Calculation of the Redox Potential of the Protein Azurin and Some Mutants

Bosch, M.W.; Swart, Marcel; Snijders, J.G.; Berendsen, Herman J. C.; Mark, Alan E.; Oostenbrink, Chris; Gunsteren, W. F. van; Canters, Gerard W.

doi:10.1002/cbic.200400244

Cited by 49 publications

(54 citation statements)

References 52 publications

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“…In cycle closure analysis, the relative binding free energy of two ligands is computed by at least two different paths, forming a cycle. Cycle closure errors are reported relatively infrequently, but in cases where they have been reported, cycles may fail to close (indicating an error) by as much as several kcal/mol, far in excess of statistical error estimates, [83][84][85][86][87] suggesting serious sampling problems. This may contribute to relative free energy calculations' reputation for unreliability, where performance can be good in some systems and terrible in others.…”

Section: Free Energy Calculations Face Serious Challengesmentioning

confidence: 99%

Perspective: Alchemical free energy calculations for drug discovery

Mobley

Klimovich²

2012

The Journal of Chemical Physics

228

295

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Section: Free Energy Calculations Face Serious Challengesmentioning

confidence: 99%

Perspective: Alchemical free energy calculations for drug discovery

Mobley

Klimovich²

2012

The Journal of Chemical Physics

228

295

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“…In order to calculate the free energy of a redox process, the thermodynamic integration method [84][85][86][87][88][89][90][91][92][93][94][95] is employed. The relation between the reduced and oxidized species of the redox reaction can be expressed using a coupling parameter (η) as shown below.…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Computational Redox Potential Predictions: Applications to Inorganic and Organic Aqueous Complexes, and Complexes Adsorbed to Mineral Surfaces

Arumugam

Becker

2014

Minerals

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Abstract:Applications of redox processes range over a number of scientific fields. This review article summarizes the theory behind the calculation of redox potentials in solution for species such as organic compounds, inorganic complexes, actinides, battery materials, and mineral surface-bound-species. Different computational approaches to predict and determine redox potentials of electron transitions are discussed along with their respective pros and cons for the prediction of redox potentials. Subsequently, recommendations are made for certain necessary computational settings required for accurate calculation of redox potentials. This article reviews the importance of computational parameters, such as basis sets, density functional theory (DFT) functionals, and relativistic approaches and the role that physicochemical processes play on the shift of redox potentials, such as hydration or spin orbit coupling, and will aid in finding suitable combinations of approaches for different chemical and geochemical applications. Identifying cost-effective and credible computational approaches is essential to benchmark redox potential calculations against experiments. Once a good theoretical approach is found to model the chemistry and thermodynamics of the redox and electron transfer process, this knowledge can be incorporated into models of more complex reaction mechanisms that include diffusion in the solute, surface diffusion, and dehydration, to name a few. This knowledge is important to fully understand the nature of redox processes be it a geochemical process that dictates natural redox reactions or one that is being used for the optimization of a chemical process in industry. In addition, it will help identify materials that will be useful to design catalytic OPEN ACCESSMinerals 2014, 4 346 redox agents, to come up with materials to be used for batteries and photovoltaic processes, and to identify new and improved remediation strategies in environmental engineering, for example the reduction of actinides and their subsequent immobilization. Highly under-investigated is the role of redox-active semiconducting mineral surfaces as catalysts for promoting natural redox processes. Such knowledge is crucial to derive process-oriented mechanisms, kinetics, and rate laws for inorganic and organic redox processes in nature. In addition, molecular-level details still need to be explored and understood to plan for safer disposal of hazardous materials. In light of this, we include new research on the effect of iron-sulfide mineral surfaces, such as pyrite and mackinawite, on the redox chemistry of actinyl aqua complexes in aqueous solution.

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“…Furthermore, when sampling is inadequate, alchemical free energy methods can easily give biased results; for example, computed free energies are often sensitive to the choice of the initial receptor or ligand structure. [20][21][22][23][24][25]58 Here, to isolate sources of error, we study a highly simplified binding site using alchemical free energy methods and molecular dynamics. We focus on the binding of small aromatic ligands to the small, buried hydrophobic binding site in an engineered mutant of T4 lysozyme (the L99A site; Figure 1) that has been studied extensively experimentally, [26][27][28][29][30][31][32] with docking methods, 30,32 and in some previous computational free energy studies.…”

Section: Introductionmentioning

confidence: 99%

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

Mobley

Graves

Chodera

et al. 2007

Journal of Molecular Biology

311

501

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A central challenge in structure-based ligand design is the accurate prediction of binding free energies. Here we apply alchemical free energy calculations in explicit solvent to predict ligand binding in a model cavity in T4 lysozyme. Even in this simple site, there are challenges. We made systematic improvements, beginning with single poses from docking, then including multiple poses, additional protein conformational changes, and using an improved charge model. Computed absolute binding free energies had an RMS error of 1.9 kcal/mol relative to previously determined experimental values. In blind prospective tests, the methods correctly discriminated between several true ligands and decoys in a set of putative binders identified by docking. In these prospective tests, the RMS error in predicted binding free energies relative to those subsequently determined experimentally was only 0.6 kcal/mol. X-ray crystal structures of the new ligands bound in the cavity corresponded closely to predictions from the free energy calculations, but sometimes differed from those predicted by docking. Finally, we examined the impact of holding the protein rigid, as in docking, with a view to learning how approximations made in docking affect accuracy and how they may be improved.

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Calculation of the Redox Potential of the Protein Azurin and Some Mutants

Cited by 49 publications

References 52 publications

Perspective: Alchemical free energy calculations for drug discovery

Perspective: Alchemical free energy calculations for drug discovery

Computational Redox Potential Predictions: Applications to Inorganic and Organic Aqueous Complexes, and Complexes Adsorbed to Mineral Surfaces

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

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