Daniel M Zuckerman, Associate Professor      

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.



Weighted Ensemble. The group aids in the development of the WESTPA implementation of weighted ensemble algorithms, which have been shown to yield super-linear parallel performance in both molecular and systems-biology simulations.

Online biophysics book. The new online book, Physical Lens on the Cell, introduces biophysical principles underlying cellular processes to students from diverse backgrounds, from biology to physics. The hyperlinked contents and side-by-side reading panels enable students to follow unique pathways through the material and cross-reference concepts as needed. Although still a work in progress, the material is publicly available.

book cover The textbook is now in use at several universities.  Statistical Physics of Biomolecules: An Introduction, by D. M. Zuckerman (CRC Press, 2010) emphasizes the basics in an accessible way, along with key biophysics applications and some non-equilibrium topics.  Available at amazon.  Corrections are available here. Solution sets are available for instructors through the publisher, who should be contacted according to country: For US/Canada the contact is Susie Carlisle (Susie.Carlisle@taylorandfrancis.com). For the UK it is Tim Page (Tim.Page@tandf.co.uk). For all other international it is Joanne Blackford (Joanne.Blackford@tandf.co.uk).

Sampling Assessment. Scripts available to assess the effective sample size and de-correlation time of trajectories. There is also code for performing “weighted ensemble” path sampling and library-based simulations. See Software Page.

The Ensemble Protein Database (EPDB). The EPDB offers ensembles of structures, generated by computation, freely. Biology requires multiple conformations, after all. Feel free to send the group requests for particular proteins.

Mixed-resolution Monte Carlo (MRMC) for proteins and ligands. MRMC allows comptutational effort to be focused on a region where chemical detail is important, such as an all-atom binding region with a ligand - coupled to a flexible, coarse-grained model of the rest of the remainder of the protein. Demonstration Videos.

Recent Work:
  • "Tunable Coarse Graining for Monte Carlo Simulations of Proteins via Smoothed Energy tables: Direct and Exchange Simulations", Justin Spirit and Daniel M. Zuckerman, J. Chem. Theory Comput., Accepted.  PMCID in process. (Link)

  • "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. (Link)

  • "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) (Link)

  • "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)(Link)

  • "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). (Link)


University of Pittsburgh Department of Computational & Systems Biology