#### Modeling Biomolecular Systems

The Zuckerman group develops and applies computer simulation methods for studying biological systems. A primary focus is the deployment of sampling algorithms based on statistical physics that can be used to study (i) large-scale, potentially allosteric motions in proteins, (ii) signaling processes encoded in interaction networks, (iii) protein binding, and (iv) protein folding. Among the strategies used in the group are approaches that can yield super-linear parallel performance – estimation of observables using N processors that is more than N times faster than an estimate based on a single-processor simulation. Prof. Zuckerman also has a strong interest in biophysics education, which has led to a textbook and a new online book.

**Recent Work:**

Estimating First Passage Time Distributions from Weighted Ensemble Simulations and non-Markovian Analyses, Ernesto Suarez, Adam J. Pratt, Lillian T. Chong, and Daniel M. Zuckerman, Protein Science, Epub.

WESTPA: An interoperable, highly scalable software package for weighted ensemble simulation and analysis, Zwier, Matthew; Adelman, Joshua; Kaus, Joseph; Pratt, Adam; Wong, Kim; Rego, Nicholas; Suárez, Ernesto; Lettieri, Steven; Wang, David; Grabe, Michael; Zuckerman, Daniel; Chong, Lillian, *J. Chem. Theory Comput.*, 11, 800–809 (2015).

Structural Integrity of the Ribonuclease H domain in HIV-1 Reverse Transcriptase, Slack, Ryan L, Spiriti, Justin M, Ahn, Jinwoo. Parniak, Michael A Zuckerman, Daniel M., Ishima, Rieko, *Proteins: Structure, function, and bioinformatics*, Accepted.

“Tunable Coarse Graining for Monte Carlo Simulations of Proteins via Smoothed Energy tables: Direct and Exchange Simulations“, Justin Spiriti and Daniel M. Zuckerman, J. Chem. Theory Comput. J. Chem. Theory Comput., 10 (11), pp 5161–5177, 2014.

“Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories“, Ernesto Suárez, Steven Lettieri, Matthew C. Zwier, Carsen A. Stringer, Sundar Raman Subramanian, Lillian T. Chong, and Daniel M Zuckerman, J. Chem. Theory Comput., 10, 2658−2667, 2014. PMCID: PMC4168800.

“Efficient stochastic simulation of chemical kinetics networks using a weighted ensemble of trajectories,” Rory M. Donovan, Andrew J. Sedgewick, James R. Faeder, and Daniel M. Zuckerman *J. Chem. Phys.*, 139, 115105, 2013.

“Tunable, mixed-resolution modeling using library-based Monte Carlo and graphics processing units,” Mamonov, Artem; Lettieri, Steven; Ding, Ying; Sarver, Jessica; Palli, Rohith; Cunningham, Timothy; Saxena, Sunil; Zuckerman, Daniel, *J. Chem. Theory Comp.,* 8, 2921-2929, 2012.

“Accelerating molecular Monte Carlo simulations using distance and orientation dependent energy tables: tuning from atomistic accuracy to smoothed “coarse-grained” models,” Steven Lettieri and Daniel M. Zuckerman, *J. Comp. Chem.* 33:268-275, 2012.