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This article reviews recent developments and applications in the area of computational electrochemistry. Our focus is on predicting the reduction potentials of electron transfer and other electrochemical reactions and half-reactions in both aqueous and nonaqueous solutions. Topics covered include various computational protocols that combine quantum mechanical electronic structure methods (such as density functional theory) with implicit-solvent models, explicit-solvent protocols that employ Monte Carlo or molecular dynamics simulations (for example, Car-Parrinello molecular dynamics using the grand canonical ensemble formalism), and the Marcus theory of electronic charge transfer. We also review computational approaches based on empirical relationships between molecular and electronic structure and electron transfer reactivity. The scope of the implicit-solvent protocols is emphasized, and the present status of the theory and future directions are outlined.

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Continuum solvent calculations of pKas and reduction potentials usually entail the use of a thermodynamic cycle to express the reaction free energy in terms of gas phase energies and free energies of solvation. In this work, we present a systematic study comparing the solution phase free energy changes obtained in this manner with those directly computed within the SMD solvation model against a large test set of 117 pKas and 42 reduction potentials in water and DMSO. The inclusion of vibrational contributions in the free energy of solvation has a negligible impact on the accuracy of thermodynamic cycle predictions of pKas and reduction potentials. Additionally, when gas phase energies in the thermodynamic cycle are computed at more accurate levels of theory, very similar results (mean unsigned difference of 0.5 kcal mol(-1)) can be achieved when the high-level computations (MP2/GTMP2Large and G3(MP2)-RAD(+)) are directly carried out within the continuum model. Increasing the accuracy of the electronic structure theory may or may not improve the agreement with experiment suggesting that the error is largely in the solvation model. For amino acids where their gas and solution phase species exist as different tautomers, the direct approach provided a significant improvement in calculated pKas. These results demonstrate that direct calculation of solution phase pKas and reduction potentials within the SMD model provides a general and reliable approximation to corresponding thermodynamic cycle based protocols, and is recommended for systems where solvation induced changes in geometry are significant. Further studies are necessary to ascertain whether the results are generalisable to other continuum solvation models.

Determining the principal energy pathways for allosteric communication in biomolecules, that occur as a result of thermal motion, remains challenging due to the intrinsic complexity of the systems involved. Graph theory provides an approach for making sense of such complexity, where allosteric proteins can be represented as networks of amino acids. In this work, we establish the eigenvector centrality metric in terms of the mutual information, as a mean of elucidating the allosteric mechanism that regulates the enzymatic activity of proteins. Moreover, we propose a strategy to characterize the range of the physical interactions that underlie the allosteric process. In particular, the well known enzyme, imidazol glycerol phosphate synthase (IGPS), is utilized to test the proposed methodology. The eigenvector centrality measurement successfully describes the allosteric pathways of IGPS, and allows to pinpoint key amino acids in terms of their relevance in the momentum transfer process. The resulting insight can be utilized for refining the control of IGPS activity, widening the scope for its engineering. Furthermore, we propose a new centrality metric quantifying the relevance of the surroundings of each residue. In addition, the proposed technique is validated against experimental solution NMR measurements yielding fully consistent results. Overall, the methodologies proposed in the present work constitute a powerful and cost effective strategy to gain insight on the allosteric mechanism of proteins.Allostery is a ubiquitous process of physico-chemical regulation in biological macromolecules such as enzymes. The fundamental step in the allosteric regulation is the binding of a ligand at a particular enzymatic site affecting the activity at a different and often very distant position of the protein. While allosteric processes have long been of interest, especially due to their relevance in developing potent and selective therapeutics, the mechanism for energy transfer between allosteric sites remains poorly understood. Thus, establishing a molecular level understanding of communication pathways between the physically distant enzymatic sites is crucial for the design of innovative drug therapies[1, 2] and protein engineering [3][4][5].Recently, there have been significant efforts toward the development of computational tools to support, interpret and/or predict experimental evidences for elucidation of allosteric pathways in proteins [2,[6][7][8][9][10][11][12]. Network analysis has been extensively used in this context, by incorporating concepts and methodologies from graph theory into the realm of molecular dynamics simulations [13][14][15][16][17][18][19] For instance, community network analysis (CNA) has emerged as a powerful and increasingly popular approach to analyze the dynamics of enzymes and protein/DNA (and/or RNA) complexes and to detect possible allosteric pathways [20][21][22][23][24][25][26].In these network theory-based approaches, a protein is represented as a network consisting of a set of nodes, n...

Surprisingly small structural changes in Donor–Acceptor Stenhouse Adducts (DASAs) result in predictable, robust and effective photochromic switches.

In this study, the aqueous pKa values for 13 neutral, 10 cationic, and 5 anionic carbon acids, including amino acids, peptides, and related species have been calculated using the high level ab initio composite procedure, G3MP2+//BMK, combined with solvation energies that were calculated using the CPCM-(UAKS/UAHF), COSMO-RS, and SM6 continuum models. The pKas were further calculated using three schemes, namely the direct method and the proton exchange method as well as the inclusion of an explicit solvent water molecule. The results of this study indicate that the direct method is unsuitable for computing the pKa of carbon acids, whereas the other two schemes perform significantly better with varying degrees of success, depending on the charge of the carbon acid. Specifically, the combination of the proton exchange scheme and CPCM-UAKS model performed particularly well for neutral species, with mean absolute deviations (MADs) of ∼1 pKa unit. The ionic species were more problematic, though the combination of the proton exchange scheme and the SM6 and CPCM-UAKS models performed reasonably well for the cationic and anionic acids, respectively. The inclusion an explicit water molecule generally improved the calculated values for anionic carbon acids.

Computational prediction of condensed phase acidity is a topic of much interest in the field today. We introduce the methods available for predicting gas phase acidity and pKas in aqueous and non-aqueous solvents including high-level electronic structure methods, empirical linear free energy relationships (LFERs), implicit solvent methods, explicit solvent statistical free energy methods, and hybrid implicit–explicit approaches. The focus of this paper is on implicit solvent methods, and we review recent developments including new electronic structure methods, cluster-continuum schemes for calculating ionic solvation free energies, as well as address issues relating to the choice of proton solvation free energy to use with implicit solvation models, and whether thermodynamic cycles are necessary for the computation of pKas. A comparison of the scope and accuracy of implicit solvent methods with ab initio molecular dynamics free energy methods is also presented. The present status of the theory and future directions are outlined.