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keywords = {molecular dynamics simulations, analysis, proteins, object-oriented design, software, membrane systems, Python programming language},
doi = {10.1002/jcc.21787},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.21787},
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@Article{Salari2018,
  author       = {Salari, Reza and Joseph, Thomas and Lohia, Ruchi and H\'enin, J\'er\^ome and Brannigan, Grace},
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  title        = {A Streamlined, General Approach for Computing Ligand Binding Free Energies and Its Application to {GPCR}-Bound Cholesterol.},
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  pages        = {6560--6573},
  volume       = {14},
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@ARTICLE{Vant2020,
	author = {Vant, John W. and Sarkar, Daipayan and Fiorin, Giacomo and Skeel, Robert and Vermaas, Josh V. and Singharoy, Abhishek},
	title = {Data-guided Multi-Map variables for ensemble refinement of molecular movies},
	elocation-id = {2020.07.23.217794},
	year = {2020},
	doi = {10.1101/2020.07.23.217794},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the 3-dimensional electron density of a protein, as it would be deter-mined by cryo-EM or X-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work uses the recently developed Multi-Map methodology to monitor concerted move-ments within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along reaction coordinates derived from electron densities can serve to induce conformational transitions between known intermediates. The simulated path-ways appear reversible, with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein DataBank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution (\~{}5 {\r A}) density information cou-pled to comparatively large structural transitions. Practical considerations for generating transitions with multiple intermediate atomic density distributions are also discussed.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2020/07/24/2020.07.23.217794},
	eprint = {https://www.biorxiv.org/content/early/2020/07/24/2020.07.23.217794.full.pdf},
	journal = {bioRxiv}
}

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  title = {Gaussian {Accelerated} {Molecular} {Dynamics}: {Unconstrained} {Enhanced} {Sampling} and {Free} {Energy} {Calculation}},
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}
