Estimating Atomic Contributions to Hydration and Binding Using Free Energy Perturbation

Irwin, Benedict; Huggins, David J.

doi:10.1021/acs.jctc.8b00027

Cited by 23 publications

(21 citation statements)

References 44 publications

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“…The host-guest systems used here are drawn from the SAMPL6 host-guest binding challenge [26]. We selected 5-hexenoic acid (OA-G3) and 4-methylpentanoic acid (OA-G6) as guest molecules of the octa-acid host (OA), and quinine (CB8-G3) for the cucurbit [8]uril (CB8) host ( Fig. 1).…”

Section: Selection Of the Three Host-guest Systemsmentioning

confidence: 99%

“…Among the methodologies that have been developed to carry out this task, physics-based methods employing classical force fields are starting to be routinely used in drug development projects and demonstrate success in real lead optimization scenarios [2][3][4][5]. These technologies are also often employed to obtain mechanistic insights into the physics of binding such as the discovery of binding poses [6] and pathways [7], or attempts at providing intuitive guidance on how to improve ligand binding potency [8]. However, the applicability domain of these models is currently limited to a narrow portion of the accessible chemical space for small molecules, and well-behaved protein-ligand systems that do not undergo significant conformational changes or solvent displacement on timescales larger than a few tens of nanoseconds [9,10].…”

Section: Introductionmentioning

confidence: 99%

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The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Rizzi

Jensen

Slochower

et al. 2020

J Comput Aided Mol Des

123

189

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Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

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Section: Selection Of the Three Host-guest Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Rizzi

Jensen

Slochower

et al. 2020

J Comput Aided Mol Des

123

189

View full text Add to dashboard Cite

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“…Hydration and host–guest binding ΔH and ΔS are typically computed less accurately than ΔG and show a more pronounced dependence on force field parameters . Overall, the accurate computation of ΔG is an important predictive method in biomolecular simulation (and in molecular science generally), and decompositions can inform the design of tighter binding host–guest complexes or appropriate protein mutations, leading to practical applications in drug design or nanotechnologies.…”

Section: Computation Of Free Energy Enthalpy and Entropymentioning

confidence: 99%

Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

Huggins

Biggin

Dämgen

et al. 2018

WIREs Comput Mol Sci

Self Cite

140

142

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development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology. enzyme, membrane, molecular dynamics, multiscale, protein, QM/MM 1 | INTRODUCTION Biomolecular simulations are now making significant contributions to a wide variety of problems in drug discovery, drug development, biocatalysis, biotechnology, nanotechnology, chemical biology, and medicine. Biomolecular simulation is a rapidly growing field in scale and impact, increasingly demonstrating its worth in understanding mechanisms and analyzing activities, and contributing to the design of drugs and biocatalysts. Physics-based simulations complement experiments in building a molecular-level understanding of biology: They can test hypotheses and interpret and analyze experimental data in terms of interactions at the atomic level. Different types of simulation techniques have been developed, which are applicable to a range of different problems in biomolecular science. Simulations have already shown their worth in helping to analyze how enzymes catalyze biochemical reactions, and how proteins adopt their functional structures, for example, within cell membranes. They contribute to the design of drugs and catalysts, and in understanding the molecular basis of disease. Simulations have played a key role in developing the conceptual framework now at the heart of biomolecular science: that the dynamics of biological molecules is central to their function. Developing methods from chemical physics and computational science will open exciting new opportunities in biomolecular science, including in drug design and development, biotechnology, and biocatalysis. With high-performance computing resources, large-scale atomistic simulations of biological machines such as the ribosome, proton pumps and motors, membrane receptor complexes, and even whole viruses have become possible. Useful simulations of smaller systems can be carried out with desktop resources, thanks to developments allowing, for example, graphics processing units (GPUs) to be used. A particular challenge across the field is the integration of simulations crossing the span of length-and timescales as different types of simulation method are required for different types of problems. 1 Biomolecular systems pose fundamental scientific challenges (e.g., protein folding, enzyme catalysis, gene regulation, disease mechanisms, and antimicrobial resistance) and are at the heart of many advanced technological developments (drug discovery, biotechnology, biocatalysis, biomaterials, and genetic engineering). Biomolecular systems are inherently complex and pose significant challenges in modeling. An essential underlying paradigm is the need to consider biomolecular ensembles and their dynamics, rather than simply static biomolecular structures to understand and predict their behavior and propertie...

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“…Such treatment reduces the orthogonal degrees of freedom that do not contribute meaningfully to the transition pathway and is commonly seen in free energy perturbation simulations. 66 The positional and orientational constraints ensure that structures derived along the pathway are relevant to the states defined by the cryo-EM maps.…”

Section: Steered Molecular Dynamics (Smd)mentioning

confidence: 99%

Data-guided Multi-Map variables for ensemble refinement of molecular movies

Vant

Sarkar

Streitwieser

et al. 2020

The Journal of Chemical Physics

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Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the 3-dimensional electron density of a protein, as it would be determined by cryo-EM or X-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous densitydriven molecular dynamics methodologies that determine only the best map-model fits, our work uses the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along reaction coordinates derived from electron densities can serve to induce conformational transitions between known intermediates. The simulated pathways appear reversible, with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein DataBank. We find that the best quantitative agreement with experimental freeenergy differences is obtained using medium resolution (∼5 Å) density information coupled to comparatively large structural transitions. Practical considerations for generating transitions with multiple intermediate atomic density distributions are also discussed.2

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Estimating Atomic Contributions to Hydration and Binding Using Free Energy Perturbation

Cited by 23 publications

References 44 publications

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

Data-guided Multi-Map variables for ensemble refinement of molecular movies

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