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Weighted ensemble: Recent mathematical developments

https://doi.org/10.1063/5.0110873

Aristoff, D.; Copperman, J.; Simpson, G.; Webber, R. J.; Zuckerman, D. M. (January 2023, The Journal of Chemical Physics)

Weighted ensemble (WE) is an enhanced sampling method based on periodically replicating and pruning trajectories generated in parallel. WE has grown increasingly popular for computational biochemistry problems due, in part, to improved hardware and accessible software implementations. Algorithmic and analytical improvements have played an important role, and progress has accelerated in recent years. Here, we discuss and elaborate on the WE method from a mathematical perspective, highlighting recent results that enhance the computational efficiency. The mathematical theory reveals a new strategy for optimizing trajectory management that approaches the best possible variance while generalizing to systems of arbitrary dimension.
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Universality and Specificity in Protein Fluctuation Dynamics

https://doi.org/10.1103/PhysRevLett.119.158101

Copperman, J.; Dinpajooh, M.; Beyerle, E. R.; Guenza, M. G. (October 2017, Physical Review Letters)
Predicting protein dynamics from structural ensembles

https://doi.org/10.1063/1.4935575

Copperman, J.; Guenza, M. G. (December 2015, The Journal of Chemical Physics)

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Coarse-Grained Langevin Equation for Protein Dynamics: Global Anisotropy and a Mode Approach to Local Complexity

https://doi.org/10.1021/jp509473z

Copperman, J.; Guenza, M. G. (July 2015, The Journal of Physical Chemistry B)

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An analytical coarse-graining method which preserves the free energy, structural correlations, and thermodynamic state of polymer melts from the atomistic to the mesoscale

https://doi.org/10.1063/1.4875923

McCarty, J.; Clark, A. J.; Copperman, J.; Guenza, M. G. (May 2014, The Journal of Chemical Physics)

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Mode localization in the cooperative dynamics of protein recognition

https://doi.org/10.1063/1.4954506

Copperman, J.; Guenza, M. G. (July 2016, The Journal of Chemical Physics)
Modifications to toxic CUG RNAs induce structural stability, rescue mis-splicing in a myotonic dystrophy cell model and reduce toxicity in a myotonic dystrophy zebrafish model

https://doi.org/10.1093/nar/gku941

deLorimier, E.; Coonrod, L. A.; Copperman, J.; Taber, A.; Reister, E. E.; Sharma, K.; Todd, P. K.; Guenza, M. G.; Berglund, J. A. (November 2014, Nucleic Acids Research)

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