Daniel M Zuckerman, Associate Professor      

 

   Publications

   Teaching resources

   Weighted ensemble & conformational transitions

  • Dynamic reaction paths and rates through importance-sampled stochastic dynamics (D.M. Z. and T.B. Woolf) . J. Chem. Phys. 111,9475-9484 (1999)
  • Efficient dynamic importance sampling of rare events in one dimension (D.M. Z. and T .B. Woolf) Phys. Rev. E 63, 016702(2001).
  • Transition Events in Butane Simulations: Similarities Across Models (D.M. Z. and T.B. Woolf) J. Chem. Phys. 116,2586-2591(2002).
  • Simulation of an ensemble of conformational transitions in a united-residue model of calmodulin, D.M. Zuckerman, J. Phys. Chem. B 108:5127-5137 (2004).
  • Fast Simulation Protocol for Protein Structural Transitions: Modeling of the Relationship of Structure and Function, Arun K. Setty and D.M. Zuckerman in Proteins as Materials, edited by V.P. Conticello, A. Chilkoti, E. Atkins, and D.G. Lynn Mater. Res. Soc. Symp. Proc. 826E, Warrendale, PA , (2004).
  • Tools for Channels: Moving Towards Molecular Calculations of Gating and Permeation in Ion Channel Biophysics, Thomas B. Woolf, D. M. Zuckerman, N. Lu, H. Jang, Journal of Molecular Graphics and Modelling, 22:359-368 (2004).
  • Transition-event durations in one-dimensional activated processes, Bin W. Zhang, David Jasnow, and Daniel M. Zuckerman, (J. Chem. Phys. 126:074504 (2007).
  • Efficient and verified simulation of a path ensemble for conformational change in a united-residue model of calmodulin, Bin W. Zhang, David Jasnow, and Daniel M. Zuckerman,Proc. Nat. Acad. Sci. USA 104:18043-18048(2007).
  • The "weighted ensemble" path sampling method is statistically exact for a broad class of stochastic processes and binning procedures, Bin Zhang, David Jasnow, and Daniel M. Zuckerman, PMCID: PMC2830257 J. Chem. Phys., 132:054107 (2010).
  • Steady state via weighted ensemble path sampling, Divesh Bhatt, Bin Zhang, and Daniel M Zuckerman, J. Chem. Phys., 133:014110 (2010). PMCID: PMC2912933.
  • Heterogeneous path ensembles for conformational transitions in semi-atomistic models of adenylate kinase, Divesh Bhatt and Daniel M. Zuckerman, J. Chem. Theory Comp., 6:3527-3539 (2010). PMCID: PMC3108504
  • Beyond microscopic reversibility: Are observable non-equilibrium processes precisely reversible?, Divesh Bhatt and Daniel M. Zuckerman, J. Chem. Theory Comp., 7:2520-2527 (2011). PMCID: PMC3159166
  • Simulations of the alternating access mechanism of the sodium symporter Mhp1, Joshua L. Adelman, Amy L. Dale, Matthew C. Zwier, Divesh Bhatt, Lillian T. Chong, Daniel M. Zuckerman, Michael Grabe, Biophysical Journal, 101:2399-2407 (2011), PMCID: PMC3218348
  • 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).
  • 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.
  • Rapid Determination of Multiple Reaction Pathways in Molecular Systems: The Soft-Ratcheting Algorithm (D.M. Z. and T .B. Woolf) [www .arXiv .org:physics/0209098]

   Sampling: General issues and assessment

  • Ensemble-based convergence analysis of biomolecular trajectories, E. Lyman and D. M. Zuckerman, Biophysical J., 91:164-172 (2006).
  • A second look at canonical sampling of biomolecules using replica exchange simulation, E. Lyman and D. M. Zuckerman, J. Chem. Theory Comp., 2:1200-1202 (2006). PMCID: PMC2586297
  • On the structural convergence of biomolecular simulations by determination of the effective sample size, E. Lyman and D. M. Zuckerman, J. Phys. Chem. B. 111:12876-12882(2007). PMCID: PMC2538559
  • Demonstrated convergence of the equilibrium ensemble for a fast united-residue protein model, F. M. Ytreberg, S. Kh. Aroutiounian, D. M. Zuckerman, J. Chem. Theory Comp. 3:1860-1866 (2007).
  • Quantifying uncertainty and sampling quality in biomolecular simulations, Alan Grossfield and Daniel M. Zuckerman, Annual Reports in Computational Chemistry, 5:23-48 (2009). PMCID: PMC2865156
  • Automated sampling assessment for molecular simulations using the effective sample size, Xin Zhang, Divesh Bhatt, and Daniel M. Zuckerman, J. Chem. Theory Comp., 6:3048-3057 (2010). PMCID: PMC3017371
  • Equilibrium Sampling in Biomolecular Simulation, DM Zuckerman, Annual Review of Biophysics, 40:41-62 (2011).

   Sampling algorithms: Polymer-growth, Monte Carlo, etc.

  • Resolution Exchange Simulation, E. Lyman, F.M. Ytreberg and D. M. Zuckerman,  Phys. Rev. Lett. 96:028105 (2006).
  • Simple estimation of absolute free energies for biomolecules, F. M. Ytreberg and D. M. Zuckerman, J. Chem. Phys. 124:104015 (2006).
  • Resolution exchange simulation with incremental coarsening, E. Lyman and D. M. Zuckerman, J. Chem. Theory Comp. 2:656-666 (2006).
  • Annealed importance sampling of peptides, Edward Lyman and Daniel M. Zuckerman, J. Chem. Phys. 127:065101(2007).
  • A black-box re-weighting analysis can correct flawed simulation data, F. Marty Ytreberg and Daniel M. Zuckerman, Proc. Nat. Acad. Sci. USA 105:7982-7987 (2008). PMCID: PMC2786942.;(Supplementary Material PDF)
  • Resampling improves the efficiency of a "fast-switch" equilibrium sampling protocol, E. Lyman and D.M. Zuckerman, PMCID: PMC2671214 J. Chem. Phys., 130:081102 (2009).
  • Absolute free energies estimated by combining pre-calculated molecular fragment libraries, Xin Zhang, Artem Mamonov, and Daniel M. Zuckerman, J. Comp. Chem., 30:1680-1691 (2009). PMCID: PMC2783641
  • A general library-based Monte Carlo technique enables equilibrium sampling of semi-atomistic protein models, Mamonov, Artem; Bhatt, Divesh; Cashman, Derek; Ding, Ying; Zuckerman, Daniel M., J. Phys. Chem. B, 113: 10891-10904 (2009). PMCID: PMC2766542
  • Absolute free energies and equilibrium ensembles of dense fluids computed from a non-dynamic growth method, Divesh Bhatt and Daniel M. Zuckerman, J. Chem. Phys., 131:214110(2009). PMCID: PMC2802520
  • Efficient Equilibrium Sampling of All-Atom Peptides Using Library-Based Monte Carlo, Ying Ding, Artem Mamonov, Daniel M. Zuckerman, J. Phys. Chem. B, 114:5870-5877 (2010). PMCID: PMC2882875
  • Rapid sampling of all-atom peptides using a library-based polymer-growth approach, Artem B. Mamonov, Xin Zhang, and Daniel M. Zuckerman, J. Comp. Chem., 32:396-405 (2011). PMCID PMC3005036
  • Extending fragment-based free energy calculations with library Monte Carlo simulation: Annealing in interaction space, Steven Lettieri, Artem Mamonov, and Daniel M. Zuckerman, J. Comp. Chem., 32:1135-1143 (2011). PMCID PMC3390976
  • 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). PMCID: PMC3408236.
  • DM Zuckerman, "Principles and Practicalities of Canonical Mixed-Resolution Sampling of Biomolecules," in Coarse-Graining of Condensed Phase and Biomolecular Systems, edited by GA Voth (Taylor and Francis, 2008).
  • 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.

   Proteins: Large-scale motions and coarse-grained modeling

  • Resolution Exchange Simulation, E. Lyman, F.M. Ytreberg and D. M. Zuckerman,  Phys. Rev. Lett. 96:028105 (2006).
  • Resolution exchange simulation with incremental coarsening, E. Lyman and D. M. Zuckerman, J. Chem. Theory Comp. 2:656-666 (2006).
  • Demonstrated convergence of the equilibrium ensemble for a fast united-residue protein model, F. M. Ytreberg, S. Kh. Aroutiounian, D. M. Zuckerman, J. Chem. Theory Comp. 3:1860-1866 (2007).
  • A general library-based Monte Carlo technique enables equilibrium sampling of semi-atomistic protein models, Mamonov, Artem; Bhatt, Divesh; Cashman, Derek; Ding, Ying; Zuckerman, Daniel M., J. Phys. Chem. B, 113: 10891-10904 (2009). PMCID: PMC2766542
  • Thermal Motions of the E. Coli Glucose-Galactose Binding Protein Studied Using Well-Sampled Semi-Atomistic Simulations, Derek Cashman, Artem B. Mamonov, Divesh Bhatt, Daniel M. Zuckerman, Current Topics in Medicinal Chemistry, 11:211-220 (2011). PMCID in process (Ref: NIHMS209353)
  • Heterogeneous path ensembles for conformational transitions in semi-atomistic models of adenylate kinase, Divesh Bhatt and Daniel M. Zuckerman, J. Chem. Theory Comp., 6:3527-3539 (2010). PMCID: PMC3108504
  • 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) PMCID: PMC3496292.
  • DM Zuckerman, "Principles and Practicalities of Canonical Mixed-Resolution Sampling of Biomolecules," in Coarse-Graining of Condensed Phase and Biomolecular Systems, edited by GA Voth (Taylor and Francis, 2008).
  • 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.

   Miscellaneous simulation & beyond

  • Electrostatics of Membrane Systems -Complex, Heterogeneous Environments (T.B. Woolf, A. Grossfield, and D.M. Z.) in "Simulation and Theory of Electrostatic Interactions in Solution", Eds. L.R. Pratt and G. Hummer (American Inst. of Physics, 1999)
  • Hydrophobic matching mechanism investigated by molecular dynamics simulations (Petrache H.I., Zuckerman D.M., Sachs J.N, Killian J.A., Koeppe R.E., Woolf T.B.) Langmuir 18: 1340-1351 (2002).
  • Molecular dynamics simulations of ionic concentration gradients across model bilayers (Sachs J.N., Petrache H.I., Zuckerman D.M., Woolf T.B.) J. Chem. Phys. 118: 1957-1969 (2003).
  • Letter to the editor regarding postdoctoral welfare, Daniel M. Zuckerman, Science 286:411 (1999). DOI: 10.1126/science.286.5439.411c
  • All Pittsburgh students should learn computer programming, Daniel M. Zuckerman, Pittsburgh Post-Gazette Sept. 8, 2013.

   Free Energy Computations

   Ionic Systems and Chemical Association (with M. E. Fisher)

   Vesicles and Polymers (with R. Bruinsma)

  • Statistical Mechanics of Membrane Adhesion by Reversible Molecular Bonds (D. Zuckerman and R. Bruinsma), Phys. Rev. Lett. 74, 3900-3903 (1995).
  • Forces between Surfaces with Weakly End-Adsorbed Polymers (J.I. Martin, Z.-G. Wang, D. Z., R. Bruinsma, and P. Pincus) J. Phys. II (France) 7, 1111-1121 (1997).
  • Vesicle-Vesicle Adhesion by Mobile Lock-and-Key Molecules: Debye-Huckel Theory and Monte Carlo Simulation (D.M. Z. and R. F. Bruinsma) Phys. Rev. E 57,964-977 (1998).

   Chronological listing of all papers

  1. Statistical Mechanics of Membrane Adhesion by Reversible Molecular Bonds (D. Zuckerman and R. Bruinsma), Phys. Rev. Lett. 74, 3900-3903 (1995).
  2. Forces between Surfaces with Weakly End-Adsorbed Polymers (J.I. Martin, Z.-G. Wang, D. Z., R. Bruinsma, and P. Pincus) J. Phys. II (France) 7, 1111-1121 (1997).
  3. Critique of Primitive Model Electrolyte Theories (D.M. Z., M.E. Fisher, and B.P. Lee) Phys. Rev. E 56, 6569-6580 (1997).
  4. Vesicle-Vesicle Adhesion by Mobile Lock-and-Key Molecules: Debye-Huckel Theory and Monte Carlo Simulation (D.M. Z. and R. F. Bruinsma) Phys. Rev. E 57,964-977 (1998).
  5. Critique of Primitive Model Electrolyte Theories using Thermodynamic Bounds (M. E. Fisher, D.M. Z., and B. P. Lee) in "Strongly Coupled Coulomb Systems", Proc. Conf. held in Boston College, August 1997, Eds. G. J. Kalman, K. Blagoev and J. M. Rommel (Plenum Publ. Corp., 1998)
  6. Chemical Association via Exact Thermodynamic Formulations (M.E. Fisher and D.M. Z.) Chem. Phys. Lett. 293, 461-468 (1998).
  7. Exact Thermodynamic Formulation of Chemical Association (M.E. Fisher and D.M. Z.) J. Chem. Phys. 109, 7961-7981 (1998).
  8. Dynamic reaction paths and rates through importance-sampled stochastic dynamics (D.M. Z. and T.B. Woolf) . J. Chem. Phys. 111,9475-9484 (1999)
  9. Electrostatics of Membrane Systems -Complex, Heterogeneous Environments (T.B. Woolf, A. Grossfield, and D.M. Z.) in "Simulation and Theory of Electrostatic Interactions in Solution", Eds. L.R. Pratt and G. Hummer (American Inst. of Physics, 1999)
  10. Asymmetric Primitive Model Electrolytes: Debye-Huckel Theory, Criticality and Energy Bounds (D.M. Z., M.E. Fisher and S. Bekiranov) Phys. Rev. E 64,011206(2001).
  11. Efficient dynamic importance sampling of rare events in one dimension (D.M. Z. and T .B. Woolf) Phys. Rev. E 63, 016702(2001).
  12. Transition Events in Butane Simulations: Similarities Across Models (D.M. Z. and T.B. Woolf) J. Chem. Phys. 116,2586-2591(2002).
  13. Overcoming finite-sampling errors in fast-switching free energy estimates: Extrapolative analysis of a molecular system (D.M. Z. and T .B. Woolf) Chem. Phys. Lett. 351, 445-453 (2002).
  14. Hydrophobic matching mechanism investigated by molecular dynamics simulations (Petrache H.I., Zuckerman D.M., Sachs J.N, Killian J.A., Koeppe R.E., Woolf T.B.)Langmuir 18: 1340-1351 (2002).
  15. Theory of a Systematic Computational Error in Free Energy Differences (D.M. Z. and T.B. Woolf) Phys. Rev. Lett. 89 180602 (2002).
  16. Molecular dynamics simulations of ionic concentration gradients across model bilayers (Sachs J.N., Petrache H.I., Zuckerman D.M., Woolf T.B.) J. Chem. Phys. 118: 1957-1969 (2003).
  17. Systematic finite-sampling inaccuracy in free energy differences and other nonlinear quantities, D.M. Zuckerman and T. B. Woolf, Journal of Statistical Physics, 114:1303-1323 (2004).
  18. Simulation of an ensemble of conformational transitions in a united-residue model of calmodulin, D.M. Zuckerman, J. Phys. Chem. B 108:5127-5137 (2004).
  19. Fast Simulation Protocol for Protein Structural Transitions: Modeling of the Relationship of Structure and Function, Arun K. Setty and D.M. Zuckerman in Proteins as Materials, edited by V.P. Conticello, A. Chilkoti, E. Atkins, and D.G. Lynn Mater. Res. Soc. Symp. Proc. 826E, Warrendale, PA , (2004).
  20. Tools for Channels: Moving Towards Molecular Calculations of Gating and Permeation in Ion Channel Biophysics, Thomas B. Woolf, D. M. Zuckerman, N. Lu, H. Jang, Journal of Molecular Graphics and Modelling, 22:359-368 (2004).
  21. Single-ensemble nonequilibrium path-sampling estimates of free energy differences, F. M. Ytreberg and D. M. Zuckerman, J. Chem. Phys., 120:10876-10879 (2004).
  22. Efficient use of nonequilibrium measurement to estimate free energy differences for molecular systems, F. M. Ytreberg and D. M. Zuckerman, J. Computational Chemistry, 25:1749-1759 (2004)(Download Scripts To Perform Extrapolation Analysis).
  23. Peptide Conformational equilibria computed via a single-stage shifting protocol, F. M. Ytreberg and D. M. Zuckerman, J. Phys. Chem. B., 109:9096-9103 (2005).
  24. Resolution Exchange Simulation, E. Lyman, F.M. Ytreberg and D. M. Zuckerman,  Phys. Rev. Lett. 96:028105 (2006).
  25. Simple estimation of absolute free energies for biomolecules, F. M. Ytreberg and D. M. Zuckerman, J. Chem. Phys. 124:104015 (2006).
  26. Resolution exchange simulation with incremental coarsening, E. Lyman and D. M. Zuckerman, J. Chem. Theory Comp. 2:656-666 (2006).
  27. Ensemble-based convergence analysis of biomolecular trajectories, E. Lyman and D. M. Zuckerman, Biophysical J., 91:164-172 (2006).
  28. A second look at canonical sampling of biomolecules using replica exchange simulation, E. Lyman and D. M. Zuckerman, J. Chem. Theory Comp., 2:1200-1202 (2006). PMCID: PMC2586297
  29. Comparison of free energy methods for molecular systems, Ytreberg, F.M., R.H. Swendsen, and D.M. Zuckerman, J. Chem. Phys. 125: 184114-11 (2006).
  30. Transition-event durations in one-dimensional activated processes, Bin W. Zhang, David Jasnow, and Daniel M. Zuckerman, J. Chem. Phys. 126:074504 (2007).
  31. Annealed importance sampling of peptides, Edward Lyman and Daniel M. Zuckerman, J. Chem. Phys. 127:065101(2007).
  32. On the structural convergence of biomolecular simulations by determination of the effective sample size, E. Lyman and D. M. Zuckerman, J. Phys. Chem. B. 111:12876-12882(2007). PMCID: PMC2538559
  33. Demonstrated convergence of the equilibrium ensemble for a fast united-residue protein model, F. M. Ytreberg, S. Kh. Aroutiounian, D. M. Zuckerman, J. Chem. Theory Comp. 3:1860-1866 (2007).
  34. Efficient and verified simulation of a path ensemble for conformational change in a united-residue model of calmodulin, Bin W. Zhang, David Jasnow, and Daniel M. Zuckerman,Proc. Nat. Acad. Sci. USA 104:18043-18048(2007).
  35. A black-box re-weighting analysis can correct flawed simulation data, F. Marty Ytreberg and Daniel M. Zuckerman, Proc. Nat. Acad. Sci. USA 105:7982-7987 (2008). PMCID: PMC2786942.;(Supplementary Material PDF)
  36. Resampling improves the efficiency of a "fast-switch" equilibrium sampling protocol, E. Lyman and D.M. Zuckerman, PMCID: PMC2671214 J. Chem. Phys., 130:081102 (2009).
  37. Absolute free energies estimated by combining pre-calculated molecular fragment libraries, Xin Zhang, Artem Mamonov, and Daniel M. Zuckerman, J. Comp. Chem., 30:1680-1691 (2009). PMCID: PMC2783641
  38. A general library-based Monte Carlo technique enables equilibrium sampling of semi-atomistic protein models, Mamonov, Artem; Bhatt, Divesh; Cashman, Derek; Ding, Ying; Zuckerman, Daniel M., J. Phys. Chem. B, 113: 10891-10904 (2009). PMCID: PMC2766542
  39. Absolute free energies and equilibrium ensembles of dense fluids computed from a non-dynamic growth method, Divesh Bhatt and Daniel M. Zuckerman, J. Chem. Phys., 131:214110(2009). PMCID: PMC2802520
  40. The "weighted ensemble" path sampling method is statistically exact for a broad class of stochastic processes and binning procedures, Bin Zhang, David Jasnow, and Daniel M. Zuckerman, PMCID: PMC2830257 J. Chem. Phys., 132:054107 (2010).
  41. Efficient Equilibrium Sampling of All-Atom Peptides Using Library-Based Monte Carlo, Ying Ding, Artem Mamonov, Daniel M. Zuckerman, J. Phys. Chem. B, 114:5870-5877 (2010). PMCID: PMC2882875
  42. Thermal Motions of the E. Coli Glucose-Galactose Binding Protein Studied Using Well-Sampled Semi-Atomistic Simulations, Derek Cashman, Artem B. Mamonov, Divesh Bhatt, Daniel M. Zuckerman, Current Topics in Medicinal Chemistry, 11:211-220 (2011). PMCID in process (Ref: NIHMS209353)
  43. Steady state via weighted ensemble path sampling, Divesh Bhatt, Bin Zhang, and Daniel M Zuckerman, J. Chem. Phys., 133:014110 (2010). PMCID: PMC2912933.
  44. Rapid sampling of all-atom peptides using a library-based polymer-growth approach, Artem B. Mamonov, Xin Zhang, and Daniel M. Zuckerman, J. Comp. Chem., 32:396-405 (2011). PMCID PMC3005036
  45. Automated sampling assessment for molecular simulations using the effective sample size, Xin Zhang, Divesh Bhatt, and Daniel M. Zuckerman, J. Chem. Theory Comp., 6:3048-3057 (2010). PMCID: PMC3017371
  46. Heterogeneous path ensembles for conformational transitions in semi-atomistic models of adenylate kinase, Divesh Bhatt and Daniel M. Zuckerman, J. Chem. Theory Comp., 6:3527-3539 (2010). PMCID: PMC3108504
  47. Extending fragment-based free energy calculations with library Monte Carlo simulation: Annealing in interaction space, Steven Lettieri, Artem Mamonov, and Daniel M. Zuckerman, J. Comp. Chem., 32:1135-1143 (2011). PMCID PMC3390976
  48. Beyond microscopic reversibility: Are observable non-equilibrium processes precisely reversible?, Divesh Bhatt and Daniel M. Zuckerman, J. Chem. Theory Comp., 7:2520-2527 (2011). PMCID: PMC3159166
  49. Simulations of the alternating access mechanism of the sodium symporter Mhp1, Joshua L. Adelman, Amy L. Dale, Matthew C. Zwier, Divesh Bhatt, Lillian T. Chong, Daniel M. Zuckerman, Michael Grabe, Biophysical Journal, 101:2399-2407 (2011), PMCID: PMC3218348
  50. 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). PMCID: PMC3408236.
  51. 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) PMCID: PMC3496292.
  52. 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).
  53. 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.
  54. 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.