Michael Levitt
Academic Appointments
- Professor, Structural Biology
- Member, Bio-X
- Professor (By courtesy), Computer Science
Key Documents
Contact Information
- Academic Offices
Personal Information Email Tel (650) 276-0500
Professional Overview
Administrative Appointments
- Associate Chair, Department of Structural Biology (2005 - 2010)
- Chair, Department of Structural Biology (1993 - 2004)
Honors and Awards
- Member, American Academy of Arts & Sciences (2010)
- Member, The US National Academy of Science (2002)
- Fellow, The Royal Society (2001)
- Member, European Molecular Biology Organization. (1981)
Postdoctoral Advisees
Jenelle Bray, Yana Gofman, Leonid Pereyaslavets, Andrea Scaiewicz Podoly, Junjie Zhang
Internet Links
Scientific Focus
Current Research Interests
I pioneered of computational biology setting up the conceptual and theoretical framework for a field that I am still actively involved in at all levels. More specifically, I still write and maintain computer programs of all types including large simulation packages and molecular graphics interfaces. I have also developed a high-level of expertise in Perl scripting, as well as in the advanced use of the Office Suite of programs (Word, Excel and PowerPoint), which is more important and rare than it may seem. My research focuses on three different but inter-related areas of research. First, we are interested in predicting the folding of a polypeptide chain into a protein with a unique native-structure with particular emphasis on how the hydrophobic forces affect the pathway. We expect hydrophobic interactions to energetically favor structure that are more native-like. In this way, the same stabilizing interactions that exist in the final folded state the search tractable. Second we are interested in predicting protein structure from sequence without regard for the process of folding. Such prediction relies on the well-established paradigms that similar protein sequences imply similar three-dimensional structures. We have focused on the hardest problem in homology modeling: the refinement of a near-native structure to make it more precisely like the actual native structure of protein. We have also focused on how the general similarity of all protein sequences resulting from their evolution from common ancestor sequence affects the nature of the protein universe. Third, we are focusing on mesoscale modeling of large macromolecular complexes such as RNA polymerase and the mammalian chaperonin. In this work, done in close collaboration with experimentalists, we use new morphing strategies combined with normal mode analysis in torsion angle space to overcome problems caused by the size and complexity of these critical, biomedically important systems. All this work depends on the way a molecular structure is represented in terms of the force-field that allows calculation of the potential energy of the system. We employ a very wide variety of such energy functions that extend from knowledge-based statistical potentials for a single interaction center per residue to quantum-mechanical force-fields that include inductive effects as well as polarization.
Publications
- The crystal structures of the eukaryotic chaperonin CCT reveal its functional partitioning. Structure. 2013; (4): 540-9
- Application of DEN refinement and automated model building to a difficult case of molecular-replacement phasing: the structure of a putative succinyl-diaminopimelate desuccinylase from Corynebacterium glutamicum. Acta Crystallogr D Biol Crystallogr. 2012; (Pt 4): 391-403
- Comparative modeling and protein-like features of hydrophobic-polar models on a two-dimensional lattice. Proteins. 2012; (6): 1683-93
- Evaluating mixture models for building RNA knowledge-based potentials. J Bioinform Comput Biol. 2012; (2): 1241010
- Improving the accuracy of macromolecular structure refinement at 7 Å resolution. Structure. 2012; (6): 957-66
- KoBaMIN: a knowledge-based minimization web server for protein structure refinement. Nucleic Acids Res. 2012; (Web Server issue): W323-8
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