DeLano Award for Computational Biosciences

Nobel laureate Levitt: ‘an intellectual leader’

Michael Levitt

This is the only prize I received in the decades preceding the Nobel Prize. Warren DeLano showed exceptional foresight in his pioneering use of Python, a trend adopted by much modern scientific software. His is a remarkable legacy I am proud to honor.


Michael Levitt of the Stanford University School of Medicine is the recipient of the DeLano Award in Computational Biosciences awarded by the American Society for Biochemistry and Molecular Biology.
The award recognizes scientists for their innovative development of computational technologies that enable life-science research at the molecular level.
Levitt, who won the Nobel Prize in chemistry in 2013, is well known for his pioneering work in modern computational biosciences – work that embodies the key elements of the DeLano award: the productive use of computers to accelerate research and ready access to these programs for the scientific community.
Raised in South Africa and later in England, Levitt earned his bachelor’s degree in physics from King’s College London. He studied at the Medical Research Council Laboratory of Molecular Biology, Cambridge (a part of Cambridge University), where he earned his Ph.D. in computational biology. He then did his postdoc at the Weizmann Institute of Science in Israel, where he later became a citizen and did a few weeks of basic service in the Israeli Defense Forces.
In the year that he spent in Israel before starting his Ph.D., Levitt published the first protein simulation using Cartesian coordinates. That revolutionary paper laid the groundwork for molecular simulations.
Later, with Arieh Warshel, Levitt described a framework for simulation studies combining quantum mechanical and classic mechanical methods. In other work done at the same time with Warshel, Levitt introduced coarse-grained models of the polypeptide chain for rapid simulation of protein folding. As a staff scientist at the MRC Laboratory of Molecular Biology, he worked with Tony Jack and laid the groundwork for modern approaches to crystallographic refinement.
Levitt’s work on protein modeling combining segments of known protein was adapted and extended by David Baker’s group at University of Washington, leading to the development of Rosetta, software widely used by the protein-modeling community. Many of the fundamental architectural principles of protein structure and protein-classification schemes taught in protein biochemistry courses stem from classic papers that Levitt authored in the 1970s along with Cyrus Chothia while he was at the MRC.
Barry Honig of Columbia University, a past recipient of the DeLano award, described Levitt as “an intellectual leader in computational structural biology.” It is notable that, apart from a an award in 1985 from the Federation of European Biochemical Societies, the DeLano Award in Computational Biosciences was the only prize awarded to Levitt before he was recognized in Stockholm.
Honig said of Levitt: “(He) has taught the practitioners new ways to apply computational tools to structural biology and that it is possible to have impact if theoretical rigor, algorithmic novelty and deep understanding of experimental reality are combined.”
Past recipients of the DeLano award include Helen Berman and Axel Brunger.
Levitt will receive the award at the ASBMB annual meeting in April in San Diego. His award lecture, tentatively titled “Solving large and difficult structures with less experimental data,” will be on Tuesday, April 29, in Room 6A of the San Diego Convention Center.

Preethi ChanderPreethi Chander ( earned a Ph.D. in structural biology from Purdue University and completed a postdoctoral fellowship at the National Institutes of Health. She works at the National Institutes of Health, Bethesda, MD, as a health science program administrator.

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