Summary of Research
Experimental techniques have brought us thousands of detailed -- but static -- structures of proteins and nucleic acids. Yet we know that biology functions dynamically. Many proteins, indeed, can be considered molecular machines, with motor proteins being only the most conspicuous examples.
The Zuckerman group develops algorithms and software to study dynamical biomolecular processes. These include algorithms for studying conformational transitions, binding, and equilibrium fluctuations, all based on the governing principles of statistical mechanics. The group pioneered rigorous multi-resolution algorithms for biomolecules, and has made important contributions to path sampling of slow processes. New algorithms are needed because traditional calculational approaches fail to capture the full range of motions and timescales intrinsic in biomoleules.
The Zuckerman group has also developed memory-intensive software designed to exploit modern hardware to the fullest, in contrast to traditional computations which use less than 1% of typically available RAM. The new "library based" software employs pre-calculated configurations of molecular fragments, keying on the modularity already present in bimolecules. The software permits tuning of a model's resolution -- for instance, to represent a binding site in full atomic detail with the remainder of the system modeled at coarser resolution, with full allosteric coupling throughout.
Daniel M. Zuckerman
Associate ProfessorWebsite: www.ccbb.pitt.edu/Faculty/zuckerman/index.html
Biomolecular motions, transitions and binding using molecular modeling and statistical mechanics-based calculations
Mamonov A, Lettieri S, Ding Y, Sarver J, Palli R, Cunningham T, Saxena S, Zuckerman DM. (2012). Tunable, mixed resolution modeling using library-based Monte Carlo and graphics processing units. Journal of Chemical Theory and Computation. 8: 2921-2929.
Lettieri S, Zuckerman DM. Accelerating molecular Monte Carlo simulations using distance and orientation-dependent energy tables: tuning from atomistic accuracy to smoothed "coarse-grained" models. J Comput Chem. 33: 268-75.
Zhang X, Bhatt D, Zuckerman DM. (2011). Automated sampling assessment for molecular simulations using the effective sample size. J Chem Theory Comput., 6: 3058-3057.
Zuckerman DM. (2011). Equilibrium sampling in biomolecular simulations. Annu Rev Biophys. 40: 41-62.
Mamonov AB, Zhang X, Zuckerman DM. (2011). Rapid sampling of all-atom peptides using a library-based polymer-growth approach. J Comput Chem. 32: 396-405.
Awards and Honors
Career Award, National Science Foundation, 2007-2011