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| Sergey Buldyrev, Yeshiva University |
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Born: 1954, St. Petersburg, Russia.
Education
Saint-Petersburg State University, Russia - Polymer Physics PhD. 1988Saint-Petersburg State University, Russia - Mathematical Physics M.S., 1977
Appointments/Affiliations
2004-present: Professor, Department of Physics,Yeshiva University, New York, USA
2002: Visiting Professor, Dipartimento di Fisica, Universita di RomaLa Sapienza, Rome, Italy.
1990-2004: Research Associate, Center for Polymer Studies and PhysicsDepartment,Boston University, Boston, USA
1984-1989: Assistant Lecturer, Departmentof Physics, Saint-Petersburg State University, Saint-Petersburg,Russia
1977-1984: Junior Research Fellow, Institute of Physics,Saint-Petersburg State University, Saint-Petersburg, Russia
Research in interests: application of computer simulations and statisticalphysics to:
1. Transformations in Liquids. 2. Transport in Disordered Media 3. Non-Linear Surface Growth 4. Polymer Physics 5. Protein Folding and Aggregation 6. Alzheimer Research 7. Physics of Lungs 8. Statistical Properties of DNA sequences and Molecular Evolution 9. Behavior of ecosystems 10. Economics
Author of more than 100 papers in refereed journals.
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Application of Discrete Molecular Dynamics to Protein Folding and Aggregation
Sergey Buldyrev, Yeshiva University
With rapid increase in computer speed and memory, simulations of proteins and other biological polymers begin to gain predictive power. However, traditional molecular dynamics methods based on explicit solvent and accurate force field models have still to gain several orders of magnitude in speed in order to simulate a folding trajectory of a moderate size protein or an aggregation process of a large number of peptides. Under these circumstances, simplified models which capture the essence of the process under study may shed some light on the problem in question. One of these simplified methods is discrete molecular dynamics (DMD), which replaces the interaction potentials between atoms and covalent bonds by discontinuous step functions. This simplification as well as coarse graining of the model (replacing groups of atoms by one effective bead) and replacing the effect of solvent by varying the strength of inter-bead interactions can speed up simulations sufficiently to generate many folding-unfolding events as well as to track the aggregation of many peptides. This speed up is gained mainly due to the ballistic motion of secondary structures of the protein or the individual peptides which is a characteristic feature of the DMD method. In my presentation, I will review successes and failures of the DMD method in protein folding and aggregation.
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