Self-Learning approaches
Publications based on the development of self-learning algorithms for enhanced sampling and/or to couple simulations and experiments
Publications based on the development of self-learning algorithms for enhanced sampling and/or to couple simulations and experiments
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Methods to couple simulations and experiments
Lee PS, Bradshaw R, MARINELLI F, Kihn K, Smith A, Wintrode PL, Deredge DJ, Faraldo-Gómez JD, Forrest LR (2021). Interpreting Hydrogen-Deuterium Exchange Experiments with Molecular Simulations: Tutorials and Applications of the HDXer Ensemble Reweighting Software. LiveCoMS, vol. 3 (1), 1521. Link
Bradshaw RT, MARINELLI F#, Faraldo-Gómez JD , Forrest LR (2020). Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles. BIOPHYS J. vol. 118; p. 1649-1664 . Link
MARINELLI F*, Fiorin G (2019). Structural characterization of biomolecules through atomistic simulations guided by DEER measurements. STRUCTURE vol. 27; p. 359-370. Link
Methods for enhanced sampling or to couple simulations and experiments
Hustedt EJ, MARINELLI F#, Stein RA, Faraldo-Gómez JD, Mchaourab HS (2018). Confidence Analysis of DEER Data and its Structural Interpretation with Ensemble-Biased Metadynamics. BIOPHYS J. vol. 115; p. 1200-1216. Link
MARINELLI F*, D, Faraldo-Gómez JD (2015). Ensemble-Biased Metadynamics: A Molecular Simulation Method to Sample Experimental Distributions. BIOPHYS J. vol. 108; p. 2779–2782. doi:10.1016/j.bpj.2015.05.024. Link
Enhanced sampling methods