As the recipients of the 2013 science Nobel prizes gather in Stockholm to celebrate and be celebrated, News & Views shares some expert opinions on the achievements honoured.
The Nobel Prize in Chemistry was awarded to Martin Karplus, Michael Levitt and Arieh Warshel for their work on developing multiscale models for complex chemical systems (see figure).
JOHAN JARNESTAD/THE ROYAL SWEDISH ACADEMY OF SCIENCES
by Walter Thiel
Complex chemical processes occur on different length- and timescales. Events that involve electrons, such as the making and breaking of chemical bonds, are localized in space and time. They need to be described by quantum mechanics (QM), whereas the influence of the environment and the slow motions of atoms during a reaction are normally well represented by classical molecular mechanics (MM).
The laureates were the first to propose a hybrid QM/MM approach for studying chemical properties and reactions, initially for the special case of planar molecules1 and then as a general scheme for modelling enzymatic reactions2. This mathematical approach is essentially a marriage of Schrödinger's quantum theories and classical Newtonian ideas, combining the best of both worlds to enable tailor-made simulations of complex chemical processes.
The prizewinners' pioneering work in the 1970s provided explicit expressions for calculating the total QM/MM energy of a system and the QM/MM interaction terms. Advances by many research groups in QM and MM methods during the 1980s paved the way to breakthroughs for QM/MM modelling in chemistry in the 1990s. Major methodological issues were then solved by establishing suitable QM/MM interaction models and treatments for the QM/MM interface region, and efficient procedures were implemented for exploring large-scale QM/MM potential surfaces (which represent total energy as a function of atomic position). Since then, there has been an exponential growth in QM/MM applications3, all underpinned by the original work of Karplus, Levitt and Warshel.
The concept of multiscale modelling is actually much broader than QM/MM, and so one can safely expect further progress towards an ever more realistic modelling of increasingly complex chemical processes.
by Gerhard Hummer
Multiscale molecular simulations, as pioneered by Karplus, Levitt and Warshel, proved to be versatile and powerful right from the start, revealing how receptors in the eye are activated by light, and how the resulting signals are passed on through changes in molecular conformation.
The laureates' approach allows each part of a molecular system to be described at the simplest level possible: as atoms, using quantum or classical mechanics; as classical pseudoparticles that represent multiple atoms; or, in the case of bulk solvent, as a continuous medium that lacks atomic detail2,4. Molecular interactions are captured by potential surfaces. Such potentials are now used routinely to determine protein structures from experimental data, to develop new drugs and to rationally design materials.
Simulations also provide fundamental insight into the function of biomolecular 'machinery' by revealing the underlying molecular motions and energetic driving forces. From photosynthesis to the processing of genetic material5, enzyme-catalysed reactions have been modelled and followed atom by atom, bond by bond3. The dynamics of molecular motors that power muscle contraction or the synthesis of ATP molecules — a cell's source of energy — have also been simulated. Even the self-assembly of biomolecular machinery can be studied, from the folding of proteins6 to the formation of entire organelles7 and the protein shells of viruses8.
With increasingly accurate representations of the energetics and dynamics of molecular systems, simulations yield detailed quantitative information and mechanistic insight that are unattainable in laboratory experiments. The vision of computational modelling as a reliable substitute for actual experiments is thus becoming a reality.
Warshel, A. & Karplus, M. J. Am. Chem. Soc. 94, 5612–5625 (1972).
Warshel, A. & Levitt, M. J. Mol. Biol. 103, 227–249 (1976).
Senn, H. M. & Thiel, W. Angew. Chem. Int. Edn 48, 1198–1229 (2009).
Levitt, M. & Warshel, A. Nature 253, 694–698 (1975).
Rosta, E., Nowotny, M., Yang, W. & Hummer, G. J. Am. Chem. Soc. 133, 8934–8941 (2011).
Lindorff-Larsen, K., Piana, S., Dror, R. O. & Shaw, D. E. Science 334, 517–520 (2011).
Takamori, S. et al. Cell 127, 831–846 (2006).
Zhao, G. et al. Nature 497, 643–646 (2013).
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