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Michael Levitt – The Nobel Prize in Chemistry 2013
The Nobel Prize is an international award administered by the Nobel Foundation in Stockholm, Sweden. It has been awarded every year since 1901 for achievements in physics, chemistry, physiology or medicine, literature and for peace. Over the course of its history, many awards have been made for scientific achievements directly related to, or involving the use of, crystallographic methods and techniques.
Michael Levitt, FRS, is a biophysicist, IUCr author and professor of structural biology at Stanford University, CA, USA, a position he has held since 1987. Levitt, together with Martin Karplus and Arieh Warshel, received the 2013 Nobel Prize in Chemistry for 'the development of multiscale models for complex chemical systems'. In the field of structural analysis, Levitt and his co-workers have been instrumental in mimicking and predicting the behaviour of molecules using computers. An example is his work in modelling the structure of protein molecules in silico to answer complex questions concerning the role of amino acids in protein structure.
In 1967 Levitt gained a BSc in physics from King's College, London, England, and through true grit and determination won a place at the Laboratory of Molecular Biology, Cambridge University, England, to study for his doctorate in biophysics. Levitt spent a year at the Weizmann Institute in Israel before taking up his studies in Cambridge. Whilst in Israel, Levitt started to work in the then brand new field of computational structural biology as a programmer for Warshel and Shneior Lifson, writing the first program for simulation of protein motion by energy minimization.
It was John Kendrew who sent the young Levitt to Israel to work with Lifson on how to use potential energy functions and computer simulations to calculate the properties of macromolecules of biological interest, giving Levitt a running start during his PhD studies, as by the time he started in 1968 he had already written the first program to move the atoms of a protein by minimizing an empirically calibrated potential energy function. Levitt's PhD thesis was entitled 'Conformational Analysis of Proteins'. It described the basic form of the energy function, different coordinate systems and energy minimization, before introducing a method for deriving a potential energy function that stabilized the known X-ray structure of a protein molecule. It then used this potential to energy refine protein structures by moving atoms so that they fit stereochemical laws, to interpret problematic regions of electron-density maps, to examine the transition state in the enzyme action of lysozyme and to examine the tertiary structure changes occurring in haemoglobin.
Levitt was awarded his PhD in 1971, and the following year he took up a postdoctoral fellowship at the Weizmann Institute. In 1974 he moved back to the MRC Laboratory of Molecular Biology at Cambridge to work as a staff scientist. Levitt spent a couple of years as a visiting scientist with Francis Crick at the Salk Institute, La Jolla, CA, USA. In 1979 he became Associate and Full Professor of Chemical Physics at the Weizmann Institute. He was a visiting scientist at the MRC Laboratory of Molecular Biology, Cambridge, between 1986 and 1987, and in 1987 he moved to the USA to become Professor of Structural Biology at Stanford University.
Levitt's early work set the stage for most of what he and the field have been doing since. In the last 45 years computing has become less expensive by at least ten-million fold. Computers now dominate all walks of life, and biology has become an information-rich science. These developments were instrumental in simulating movement by Newton's second law as the power of the computer could model literally billions of iterations. The same methods are also applied to proteins, RNA and DNA and they can also be used to refine structures against NMR and X-ray data.
To this very day Levitt is still inspired and involved in many of the structural methods he and co-workers pioneered. For example, one aspect of his research focuses on the combined use of mass spectrometry, cryo-electron microscopy and R-value exploration of X-ray data with computer methods that allow large macromolecular complexes to be solved with less information.
One of Levitt's recent papers published with the IUCr looks at membrane proteins and large protein complexes that are notoriously difficult to study with conventional X-ray crystallography methods, not least because they are often very difficult, if not impossible, to crystallize, but also because their very nature means they are highly flexible. The result is that when a structure can be obtained it is often of low resolution, ambiguous and reveals a mosaic-like spread of protein domains that sometimes create more puzzles than they solve.
Levitt and co-workers have reviewed their earlier refinement technique known as Deformable Elastic Network (DEN) and found ways to optimize it successfully for the investigation of several particularly problematic protein structures, including soluble proteins and membrane proteins up to a resolution limit ranging from 3 to 7 Å [Schröder, Levitt & Brunger (2014). Acta Cryst. D70, 2241–2255; doi: http://doi.org/zm7].
The team explains that advances in X-ray technology and light sources have in recent years led to structures for previously intractable proteins such as the ribosome, transcription complexes and even viruses. The details then lie in a successful refinement that can provide valuable information about the structure in question despite lower resolution than would normally be desirable. 'The interpretation of low-resolution diffraction data is generally difficult', the team says, 'owing to the unfavorable ratio of parameters (variable degrees of freedom, such as flexible torsion angles or Cartesian atomic coordinates) to observables (observed diffraction intensities).' Ambiguities and errors of interpretation abound.
The DEN approach begins with a model, a prediction, of the target structure containing as much information as is known ahead of the insertion of the diffraction data, and determines which features of the model ought to be adjusted to fit the diffraction data emerging from the X-ray experiments. In other words, a null hypothesis is applied; those parts of the model not predicted to alter the diffraction data are retained as is. Distances between randomly chosen pairs of atoms within the structure are tested and fine-tuned accordingly within a distance restraint, customarily referred to as the elastic network.
Alongside many friends, mentors and colleagues (such as John Kendrew, Max Perutz, Francis Crick and Aaron Klug), Levitt feels the computer industry has also played an instrumental role in being able to accomplish all that he has.
'Computers and biology go together. Biology is very complicated, and computers are such wonderful, powerful tools. And they just keep getting more and more powerful.'by Jonathan Agbenyega, IUCr Business Development Manager (email@example.com)