Comparison of free‐energy methods using a tripeptide‐water model system

Maurer, Manuela; Hansen, Niels; Oostenbrink, Chris

doi:10.1002/jcc.25537

Cited by 10 publications

(11 citation statements)

References 47 publications

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“…This might of course be either because the experimental value is incorrect, but it is also at least equally likely that this is due to simplifications that are common in ligand design, in this case also in the employed program suite. This case study highlights the need for powerful computational methods to tackle the deceptively simple problem of water‐governed ligand binding.…”

Section: Ligands and Watermentioning

confidence: 99%

Water in protein hydration and ligand recognition

Maurer

Oostenbrink

2019

J of Molecular Recognition

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109

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This review describes selected basics of water in biomolecular recognition. We focus on a qualitative understanding of the most important physical aspects, how these change in magnitude between bulk water and protein environment, and how the roles that water plays for proteins arise from them. These roles include mechanical support, thermal coupling, dielectric screening, mass and charge transport, and the competition with a ligand for the occupation of a binding site. The presence or absence of water has ramifications that range from the thermodynamic binding signature of a single ligand up to cellular survival. The large inhomogeneity in water density, polarity and mobility around a solute is hard to assess in experiment. This is a source of many difficulties in the solvation of protein models and computational studies that attempt to elucidate or predict ligand recognition. The influence of water in a protein binding site on the experimental enthalpic and entropic signature of ligand binding is still a point of much debate. The strong water‐water interaction in enthalpic terms is counteracted by a water molecule's high mobility in entropic terms. The complete arrest of a water molecule's mobility sets a limit on the entropic contribution of a water displacement process, while the solvent environment sets limits on ligand reactivity.

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Section: Ligands and Watermentioning

confidence: 99%

Water in protein hydration and ligand recognition

Maurer

Oostenbrink

2019

J of Molecular Recognition

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109

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“… 22 , 25 − 29 However, for the small values of s that are often used, the energy minima of the reference Hamiltonian no longer correspond to the energy minima of the original end states, reducing the phase-space overlap and further complicating the search for the optimal values of s and Δ F i R . 28 , 30 − 34 A computationally expensive approach to bypass this problem uses replica-exchange simulations, in which multiple replicas are performed at different values of s . 35 , 36 …”

Section: Introductionmentioning

confidence: 99%

Toward Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS)

Perthold

Petrov

Oostenbrink

2020

J. Chem. Inf. Model.

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Free-energy perturbation (FEP) methods are commonly used in drug design to calculate relative binding free energies of different ligands to a common host protein. Alchemical ligand transformations are usually performed in multiple steps which need to be chosen carefully to ensure sufficient phase-space overlap between neighboring states. With one-step or single-step FEP techniques, a single reference state is designed that samples phase-space not only representative of a full transformation but also ideally resembles multiple ligand end states and hence allows for efficient multistate perturbations. Enveloping distribution sampling (EDS) is one example for such a method in which the reference state is created by a mathematical combination of the different ligand end states based on solid statistical mechanics. We have recently proposed a novel approach to EDS which enables efficient barrier crossing between the different end states, termed accelerated EDS (A-EDS). In this work, we further simplify the parametrization of the A-EDS reference state and demonstrate the automated calculation of multiple free-energy differences between different ligands from a single simulation in three different well-described drug design model systems.

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“…The KXK system (Figure a) consists of a Lys-X-Lys tripeptide, where X can be any amino acid. Experimental − and computational − studies have aimed at elucidating the effect of the central residue on the binding affinity toward oligopeptide binding protein A. The most recent studies ,, involved several alchemical free energy simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental − and computational − studies have aimed at elucidating the effect of the central residue on the binding affinity toward oligopeptide binding protein A. The most recent studies ,, involved several alchemical free energy simulations. The convergence of these calculations was found to be adversely affected by strongly differing conformational preferences between the end points, as well as large barriers along the backbone dihedral angles φ and ψ.…”

Section: Introductionmentioning

confidence: 99%

Overcoming Orthogonal Barriers in Alchemical Free Energy Calculations: On the Relative Merits of λ-Variations, λ-Extrapolations, and Biasing

Hahn

König

Hünenberger

2020

J. Chem. Theory Comput.

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Alchemical free energy calculations using conventional molecular dynamics and thermodynamic integration rely on simulations performed at fixed values of the coupling parameter λ. When multiple conformers in equilibrium are separated by high barriers in the space orthogonal to λ, proper convergence may require extremely long simulations. Four main strategies can be employed to address this orthogonal-sampling problem: (a) λ-variations, where λ can change along the simulations to circumvent barriers; (b) λ-extrapolations, where statistical information is transferred between λ-points; (c) specific biasing, where orthogonal barriers are reduced using a biasing potential designed specifically for the system; and (d) generic biasing, where orthogonal barriers are reduced using a generic approach. Here, we investigate the relative merits of the first three strategies considering two benchmark systems. The KXK system involves a mutation of the central residue in a tripeptide to a glycine and the XTP system involves a hydrogen-to-bromine mutation in the base of a nucleotide. Three sampling methods are compared, the latter two involving λ-variations: molecular dynamics simulations with fixed λ-points, Hamiltonian replica exchange, and the recently introduced conveyor belt method. Two free energy estimators are applied, the second one involving λ-extrapolations: thermodynamic integration with Simpson quadrature and the multistate Bennett acceptance ratio. Finally, three different seeding schemes are considered for the generation of the initial configurations. For both benchmark systems, λ-extrapolations are found to provide little gain, whereas λ-variations can significantly enhance the convergence. They are sufficient on their own if the orthogonal barriers are low in at least one state (e.g., the glycine state in KXK). However, if the orthogonal barriers are high over the entire λ-range (e.g., the XTP system), λ-variations are only effective when applied together with a specific biasing for introducing such a low-barrier state.

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Comparison of free‐energy methods using a tripeptide‐water model system

Cited by 10 publications

References 47 publications

Water in protein hydration and ligand recognition

Water in protein hydration and ligand recognition

Toward Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS)

Overcoming Orthogonal Barriers in Alchemical Free Energy Calculations: On the Relative Merits of λ-Variations, λ-Extrapolations, and Biasing

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