Sampling): Structures are often determined by an optimization of an
energy function. I introduced mean field approaches that modify the
target function and make it more accessible to global optimization.
We have applied these techniques to determine conformations of
short peptides and to refine low-resolution structures of proteins.
Ph.D. Hebrew Univ., Jerusalem, 1984
My research is in the field of Computational Molecular Biology. We
develop computer algorithms to study sequences, structures,
dynamics and function of proteins and apply these methods to a
variety of biological problems. Our techniques are implemented in a
single system MOIL available on the web.
Current research directions include: Mean field approaches for
global optimization and structure prediction (Locally
Development of folding potentials using linear programming: The
design of folding potentials relies on considerable human intuition
and many trials and errors. I developed an automated protocol that
"learns" from experience and failures and constantly improves the
quality of the current potential energy. The procedure is based on
linear programming and exact manipulation of large amount of
experimental information is possible. We used about 30 million
constraints to derive a new folding potential. We specifically design
energy functions for which threading and folding are performed
efficiently and accurately.
Extending the time scale of simulations. One of the striking
observations in dynamics of biological molecules is the extremely
large time scale they covered. Initiation by light absorption of
biochemical processes is very rapid (10-15 seconds), while protein
folding is slow (milliseconds to minutes). Current simulation
approaches (Molecular Dynamics MD) are restricted to
nanoseconds (10-9 seconds). I developed a stochastic path integral
formulation that provides a numerically stable trajectory for almost
an arbitrary time step. We apply the new algorithm to study
activation of proteins (the R->T transitions in hemoglobin
_microseconds) and to protein folding (folding of C peptide). The
method provides systematic approximation to the dynamics and is
more efficient than MD by orders of magnitude.
- Director of international
research group on protein
Sept. 99 - Jan. 00:
Hebrew Univ., Institute of
NIH committee on
Biomedicine in the Era of
Teraflop Computing", March
NIH study section, June 1999
- Long time dynamics of biomolecules. Department of Chemistry, Weizmann Institute,
Rehovot, Dec. 1998
Stochastic path approach
to folding kinetics. Keck
Center for Computational Biology, Rice
Univ., Houston, March 1999
- Design of folding potentials. MolDyn, Boston, Apr. 1999
- A stochastic path approach to
compute atomically detailed
trajectories: Application to the
folding of C peptide.
Phys. Chem. B 103 (1999),
899-911 (with J. Meller and R.
Fractal Analysis of
Potential Energy Landscapes.
Phys. Rev. E 59 (1999),2231-2243 (with D. A.
Thirumalai, and R. B.
Application of a stochastic path
integral to the computations of
an optimal path and ensembles
in Computational Science and
4 (P. Deuflhard, J.
Hermans, B. Leimukhlar, A. E.
Mark, S. Reich, R. D.
- Computational Molecular Dynamics: Challenges, Methods, Ideas. Springer
Verlag, Berlin Heidelberg,
(1999), 263-280 (with B. Roux
and R. Olender).
prediction of MHC class I
bound peptides: A study of
Folding and Design 3 (1998),
549-564 (with O.
Schueler-Furman and H. Margalit).
Dynamics of peptide folding.
Classical and Quantum
Dynamics in Condensed
Phase Simulations (B. Berne,
Ciccotti, and D. Coker,
Singapore, (1998), 423-444
(with D. Mohanty and C.