Biasing Potential Replica Exchange Multisite λ-Dynamics for Efficient Free Energy Calculations

Armacost, Kira A.; Goh, Garrett B.; Brooks, Charles L.

doi:10.1021/ct500894k

Cited by 33 publications

(90 citation statements)

References 55 publications

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“…However, how would we transform between all the different amino acids in a single simulation? A few researchers have designed thermodynamic paths connecting multiple molecules through multidimensional paths [51,52], shared volume methods [49,50], and iteratively updating the thermodynamic path [91,103,104]. The researchers implementing these methods have been able to generate free energy differences for 2 to 10 3 structurally similar molecules, with more theoretically possible [50].…”

Section: Current Limitations In Simulation-based Estimates Of the Frementioning

confidence: 99%

“…We propose moves through λ-dynamics [51,52,202] where both Cartesian coordinates, and chemical coordinates are updated at the same time. Each λ variable is assigned a fictitious mass and is subject to a Hamiltonian of…”

Section: Chemical Sampling With λ-Dynamicsmentioning

confidence: 99%

“…λ-dynamics has known simulation instability and error in free energy estimates based on the choice of the fictitious alchemical mass [51,52]. HMC allows us to overcome both of these issues and the alchemical mass now becomes a HMC acceptance rate tuning parameter.…”

Section: Chemical Sampling With λ-Dynamicsmentioning

confidence: 99%

“…Computational methods can only help reduce the number of molecules that need to be synthesized, and will never replace the need for a laboratory. Free energy differences are a particularly helpful property to compute as they provide This work specifically combines our basis function method with λ-dynamics [51,52] and Hybrid MC [193,194] to preserve the correct thermodynamic ensemble, run stable simulations, and explore a multidimensional chemical space. In this implementation,…”

Section: Introductionmentioning

confidence: 99%

“…They unfortunately require substantial reference data and are very prone to over fitting as small changes in molecular structure can result in large changes to thermodynamic properties, even for approaches which couple data mining with experimentation to fill in the gaps [48]. Within drug design, the methods of simultaneously estimating properties in chemical spaces are limited to either compounds which roughly share the same volume [49,50], or can only scale to a few molecular structure changes before computational time becomes a limiting factor [51,52]. There is substantial room for improvement in developing new methods for estimating properties across large chemical spaces.…”

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confidence: 99%

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Predicting Thermodynamic Properties over Combinatorially Large Chemical Spaces

Naden¹

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Current computational property prediction methods are limited in the number of molecules they can test at once. To predict properties for thousands or millions of molecules at once, new techniques must be developed with efficient computational scaling in the number of molecules simultaneously tested. In this dissertation, I develop a general approach to carry out computational alchemical free energy calculations using a variance minimized linear basis function approach. This approach provides a means to collect data for statistical free energy estimates that scales efficiently with the number of thermodynamic states or tested molecules. I achieve efficient speed-up over relying on simulation force code to compute energies required for free energy estimation.

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Section: Current Limitations In Simulation-based Estimates Of the Frementioning

confidence: 99%

Section: Chemical Sampling With λ-Dynamicsmentioning

confidence: 99%

Section: Chemical Sampling With λ-Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Predicting Thermodynamic Properties over Combinatorially Large Chemical Spaces

Naden¹

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Orthogonal sampling in free-energy calculations of residue mutations in a tripeptide: TI versusλ-LEUS

Bieler¹,

Hünenberger²

2015

J. Comput. Chem.

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In a recent article (Bieler et al., J. Chem. Theory Comput. 2014, 10, 3006), we introduced a combination of λ-dynamics and local-elevation umbrella-sampling termed λ-LEUS to calculate free-energy changes associated with alchemical processes using molecular dynamics simulations. This method was suggested to be more efficient than thermodynamic integration (TI), because the dynamical variation of the alchemical variable λ opens up pathways to circumvent barriers in the orthogonal space (defined by the N - 1 degrees of freedom that are not subjected to the sampling enhancement), a feature λ-LEUS shares with Hamiltonian replica-exchange (HR) approaches. However, the mutation considered, hydroquinone to benzene in water, was no real challenge in terms of orthogonal-space properties, which were restricted to solvent-relaxation processes. In the present article, we revisit the comparison between TI and λ-LEUS considering non-trivial mutations of the central residue X of a KXK tripeptide in water (with X = G, E, K, S, F, or Y). Side-chain interactions that may include salt bridges, hydrogen bonds or steric clashes lead to slow relaxation in the orthogonal space, mainly in the two-dimensional subspace spanned by the central φ and ψ dihedral angles of the peptide. The efficiency enhancement afforded by λ-LEUS is confirmed in this more complex test system and can be attributed explicitly to the improved sampling of the orthogonal space. The sensitivity of the results to the nontrivial choices of a mass parameter and of a thermostat coupling time for the alchemical variable is also investigated, resulting in recommended ranges of 50 to 100 u nm(2) and 0.2 to 0.5 ps, respectively.

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CHARMM‐GUI 10 years for biomolecular modeling and simulation

et al. 2016

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CHARMM-GUI, http://www.charmm-gui.org, is a web-based graphical user interface that prepares complex biomolecular systems for molecular simulations. CHARMM-GUI creates input files for a number of programs including CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Since its original development in 2006, CHARMM-GUI has been widely adopted for various purposes and now contains a number of different modules designed to set up a broad range of simulations: (1) PDB Reader & Manipulator, Glycan Reader, and Ligand Reader & Modeler for reading and modifying molecules; (2) Quick MD Simulator, Membrane Builder, Nanodisc Builder, HMMM Builder, Monolayer Builder, Micelle Builder, and Hex Phase Builder for building all-atom simulation systems in various environments; (3) PACE CG Builder and Martini Maker for building coarse-grained simulation systems; (4) DEER Facilitator and MDFF/xMDFF Utilizer for experimentally guided simulations; (5) Implicit Solvent Modeler, PBEQ-Solver, and GCMC/BD Ion Simulator for implicit solvent related calculations; (6) Ligand Binder for ligand solvation and binding free energy simulations; and (7) Drude Prepper for preparation of simulations with the CHARMM Drude polarizable force field. Recently, new modules have been integrated into CHARMM-GUI, such as Glycolipid Modeler for generation of various glycolipid structures, and LPS Modeler for generation of lipopolysaccharide structures from various Gram-negative bacteria. These new features together with existing modules are expected to facilitate advanced molecular modeling and simulation thereby leading to an improved understanding of the molecular details of the structure and dynamics of complex biomolecular systems. Here, we briefly review these capabilities and discuss potential future directions in the CHARMM-GUI development project.

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Biasing Potential Replica Exchange Multisite λ-Dynamics for Efficient Free Energy Calculations

Cited by 33 publications

References 55 publications

Predicting Thermodynamic Properties over Combinatorially Large Chemical Spaces

Predicting Thermodynamic Properties over Combinatorially Large Chemical Spaces

Orthogonal sampling in free-energy calculations of residue mutations in a tripeptide: TI versusλ-LEUS

CHARMM‐GUI 10 years for biomolecular modeling and simulation

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