Ever since the catalytic power of enzymes was first recognised, chemists have wondered and argued about how they work. Enzymes are outstandingly efficient natural catalysts. Better understanding of the mechanisms by which they achieve these catalytic properties promises technological spin-offs such as routes to new drugs (many drugs are enzyme inhibitors, which bind to enzymes and prevent them from functioning), analysis of the effects of genetic variation and mutation (for example in predicting individual metabolism of pharmaceuticals); and the design of new catalysts (for example biomimetic catalysts or engineered enzymes). There is great interest in developing protein catalysts for practical applications, for instance in the pharmaceutical, chemical and biotechnology industries. Computational modelling has a vital role to play in these developments: unstable species such as transition states and reaction intermediates are crucial to questions of reactivity, but cannot be studied directly by experiment in systems as complex as enzymes. The field of enzyme reaction modelling has grown enormously in recent years and has matured to the point that computational enzymology is increasingly recognised as essential for understanding these fascinating biological catalysts [1-4]. Recent calculations [5] bring a new level of accuracy to bear on the problem, essential for quantitative conclusions and comparisons with experiment.
Combined quantum mechanics/molecular mechanics (QM/MM) methods allow enzyme reactions to be modelled: a small region at the active site (where the reaction happens) is treated by a quantum mechanical electronic structure method; this region interacts with the protein and solvent environment, which are included more simply (though in atomic detail) by an empirical 'molecular mechanics' force field [1,2,4,6]. This approach combines the simplicity and speed of the MM treatment of the protein structure with the flexibility and power of a quantum chemical treatment (which allows modelling of bond breaking and making, and electronic polarization). Until recently, QM/MM investigations of enzymes have generally been limited to relatively low levels of QM theory, such as semi-empirical methods or density functional theory (DFT). Semi-empirical methods are computationally cheap, fast enough for QM/MM molecular dynamics simulations, but error-prone, and give reaction energies and barriers that can be in error by 10 kcal/mol or more. DFT (especially with the B3LYP hybrid functional) offers improved accuracy, and has opened new classes of enzymes (particularly metalloenzymes) to computational investigation [7], such as studies of cytochrome P450 enzymes that metabolize drugs in the body [8,9]. These DFT methods, however, lack key physical interactions, such as dispersion, which are important in the binding of ligands to proteins. DFT often gives barrier heights that are too low by several kcal/mol, and it does not offer a route to their systematic improvement or testing, making it difficult to assess the accuracy of results. Other modelling methods such as the empirical valence bond technique can give excellent results for enzyme activation energies [3,10], and have provided important insights into the basic causes of catalysis. Such empirical approaches, however, require extensive fitting to experimental data, and do not consider the electronic structure explicitly.
Enzymology has been marked by vigorous debates and controversial proposals about enzyme mechanisms, and about the physical origins of enzyme catalysis. Identifying the chemical mechanisms of enzymes has proved difficult: it is often hard to differentiate between alternative proposals, and many 'textbook' mechanisms are probably incorrect in important details. Recent controversies over enzyme catalysis include proposals of 'low-barrier' hydrogen bonds [11-14], 'near-attack conformations' [4,15,16], the role of enzyme dynamics in catalysis [2,3], quantum tunnelling [17] and entropic effects [3]. The applicability of transition state theory to enzyme reactions has been questioned. These arguments have often proved extremely difficult to resolve, because the complexity and large size of enzymes makes experimental analysis very difficult. Atomistic simulations have a potentially vital role to play in these debates, in the interpretation of experimental data, and in providing a molecular level picture of reactions in enzymes. Calculations have the potential to identify probable mechanisms, and to analyse key interactions and catalytic effects. For quantitative comparisons with experiments, and reliable predictions, high-level electronic structure methods are needed. Recent work by Claeyssens et al. has shown that it is now possible to achieve an unprecedented level of accuracy for enzyme-catalysed reactions in QM/MM calculations [5]. Calculated activation energies for two enzyme reactions agree very well with experiment; indeed the agreement is so good that, given the known properties of the high-level methods now available, it is clear that near chemical accuracy (1 kcal/mol) can be achieved in calculations on enzyme-catalysed reactions. Such quantitative predictions in first principles calculations were only previously possible for very small molecules. These findings herald a new era of computational biochemistry.
Answers to all your Biology Questions
