2013.10.09 Wed posted at 19:25 JST
【ExecutiveVoice】ビッグデータ活用のカギは職人依存からの脱却--統計数理研究所 所長 樋口氏
9 October 2013
The Royal Swedish Academy of Sciences has decided to award
the Nobel Prize in Chemistry for 2013
Université de Strasbourg, France and Harvard University, Cambridge, MA, USA
Stanford University School of Medicine, Stanford, CA, USA
University of Southern California, Los Angeles, CA, USA
“for the development of multiscale models for complex chemical systems”
The computer ― your Virgil in the world of atoms
Chemists used to create models of molecules using plastic balls and sticks. Today, the modelling is carried out in computers. In the 1970s, Martin Karplus, Michael Levitt and Arieh Warshel laid the foundation for the powerful programs that are used to understand and predict chemical processes. Computer models mirroring real life have become crucial for most advances made in chemistry today.
Chemical reactions occur at lightning speed. In a fraction of a millisecond, electrons jump from one atomic nucleus to the other. Classical chemistry has a hard time keeping up; it is virtually impossible to experimentally map every little step in a chemical process. Aided by the methods now awarded with the Nobel Prize in Chemistry, scientists let computers unveil chemical processes, such as a catalyst’s purification of exhaust fumes or the photosynthesis in green leaves.
The work of Karplus, Levitt and Warshel is ground-breaking in that they managed to make Newton’s classical physics work side-by-side with the fundamentally different quantum physics. Previously, chemists had to choose to use either or. The strength of classical physics was that calculations were simple and could be used to model really large molecules. Its weakness, it offered no way to simulate chemical reactions. For that purpose, chemists instead had to use quantum physics. But such calculations required enormous computing power and could therefore only be carried out for small molecules.
This year’s Nobel Laureates in chemistry took the best from both worlds and devised methods that use both classical and quantum physics. For instance, in simulations of how a drug couples to its target protein in the body, the computer performs quantum theoretical calculations on those atoms in the target protein that interact with the drug. The rest of the large protein is simulated using less demanding classical physics.
Today the computer is just as important a tool for chemists as the test tube. Simulations are so realistic that they predict the outcome of traditional experiments.
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Martin Karplus, U.S. and Austrian citizen. Born 1930 in Vienna, Austria. Ph.D. 1953 from California Institute of Technology, CA, USA. Professeur Conventionné, Université de Strasbourg, France and Theodore William Richards Professor of Chemistry, Emeritus, Harvard University, Cambridge, MA, USA.
Michael Levitt, U.S., British and Israeli citizen. Born 1947 in Pretoria, South Africa. Ph.D. 1971 from University of Cambridge, UK. Robert W. and Vivian K. Cahill Professor in Cancer Research, Stanford University School of Medicine, Stanford, CA, USA.
Arieh Warshel, U.S. and Israeli citizen. Born 1940 in Kibbutz Sde-Nahum, Israel. Ph.D. 1969 from Weizmann Institute of Science, Rehovot, Israel. Distinguished Professor, University of Southern California, Los Angeles, CA, USA.
The Prize amount: SEK 8 million, to be shared equally between the Laureates.
Contact: Perina Stjernlöf, Press Officer/Editor, Phone +46 8 673 95 44, +46 70 673 96 50, firstname.lastname@example.org
The Royal Swedish Academy of Sciences, founded in 1739, is an independent organization whose overall objective is to promote the sciences and strengthen their influence in society. The Academy takes special responsibility for the natural sciences and mathematics, but endeavours to promote the exchange of ideas between various disciplins.
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Scientific Background on the Nobel Prize in Chemistry 2013
DEVELOPMENT OF MULTISCALE MODELS FOR
COMPLEX CHEMICAL SYSTEMS
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Scientific background on the Nobel Prize in Chemistry 2013
DEVELOPMENT OF MULTISCALE MODELS FOR COMPLEX CHEMICAL SYSTEMS
The Royal Swedish Academy of Science has decided to award the 2013 Nobel Prize in Chemistry
Martin Karplus, Harvard U., Cambridge, MA, USA
Michael Levitt, Stanford U., Stanford, CA, USA
Arieh Warshel, U. Southern Ca., Los Angeles, CA, USA
“Development of Multiscale Models for Complex Chemical Systems”
Multiscale models for Complex Chemical Systems
The Nobel Prize in Chemistry 2013 has been awarded to Martin Karplus, Michel Levitt and
Arieh Warshel for development of multiscale models of complex chemical systems.
Chemistry and Biochemistry have developed very rapidly during the last 50 years. This applies
to all parts of the fields, but the development of Biochemistry is perhaps the most striking one.
In the first half of these 50 years the determination of protein structure was perhaps the field
where the largest efforts were spent and the largest progress was made. The standard methods
to analyse the structure of proteins are X-ray crystallography of crystals or analysing the spin –
spin couplings obtained from NMR-spectroscopy. What is perhaps less well known is that in the
computer programs that are used to analyse the diffraction pattern from an X-ray investigation
or the spin-spin couplings obtained from a NMR experiment there is hidden a computer code
that calculates the energy of the considered structure based on empirically and theoretically
obtained potentials describing the interaction between the atoms in the system. The reason for
this is that there is not enough experimental information to uniquely determine the structure of
the studied system. This is just one of the aspects of how computers and theoretical models have
become essential tools for the experimental chemist.
Today the focus of chemical research is much more on function than on structure. Chemists asks
questions like “How does this happen?” rather than “What does this look like?” . Question about
function are generally difficult to answer using experimental techniques. Isotope labelling and
femtosecond spectroscopy can give clues, but rarely produce conclusive evidence for a given
mechanism in systems with the complexity characterizing many catalytic chemical processes
and almost all biochemical processes. This makes theoretical modelling an important tool as a
complement to the experimental techniques. Chemical processes are characterized by a
transition state, a configuration with the lowest possible (free) energy that links the product(s)
with the reactant(s). This state is normally not experimentally accessible, but there are
theoretical methods to search for such structures. Consequently theory is a necessary
complement to experiment.
The work awarded this year´s Nobel Prize in Chemistry focuses on the development of methods
using both classical and quantum mechanical theory and that are used to model large complex
chemical systems and reactions. In the quantum chemical model the electrons and the atomic
nuclei are the particles of interest. In the classical models atoms or group of atoms are the
particles that are described. The classical models contain much fewer degrees of freedom and
they are consequently evaluated much faster on a computer. Further more, the physics that is
used to describe the classical particles is much simpler and this also contributes to speeding up
the modelling on a computer. This year´s laureates have shown how to develop models that
describe part of a system using first principle, quantum chemical models for a central part of the
system and how to link this part to a surrounding, which is modelled using classical particles
(atoms or group of atoms). The key accomplishment was to show how the two regions in the
modelled system can be made to interact in a physically meaningful way. Frequently the entire
molecular system is embedded in a dielectric continuum. A cartoon of a typical system is shown
in Figure 1.
Figure 1 Multi-copper-oxidase embedded in water 1
Theoretical modelling as described above rests on basically four different types of development.
The central region in the system, the spacefilling atoms (red and gray), is described using a
Quantum Chemical method 2. Walter Kohn and John Pople were awarded the Nobel Prize in
Chemistry 1998 for the development of such methods. The development of Quantum
Mechanics3, which the Quantum Chemistry rests on is almost 75 years older and was the basis
for five different Nobel Prizes in Physics from 1918 – 1933. The laureates were M. Planck in
1918, N. Bohr in 1922, Prince de Broglie in 1929, W. Heisenberg in 1932, and E. Schrödinger and
P. Dirac in 1933.
The theory used for the modelling of the surrounding molecular system consists of several pieces.
First of all a model is needed to describe the intra molecular potential for these molecules. The
model that is used today originates from 1946, when three groups4-6 independently suggested
such a model, based on Coulomb and van der Waals (van der Waals was awarded the Nobel Prize
in Physics 1910) interactions. F.H. Westheimers group soon became leading in this field. In those
days computers did not exist. N. Allinger developed computer code and used computers to
optimize the structure of molecules using such classical, empirical potentials in a set of molecular
called MM1, MM2 and so on. In these methods the energy of the system was
minimized to obtain the structure of the studied system. The MM-methods were primarily used
for systems built from organic molecules.
In a parallel line of development G. Némety and H. Scheraga8 used the ideas of Westheimer and
Allinger and developed simplified versions of their potentials for the use in statistical mechanics
simulations and for energy minimisation of protein structures. Roughly at this time quantum
chemical methods started be used for the construction of inter- and intra-molecular potentials
for complex systems. Leading persons in this field were S. Lifson and A. Warshel with the
development of the Consistent Force Field (CFF) method9. M. Levitt and S. Lifson were the first
to use such potentials to minimize the energy of a protein10. Another well-known example of a
theoretically constructed potential was the so-called MCY11
potential for the water – water
interaction. This potential was based entirely on quantum chemical calculations that were used
to create a classical potential with terms describing electrostatic and van der Waals interactions.
The advantage of the classical potential-based methods is that the energy can easily be evaluated
and large systems can be studied. The drawback is that they can only be used for structures
where the interacting molecules are weakly perturbed. Consequently they cannot be used for the
study of chemical reactions where new molecules are formed from the reactants.
Conversely, quantum chemical methods can be used for the study of chemical reactions where
molecules are formed and destroyed, but they are very demanding with respect to computer
time and storage and only smaller systems can be handled.
Given that the problem with the potential functions describing the surrounding is solved, the
problem of deciding the proper conformation(s) for the surrounding remains. There are two
different approaches to this problem, the one used by Allinger in his MMX methods, to
minimize the energy of the system and generate one characteristic conformation, and that used
by Némety and Scheraga, to use statistical mechanics methods, like Molecular Dynamics (MD) 12
or Monte Carlo (MC) 12 and generate many configurations with a correct (in principle) statistic
The importance of the work of the laureates is independent of what strategy is used for the
choice of studied configuration(s). The prize focuses on how to evaluate the variation in the
energy of the real system in a accurate and efficient way for systems where relatively large
geometry changes or changes in electronic configuration in a smaller part of the studied system
is strongly coupled to a surrounding that is only weakly perturbed. One way to address this
problem is to develop an efficient computer code based on the Schrödinger equation that makes
it possible to handle systems of the size that is required. The Car – Parinello approach 13 is the
leading strategy along this line. It is however still too demanding with respect to computer
resources to be able to handle the large systems necessary for bio-molecular modelling or
extended supra-molecular systems with the required accuracy. The solution to the problem is
instead to combine classical modelling of the larger surrounding, along the line suggested by
Westheimer 4, Allinger 7
, Némety and Scheraga 8, with quantum chemical modelling of the core
region, where the chemically interesting action takes place.
The contributions of the three laureates
The first step in the development of multi scale modelling was taken when Arieh Warshel came
to visit Martin Karplus at Harvard in the beginning of the 70’ies. Warshel had a background in
inter- and intra-molecular potentials and Karplus had the necessary quantum chemical
experience. Together they constructed a computer program that could calculate the π-electron
spectra and the vibration spectra of a number of planar molecules with excellent results14. The
basis for this approach was that the effects of the σ-electrons and the nuclei were modelled using
a classical approach and that the π-electrons were modelled using a PPP15 (Praiser – Parr –
Pople) quantum chemical approach corrected for nearest overlap. Figure 2 shows a typical
molecule studied in that work
Figure 2. The mirror symmetric molecule 1,6-Diphenyl-1,3,5-hexatriene studied by Martin Karplus and
This was the first work to show that it is possible to construct hybrid methods that combine the
advantages of classical and quantum methods to describe complex chemical systems. This
particular method is restricted to planar systems where symmetry makes a natural separation
between the π-electrons that were quantum chemically described and the σ-electrons that were
handled by the classical model, but this is not a principal limitation, as was shown a few years
later, in 1976, when Arieh Warshel and Michel Levitt showed that it is possible to construct a
general scheme for a partitioning between electrons that are included in the classical modelling
and electrons that are explicitly described by a quantum chemical model. This was made in their
study of the “Dielectric, Electrostatic and Steric Stabilisation of the Carbonium Ion in the
Reaction of Lysosyme”16. Several fundamental problems needed to be solved in order for such a
procedure to work. Energetic coupling terms that model the interaction between the classical
and the quantum system must be constructed, as well as couplings between the classical and
quantum parts of the system with the dielectric surrounding. The studied system is shown in
Figure 3. To understand how lysozyme cleaves a glycoside chain, it is necessary to model only the relevant
parts of the system using quantum chemistry, while most of the surrounding may be treated using
molecular mechanics or a continuum model. The figure is adapted from16
In the time between the publishing of the two publications referred to above (1975), an other
important step, which made it possible to study even larger systems, was taken by Michel Levitt
and Arieh Warshel in their study of the folding of the protein Bovine Pancreas Trypsin Inhibitor
(BPTI)17. The type of simplifications of the studied system used in that study is illustrated in
Figure 4. The detailed structure of a polypeptide chain (top) is simplified by assigning each amino acid
residue with an interaction volume (middle) and the resulting string-of-pearls like structure (bottom) is
used for the simulation.
In this work, the folding of the protein from an open conformation to a folded conformation was
studied, and it was shown that it is possible to group atoms in a classical system into rigid units
and to treat these as classical pseudo atoms. Obviously, this approach further speeds up the
modelling of a system.
Multiscale modelling today
The work behind this year’s Nobel Prize has been the starting point for both further theoretical
developments of more accurate models and applied studies. Important contributions have been
given not only by this year’s laureates18-20 but also by many others including J. Gao 21
, F. Maceras
and K. Morokuma 22, U.C. Sing and P. Kollman 23 and H. M. Senn and W. Thiel 24. The
methodology has been used to study not only complex processes in organic chemistry and
biochemistry, but also for heterogeneous catalysis and theoretical calculation of the spectrum of
molecules dissolved in a liquid. But most importantly, it has opened up a fruitful cooperation
between theory and experiment that has made many otherwise unsolvable problems solvable.
1. Figure 1 was kindly provided by Professor Ulf Ryde
2. Nobel Prize in Chemistry 1998
3. Nobel Prize in Physics 1918, 1922, 1929, 1932, 1933
4. F. H. Westheimer and J. E. Mayer, J. Chem. Phys. 14, 733, 1946.
5. T. L. Hill, J. Chem. Phys. 14, 465, 1946.
6. J. Drostovsky, E. D. Hughes and C. K. Ingold, J. Chem. Soc. 173, 1946.
7. N. L. Allinger, M. A. Miller, L. W. Chow, R. A. Ford and J. C. Graham, J. Amer.
Chem. Soc. 87, 3430, 1965, N. L. Allinger, M. A. Miller, F. A. VanCatledge and
J. A. Hirsch, J. Amer. Chem. Soc. 89, 4345, 1967.
8. G. Némethy and H. Scheraga, Biopolymers 4,155,1965.
9. S. Lifson and A. Warshel, J. Chem.Phys. 49, 5116, 1968.
10. M. Levitt and S. Lifson, J. Mol. Biol. 46, 269, 1969.
11. O. Matsuoka, E. Clementi and M. Yoshimine, J. Chem. Phys. 66, 1351, 1976.
12. See e.g. Understanding Molecular Simulations by D. Frenkel and B. Smit,
Academic Press, San Diego, USA, 1996.
13. R. Car and M. Parinello, Phys. Rev. Lett. 55, 2471, 1985.
14. A. Warshel and M. Karplus, J. Amer. Chem. Soc. 94, 5612, 1972.
15. R. Praiser and R. Parr, J. Chem.Phys. 21, 466, 1953., J. A. Pople, Trans.
Faraday Soc. 49, 1375, 1953.
16. A. Warshel and M. Levitt, J. Mol. Biol. 103, 227, 1976.
17. M. Levitt and A. Warshel, Nature 253, 694, 1975.
18. M. Levitt, J. Mol. Biol. 104, 59, 1976.
19. S. Mukherjee and A. Warshel, PNAS 109, 14881, 2012.
20. B. M. Messer, M Roca, Z. T. Chu, S. Vicatos, A. V. Kilshtain and A. Warshel,
Proteins 78, 1212, 2010.
21. J. Gao, Rev. Comput. Chem. 7,119,1996.
22. F. Maceras and K. Morokuma, J. Comput. Chem. 16, 1170, 1995.
23. U. C. Singh and P. Kollman, J. Comput. Chem. 7, 718, 1986.
24. H. M. Senn and W. Thiel, Angew. Chem. Int. Ed. Engl. 48, 1198, 2009.