PLUMED-TUTORIALS

Bibliography

Here is the complete list of the published papers connected to the lessons deposited in PLUMED-TUTORIALS:

[ID:24.019] D. Sucerquia, C. Parra, P. Cossio, O. Lopez-Acevedo, Ab initio metadynamics determination of temperature-dependent free-energy landscape in ultrasmall silver clusters. The Journal of Chemical Physics. 156 (2022)

[ID:24.018] S. Pipolo, M. Salanne, G. Ferlat, S. Klotz, A. M. Saitta, F. Pietrucci, Navigating at Will on the Water Phase Diagram. Physical Review Letters. 119 (2017)

[ID:24.016] P. Febrer Martinez, V. Rizzi, S. Aureli, F. L. Gervasio, Host-Guest binding free energies à la carte: an automated OneOPES protocol (2024)

[ID:24.015] T. Giorgino, PYCV: a PLUMED 2 Module Enabling the Rapid Prototyping of Collective Variables in Python. Journal of Open Source Software. 4, 1773 (2019)

[ID:24.014] Z. F. Brotzakis, S. Zhang, M. Vendruscolo, AlphaFold Prediction of Structural Ensembles of Disordered Proteins (2023)

[ID:24.013] N. S. M. Herringer, S. Dasetty, D. Gandhi, J. Lee, A. L. Ferguson, Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables. Journal of Chemical Theory and Computation. 20, 178–198 (2023)

[ID:24.011] M. Bernetti, M. Masetti, M. Recanatini, R. E. Amaro, A. Cavalli, An Integrated Markov State Model and Path Metadynamics Approach To Characterize Drug Binding Processes. Journal of Chemical Theory and Computation. 15, 5689–5702 (2019)

[ID:24.010] G. Stirnemann, Recent Advances and Emerging Challenges in the Molecular Modeling of Mechanobiological Processes. The Journal of Physical Chemistry B. 126, 1365–1374 (2022)

[ID:24.008] J. Rydzewski, O. Valsson, Finding multiple reaction pathways of ligand unbinding. The Journal of Chemical Physics. 150 (2019)

[ID:24.007] J. F. Dama, G. Rotskoff, M. Parrinello, G. A. Voth, Transition-Tempered Metadynamics: Robust, Convergent Metadynamics via On-the-Fly Transition Barrier Estimation. Journal of Chemical Theory and Computation. 10, 3626–3633 (2014)

[ID:24.006] B. I. Armstrong, A. Silvestri, M. De La Pierre, P. Raiteri, J. D. Gale, Determining the Complete Stability of Calcite Kink Sites: Real vs Ideal. The Journal of Physical Chemistry C. 127, 13958–13968 (2023)

[ID:24.005] Y. Litman, V. Kapil, Y. M. Y. Feldman, D. Tisi, T. Begušić, K. Fidanyan, G. Fraux, J. Higer, M. Kellner, T. E. Li, E. S. Pós, E. Stocco, G. Trenins, B. Hirshberg, M. Rossi, M. Ceriotti, i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations. The Journal of Chemical Physics. 161 (2024)

[ID:24.004] R. Capelli, P. Carloni, M. Parrinello, Exhaustive Search of Ligand Binding Pathways via Volume-Based Metadynamics. The Journal of Physical Chemistry Letters. 10, 3495–3499 (2019)

[ID:24.002] M. Fortino, C. Cozza, M. Bonomi, A. Pietropaolo, Multi-replica biased sampling for photoswitchable π-conjugated polymers. The Journal of Chemical Physics. 154 (2021)

[ID:24.001] F. Ballabio, C. Paissoni, M. Bollati, M. de Rosa, R. Capelli, C. Camilloni, Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations. Journal of Chemical Theory and Computation. 19, 8401–8413 (2023)

[ID:22.017] S. E. Hoff, F. E. Thomasen, K. Lindorff-Larsen, M. Bonomi, Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference. PLOS Computational Biology. 20, e1012180 (2024)

[ID:22.015] M. J. Hartmann, Y. Singh, E. Vanden-Eijnden, G. M. Hocky, Infinite switch simulated tempering in force (FISST). The Journal of Chemical Physics. 152 (2020)

[ID:22.013] A. Arsiccio, P. Ganguly, J.-E. Shea, A Transfer Free Energy Based Implicit Solvent Model for Protein Simulations in Solvent Mixtures: Urea-Induced Denaturation as a Case Study. The Journal of Physical Chemistry B. 126, 4472–4482 (2022)

[ID:22.012] P. M. Piaggi, M. Parrinello, Calculation of phase diagrams in the multithermal-multibaric ensemble. The Journal of Chemical Physics. 150 (2019)

[ID:22.011] B. Pampel, O. Valsson, Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials. Journal of Chemical Theory and Computation. 18, 4127–4141 (2022)

[ID:22.010] G. Bussi, Hamiltonian replica exchange in GROMACS: a flexible implementation. Molecular Physics. 112, 379–384 (2013)

[ID:22.009] G. Díaz Leines, B. Ensing, Path Finding on High-Dimensional Free Energy Landscapes. Physical Review Letters. 109 (2012)

[ID:22.008] C. Perego, M. Salvalaglio, M. Parrinello, Molecular dynamics simulations of solutions at constant chemical potential. The Journal of Chemical Physics. 142 (2015)

[ID:22.007] S. Mehdi, D. Wang, S. Pant, P. Tiwary, Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck. Journal of Chemical Theory and Computation. 18, 3231–3238 (2022)

[ID:22.006] A. D. White, G. A. Voth, Efficient and Minimal Method to Bias Molecular Simulations with Experimental Data. Journal of Chemical Theory and Computation. 10, 3023–3030 (2014)

[ID:22.005] L. Bonati, E. Trizio, A. Rizzi, M. Parrinello, A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar. The Journal of Chemical Physics. 159 (2023)

[ID:22.003] M. Invernizzi, M. Parrinello, Rethinking Metadynamics: From Bias Potentials to Probability Distributions. The Journal of Physical Chemistry Letters. 11, 2731–2736 (2020)

[ID:22.002] D. Trapl, V. Spiwok, Analysis of the Results of Metadynamics Simulations by metadynminer and metadynminer3d. The R Journal. 14, 46–58 (2022)

[ID:22.001] S. Raniolo, V. Limongelli, Ligand binding free-energy calculations with funnel metadynamics. Nature Protocols. 15, 2837–2866 (2020)

[ID:21.006] D. Branduardi, F. L. Gervasio, M. Parrinello, From A to B in free energy space. The Journal of Chemical Physics. 126 (2007)

[ID:21.005] Y. Sugita, Y. Okamoto, Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters. 314, 141–151 (1999)

[ID:21.004] A. Laio, M. Parrinello, Escaping free-energy minima. Proceedings of the National Academy of Sciences. 99, 12562–12566 (2002)

[ID:21.003] G. M. Torrie, J. P. Valleau, Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. Journal of Computational Physics. 23, 187–199 (1977)

Further reading

Here you can find a list of additional papers containing practical PLUMED tutorials:

• Bussi, G. and Tribello, G. A. (2019) Analyzing and biasing simulations with PLUMED

• Barducci A., Pfaendtner J., Bonomi M. (2015) Tackling Sampling Challenges in Biomolecular Simulations. In: Kukol A. (eds) Molecular Modeling of Proteins. Methods in Molecular Biology (Methods and Protocols), vol 1215. Humana Press, New York, NY

• Bussi, G. and Branduardi, D. (2015). Free‐Energy Calculations with Metadynamics: Theory and Practice. In Reviews in Computational Chemistry Volume 28 (eds A. L. Parrill and K. B. Lipkowitz)