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 Professor