Michael Levitt

Michael Levitt is a South African-born biophysicist recognized as one of the founding figures of computational biology. Levitt is a professor of structural biology at Stanford University. He earned his undergraduate degree in physics at King's College in London and his doctoral degree in computational biology from the University of Cambridge. Early in his academic journey, Levitt was inspired by John Kendrew's BBC television series "The Thread of Life," which influenced his decision to pursue a PhD at the Medical Research Council in Cambridge. This experience sparked his enduring interest in applying physics and computing to the life sciences.

In the 1970s, Levitt, along with Martin Karplus and Arieh Warshel, developed methods that combined quantum and classical mechanics to calculate chemical reactions using computers. Their research provided new insights into protein folding, enzyme catalysis, and the structural basis of diseases, revolutionizing biochemistry and molecular biology. Levitt's pioneering work in computational structural biology enabled the prediction of molecular structures, modeling of enzyme catalysis, refinement of experimental structures, and classification of protein structures. These advancements laid the foundation for modern computational biology and contributed to practical methods for antibody humanization, which are essential for anticancer therapies such as the drug Avastin.

Levitt received the Nobel Prize in Chemistry, together with Karplus and Warshel, for the development of multiscale models for complex chemical systems. He has been elected as a member of EMBO, a Fellow of the Royal Society, and a member of the National Academy of Sciences. Levitt has also received the DeLano Award for Computational Biosciences and the Rev. Joseph Carrier, C.S.C. Science Medal from the University of Notre Dame's College of Science, an honor awarded for sustained achievements in science.

Levitt was among the first researchers to conduct molecular dynamics simulations of DNA and proteins and developed the first software for this purpose. He is known for developing approaches to predict macromolecular structures and has participated in many Critical Assessment of Techniques for Protein Structure Prediction competitions. Levitt served as a founding co-editor of the Annual Review of Biomedical Data Science. His research continues to focus on the combined use of mass spectrometry, cryo-electron microscopy, and computational methods to solve large macromolecular complexes. Levitt's career demonstrates the impact of interdisciplinary science, bridging physics, chemistry, biology, and computing to advance medicine and deepen understanding of molecular life sciences.