graphene monolayer on the shear direction up to a distance from a bilayer. We show that the decoupling and umbrella methods give highly consistent free energy results for three bilayer graphene samples with different size. This strongly suggests that the systems in both methods remain closely in equilibrium as we move between the states before and after the exfoliation. Therefore, the amount of nonequilibrium work needed to peel the two layers apart is minimized efficiently.

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“…The term,

, at the denominators makes the potential convergent as

, for

. This is useful especially for advanced steps of the decoupling (

) as it improves the sampling at distances close to

from the atoms of the solute [ 32 , 45 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

On the Consistency of the Exfoliation Free Energy of Graphenes by Molecular Simulations

Gotzias

Tocci

Sapalidis

2021

IJMS

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“…In cases where no reliable experimental structure with ligand bound is available, the generalized replica exchange with solute tempering (gREST) + FEP ( Oshima et al, 2020 ) approach where protein-ligand interactions are weakened through simulation at high temperature to force refinement of ligand binding orientation or Alchemical Grid Dock ( Minh, 2020 ) method can be performed to obtain high quality binding poses. Alternative lambda schedules aimed at reducing the number of intermediate windows to simulate without sacrificing low variance are introduced by Konig et al ( Konig et al, 2020 ) with enveloping distribution sampling and addition of a restraint energy distribution function in the screening of SARS-CoV-2 protease inhibitors ( Li et al, 2020 ). Entropic bottlenecks caused by order/disorder transitions that inhibit convergence can be avoided by biasing the simulation with the integrated logistic function away from the transition regions ( Pal and Gallicchio, 2019 ).…”

Section: Free Energy Calculation Approachesmentioning

confidence: 99%

Recent Developments in Free Energy Calculations for Drug Discovery

King

Aitchison

et al. 2021

Front. Mol. Biosci.

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The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

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“…16,[18][19][20][21] In addition, the effects of the choice of intermediate states and exact coupling of the transformation to the Hamiltonian of the system through a coupling parameter λ have been explored. [22][23][24][25][26] Related to this, the transformation pathway depends on the definition of alchemical changes, which in turn might strongly affect the performance of the calculations. In particular, the dual topology approach replaces all atoms of one compound with the atoms of the other.…”

Section: Introductionmentioning

confidence: 99%

Perturbation Free-Energy Toolkit: Automated Alchemical Topology Builder and Optimized Simulation Update Scheme

Petrov

2021

Preprint

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Free-energy calculations play an important role in the application of computational chemistry to a range of fields, including protein biochemistry, rational drug design or material science. Importantly, the free energy difference is directly related to experimentally measurable quantities such as partition and adsorption coefficients, water activity and binding affinities. Among several techniques aimed at predicting the free-energy differences, perturbation approaches, involving alchemical transformation of one molecule into another through intermediate states, stand out as rigorous methods based on statistical mechanics. However, despite the importance of efficient and accurate free energy predictions, applicability of the perturbation approaches is still largely impeded by a number of challenges. This study aims at addressing two of them: 1) the definition of the perturbation path, i.e., alchemical changes leading to the transformation of one molecule to the other, and 2) determining the amount of sampling along the path to reach desired convergence. In particular, an automatic perturbation builder based on a graph matching algorithm is developed, that is able to identify the maximum common substructure of two molecules and provide the perturbation topologies suitable for free-energy calculations using GROMOS and GROMACS simulation packages. Moreover, it was used to calculate the changes in free energy of a set of post-translational modifications and analyze their convergence behavior. Different methods were tested, which showed that MBAR and extended thermodynamic integration (TI) in combination with MBAR show better performance as compared to BAR, extended TI with linear interpolation and plain TI. Also, a number of error estimators were explored and how they relate to the true error, estimated as the difference in free energy from an extensive set of simulation data. This analysis shows that most of the estimators provide only a qualitative agreement to the true error, with little quantitative predictive power. This notwithstanding, the preformed analyses provided insight into the convergence of free-energy calculations, which allowed for development of an iterative update scheme for perturbation simulations that aims at minimizing the simulation time to reach the convergence, i.e., optimizing the efficiency. Importantly, this toolkit is made available online as an open-source python package (https://github.com/drazen-petrov/SMArt).

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An Alternative to Conventional λ-Intermediate States in Alchemical Free Energy Calculations: λ-Enveloping Distribution Sampling

Cited by 23 publications

References 125 publications

On the Consistency of the Exfoliation Free Energy of Graphenes by Molecular Simulations

On the Consistency of the Exfoliation Free Energy of Graphenes by Molecular Simulations

Recent Developments in Free Energy Calculations for Drug Discovery

Perturbation Free-Energy Toolkit: Automated Alchemical Topology Builder and Optimized Simulation Update Scheme

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