gamd_namd_refs.bib

@book{wang_gaussian_2021,
	title = {Gaussian accelerated molecular dynamics: {Principles} and applications},
	volume = {11},
	isbn = {0000000332576},
	abstract = {Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., “Gaussian approximation”). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica-exchange GaMD (rex-GaMD) and replica-exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new “selective GaMD” algorithms including the Ligand GaMD (LiGaMD) and Peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein–protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design. This article is categorized under: Structure and Mechanism {\textgreater} Computational Biochemistry and Biophysics Molecular and Statistical Mechanics {\textgreater} Molecular Dynamics and Monte-Carlo Methods Molecular and Statistical Mechanics {\textgreater} Free Energy Methods.},
	author = {Wang, Jinan and Arantes, Pablo R. and Bhattarai, Apurba and Hsu, Rohaine V. and Pawnikar, Shristi and Huang, Yu ming M. and Palermo, Giulia and Miao, Yinglong},
	year = {2021},
	doi = {10.1002/wcms.1521},
        issue = {5},
        journal = {Wiley Interdiscip. Rev. Comput. Mol. Sci.}
}

@article{celerse_efficient_2022,
	title = {An {Efficient} {Gaussian}-{Accelerated} {Molecular} {Dynamics} ({GaMD}) {Multilevel} {Enhanced} {Sampling} {Strategy}: {Application} to {Polarizable} {Force} {Fields} {Simulations} of {Large} {Biological} {Systems}},
	volume = {18},
	issn = {15499626},
	doi = {10.1021/acs.jctc.1c01024},
	abstract = {We introduce a novel multilevel enhanced sampling strategy grounded on Gaussian-accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water"mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups, thanks to the use of fast multiple-time step integrators. To further reduce the time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD-US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2-CD58 system (168 000 atoms), allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added, enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling-US-GaMD (ASUS-GaMD) scheme. The highly parallel ASUS-GaMD setup decreases time to convergence by, respectively, 10 and 20 times, compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water"enhanced sampling approaches increasing the applicability of polarizable force fields to large-scale simulations of biological systems.},
	number = {2},
	journal = {J. Chem. Theory Comput.},
	author = {Célerse, Frédéric and Inizan, Théo Jaffrelot and Lagardère, Louis and Adjoua, Olivier and Monmarché, Pierre and Miao, Yinglong and Derat, Etienne and Piquemal, Jean Philip},
	year = {2022},
	pmid = {35080892},
	pages = {968--977},
}

@article{miao_gaussian_2015,
    author = {Miao, Yinglong and Feher, Victoria A. and McCammon, J. Andrew},
    title = {Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation},
    journal = {J. Chem. Theory Comput.},
    volume = {11},
    number = {8},
    pages = {3584-3595},
    year = {2015},
    doi = {10.1021/acs.jctc.5b00436},
    abstract = {A Gaussian accelerated molecular dynamics (GaMD) approach for simultaneous enhanced sampling and free energy calculation of biomolecules is presented. By constructing a boost potential that follows Gaussian distribution, accurate reweighting of the GaMD simulations is achieved using cumulant expansion to the second order. Here, GaMD is demonstrated on three biomolecular model systems: alanine dipeptide, chignolin folding, and ligand binding to the T4-lysozyme. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of these biomolecules. Furthermore, the free energy profiles obtained from reweighting of the GaMD simulations allow us to identify distinct low-energy states of the biomolecules and characterize the protein-folding and ligand-binding pathways quantitatively.},
    note ={PMID: 26300708},
    URL = {https://doi.org/10.1021/acs.jctc.5b00436},
    eprint = {https://doi.org/10.1021/acs.jctc.5b00436}
}

@article{brooks_charmm_2009,
	title = {{CHARMM}: {The} biomolecular simulation program},
	volume = {30},
	issn = {0192-8651, 1096-987X},
	shorttitle = {{CHARMM}},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/jcc.21287},
	doi = {10.1002/jcc.21287},
	abstract = {CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. In addition, the CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This paper provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM paper in 1983.},
	language = {en},
	number = {10},
	urldate = {2023-12-08},
	journal = {J. Comput. Chem.},
	author = {Brooks, B. R. and Brooks, C. L. and Mackerell, A. D. and Nilsson, L. and Petrella, R. J. and Roux, B. and Won, Y. and Archontis, G. and Bartels, C. and Boresch, S. and Caflisch, A. and Caves, L. and Cui, Q. and Dinner, A. R. and Feig, M. and Fischer, S. and Gao, J. and Hodoscek, M. and Im, W. and Kuczera, K. and Lazaridis, T. and Ma, J. and Ovchinnikov, V. and Paci, E. and Pastor, R. W. and Post, C. B. and Pu, J. Z. and Schaefer, M. and Tidor, B. and Venable, R. M. and Woodcock, H. L. and Wu, X. and Yang, W. and York, D. M. and Karplus, M.},
	month = jul,
	year = {2009},
	pages = {1545--1614},
}

@article{pang_gaussian_2017,
	title = {Gaussian {Accelerated} {Molecular} {Dynamics} in {NAMD}},
	volume = {13},
	issn = {1549-9618},
	url = {https://doi.org/10.1021/acs.jctc.6b00931},
	doi = {10.1021/acs.jctc.6b00931},
	abstract = {Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that provides efficient free energy calculations of biomolecules. Like the previous accelerated molecular dynamics (aMD), GaMD allows for “unconstrained” enhanced sampling without the need to set predefined collective variables and so is useful for studying complex biomolecular conformational changes such as protein folding and ligand binding. Furthermore, because the boost potential is constructed using a harmonic function that follows Gaussian distribution in GaMD, cumulant expansion to the second order can be applied to recover the original free energy profiles of proteins and other large biomolecules, which solves a long-standing energetic reweighting problem of the previous aMD method. Taken together, GaMD offers major advantages for both unconstrained enhanced sampling and free energy calculations of large biomolecules. Here, we have implemented GaMD in the NAMD package on top of the existing aMD feature and validated it on three model systems: alanine dipeptide, the chignolin fast-folding protein, and the M3 muscarinic G protein-coupled receptor (GPCR). For alanine dipeptide, while conventional molecular dynamics (cMD) simulations performed for 30 ns are poorly converged, GaMD simulations of the same length yield free energy profiles that agree quantitatively with those of 1000 ns cMD simulation. Further GaMD simulations have captured folding of the chignolin and binding of the acetylcholine (ACh) endogenous agonist to the M3 muscarinic receptor. The reweighted free energy profiles are used to characterize the protein folding and ligand binding pathways quantitatively. GaMD implemented in the scalable NAMD is widely applicable to enhanced sampling and free energy calculations of large biomolecules.},
	number = {1},
	urldate = {2023-12-18},
	journal = {J. Chem. Theory Comput.},
	author = {Pang, Yui Tik and Miao, Yinglong and Wang, Yi and McCammon, J. Andrew},
	month = jan,
	year = {2017},
	pages = {9--19},
}

@article{miao_improved_2014,
	title = {Improved {Reweighting} of {Accelerated} {Molecular} {Dynamics} {Simulations} for {Free} {Energy} {Calculation}},
	volume = {10},
	issn = {1549-9618},
	url = {https://doi.org/10.1021/ct500090q},
	doi = {10.1021/ct500090q},
	abstract = {Accelerated molecular dynamics (aMD) simulations greatly improve the efficiency of conventional molecular dynamics (cMD) for sampling biomolecular conformations, but they require proper reweighting for free energy calculation. In this work, we systematically compare the accuracy of different reweighting algorithms including the exponential average, Maclaurin series, and cumulant expansion on three model systems: alanine dipeptide, chignolin, and Trp-cage. Exponential average reweighting can recover the original free energy profiles easily only when the distribution of the boost potential is narrow (e.g., the range ≤20kBT) as found in dihedral-boost aMD simulation of alanine dipeptide. In dual-boost aMD simulations of the studied systems, exponential average generally leads to high energetic fluctuations, largely due to the fact that the Boltzmann reweighting factors are dominated by a very few high boost potential frames. In comparison, reweighting based on Maclaurin series expansion (equivalent to cumulant expansion on the first order) greatly suppresses the energetic noise but often gives incorrect energy minimum positions and significant errors at the energy barriers (∼2–3kBT). Finally, reweighting using cumulant expansion to the second order is able to recover the most accurate free energy profiles within statistical errors of ∼kBT, particularly when the distribution of the boost potential exhibits low anharmonicity (i.e., near-Gaussian distribution), and should be of wide applicability. A toolkit of Python scripts for aMD reweighting “PyReweighting” is distributed free of charge at http://mccammon.ucsd.edu/computing/amdReweighting/.},
	number = {7},
	urldate = {2024-09-20},
	journal = {J. Chem. Theory Comput.},
	author = {Miao, Yinglong and Sinko, William and Pierce, Levi and Bucher, Denis and Walker, Ross C. and McCammon, J. Andrew},
	month = jul,
	year = {2014},
	note = {Publisher: American Chemical Society},
	pages = {2677--2689},
}

@article{ahn_gaussian-accelerated_2021,
	title = {Gaussian-{Accelerated} {Molecular} {Dynamics} with the {Weighted} {Ensemble} {Method}: {A} {Hybrid} {Method} {Improves} {Thermodynamic} and {Kinetic} {Sampling}},
	volume = {17},
	issn = {1549-9626},
	shorttitle = {Gaussian-{Accelerated} {Molecular} {Dynamics} with the {Weighted} {Ensemble} {Method}},
	doi = {10.1021/acs.jctc.1c00770},
	abstract = {Gaussian-accelerated molecular dynamics (GaMD) is a well-established enhanced sampling method for molecular dynamics simulations that effectively samples the potential energy landscape of the system by adding a boost potential, which smoothens the surface and lowers the energy barriers between states. GaMD is unable to give time-dependent properties such as kinetics directly. On the other hand, the weighted ensemble (WE) method can efficiently sample transitions between states with its many weighted trajectories, which directly yield rates and pathways. However, convergence to equilibrium conditions remains a challenge for the WE method. Hence, we have developed a hybrid method that combines the two methods, wherein GaMD is first used to sample the potential energy landscape of the system and WE is subsequently used to further sample the potential energy landscape and kinetic properties of interest. We show that the hybrid method can sample both thermodynamic and kinetic properties more accurately and quickly compared to using either method alone.},
	language = {eng},
	number = {12},
	journal = {J. Chem. Theory Comput.},
	author = {Ahn, Surl-Hee and Ojha, Anupam A. and Amaro, Rommie E. and McCammon, J. Andrew},
	month = dec,
	year = {2021},
	pmid = {34844409},
	pmcid = {PMC8983023},
	pages = {7938--7951},
}

@article{shen_statistical_2008,
	title = {A statistical analysis of the precision of reweighting-based simulations},
	volume = {129},
	issn = {0021-9606},
	url = {https://doi.org/10.1063/1.2944250},
	doi = {10.1063/1.2944250},
	abstract = {Various advanced simulation techniques, which are used to sample the statistical ensemble of systems with complex Hamiltonians, such as those displayed in condensed matters and biomolecular systems, rely heavily on successfully reweighting the sampled configurations. The sampled points of a system from an elevated thermal environment or on a modified Hamiltonian are reused with different statistical weights to evaluate its properties at the initial desired temperature or of the original Hamiltonian. Often, the decrease of accuracy induced by this procedure is ignored and the final results can be far from what is expected. We have addressed the reasons behind such a phenomenon and have provided a quantitative method to estimate the number of sampled points required in the crucial step of reweighting of these advanced simulation methods. We also provided examples from temperature histogram reweighting and accelerated molecular dynamics reweighting to illustrate this idea, which can be generalized to the dynamic reweighting as well. The study shows that this analysis may provide a priori guidance for the strategy of setting up the parameters of advanced simulations before a lengthy one is carried out. The method can therefore provide insights for optimizing the parameters for high accuracy simulations with finite amount of computational resources.},
	number = {3},
	urldate = {2024-09-20},
	journal = {J. Chem. Phys.},
	author = {Shen, Tongye and Hamelberg, Donald},
	month = jul,
	year = {2008},
	pages = {034103},
}

@article{hamelberg_sampling_2007,
	title = {Sampling of slow diffusive conformational transitions with accelerated molecular dynamics},
	volume = {127},
	issn = {0021-9606},
	url = {https://doi.org/10.1063/1.2789432},
	doi = {10.1063/1.2789432},
	abstract = {Slow diffusive conformational transitions play key functional roles in biomolecular systems. Our ability to sample these motions with molecular dynamics simulation in explicit solvent is limited by the slow diffusion of the solvent molecules around the biomolecules. Previously, we proposed an accelerated molecular dynamics method that has been shown to efficiently sample the torsional degrees of freedom of biomolecules beyond the millisecond timescale. However, in our previous approach, large-amplitude displacements of biomolecules are still slowed by the diffusion of the solvent. Here we present a unified approach of efficiently sampling both the torsional degrees of freedom and the diffusive motions concurrently. We show that this approach samples the configuration space more efficiently than normal molecular dynamics and that ensemble averages converge faster to the correct values.},
	number = {15},
	urldate = {2024-09-28},
	journal = {J. Chem. Phys.},
	author = {Hamelberg, Donald and de Oliveira, César Augusto F. and McCammon, J. Andrew},
	month = oct,
	year = {2007},
	pages = {155102},
	file = {Snapshot:C\:\\Users\\hmich\\Zotero\\storage\\FJIJNI76\\915164.html:text/html},
}

@article{wang_implementation_2011,
	title = {Implementation of {Accelerated} {Molecular} {Dynamics} in {NAMD}},
	volume = {4},
	issn = {1749-4699},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3115733/},
	doi = {10.1088/1749-4699/4/1/015002},
	abstract = {Accelerated molecular dynamics (aMD) is an enhanced-sampling method that improves the conformational space sampling by reducing energy barriers separating different states of a system. Here we present the implementation of aMD in the parallel simulation program NAMD. We show that aMD simulations performed with NAMD have only a small overhead compared with classical MD simulations. Through example applications to the alanine dipeptide, we discuss the choice of acceleration parameters, the interpretation of aMD results, as well as the advantages and limitations of the aMD method.},
	number = {1},
	urldate = {2024-09-28},
	journal = {Comput. Sci. Discov.},
	author = {Wang, Yi and Harrison, Christopher B. and Schulten, Klaus and McCammon, J. Andrew},
	year = {2011},
	pmid = {21686063},
	pmcid = {PMC3115733},
	pages = {015002},
}

@article{copeland_gaussian_2022,
	title = {Gaussian {Accelerated} {Molecular} {Dynamics} in {OpenMM}},
	volume = {126},
	copyright = {https://doi.org/10.15223/policy-029},
	issn = {1520-6106, 1520-5207},
	url = {https://pubs.acs.org/doi/10.1021/acs.jpcb.2c03765},
	doi = {10.1021/acs.jpcb.2c03765},
	language = {en},
	number = {31},
	urldate = {2024-09-28},
	journal = {J. Phys. Chem. B},
	author = {Copeland, Matthew M. and Do, Hung N. and Votapka, Lane and Joshi, Keya and Wang, Jinan and Amaro, Rommie E. and Miao, Yinglong},
	month = aug,
	year = {2022},
	pages = {5810--5820},
}

@article{huang_replica_2018,
	title = {Replica {Exchange} {Gaussian} {Accelerated} {Molecular} {Dynamics}: {Improved} {Enhanced} {Sampling} and {Free} {Energy} {Calculation}},
	volume = {14},
	issn = {1549-9618},
	shorttitle = {Replica {Exchange} {Gaussian} {Accelerated} {Molecular} {Dynamics}},
	url = {https://doi.org/10.1021/acs.jctc.7b01226},
	doi = {10.1021/acs.jctc.7b01226},
	abstract = {Through adding a harmonic boost potential to smooth the system potential energy surface, Gaussian accelerated molecular dynamics (GaMD) provides enhanced sampling and free energy calculation of biomolecules without the need of predefined reaction coordinates. This work continues to improve the acceleration power and energy reweighting of the GaMD by combining the GaMD with replica exchange algorithms. Two versions of replica exchange GaMD (rex-GaMD) are presented: force constant rex-GaMD and threshold energy rex-GaMD. During simulations of force constant rex-GaMD, the boost potential can be exchanged between replicas of different harmonic force constants with fixed threshold energy. However, the algorithm of threshold energy rex-GaMD tends to switch the threshold energy between lower and upper bounds for generating different levels of boost potential. Testing simulations on three model systems, including the alanine dipeptide, chignolin, and HIV protease, demonstrate that through continuous exchanges of the boost potential, the rex-GaMD simulations not only enhance the conformational transitions of the systems but also narrow down the distribution width of the applied boost potential for accurate energetic reweighting to recover biomolecular free energy profiles.},
	number = {4},
	urldate = {2024-09-28},
	journal = {J. Chem. Theory Comput.},
	author = {Huang, Yu-ming M. and McCammon, J. Andrew and Miao, Yinglong},
	month = apr,
	year = {2018},
	note = {Publisher: American Chemical Society},
	pages = {1853--1864},
}

@article{tian_ff19sb_2020,
	title = {{ff19SB}: {Amino}-{Acid}-{Specific} {Protein} {Backbone} {Parameters} {Trained} against {Quantum} {Mechanics} {Energy} {Surfaces} in {Solution}},
	volume = {16},
	issn = {1549-9618},
	shorttitle = {{ff19SB}},
	url = {https://doi.org/10.1021/acs.jctc.9b00591},
	doi = {10.1021/acs.jctc.9b00591},
	abstract = {Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.},
	number = {1},
	urldate = {2024-10-08},
	journal = {J. Chem. Theory Comput.},
	author = {Tian, Chuan and Kasavajhala, Koushik and Belfon, Kellon A. A. and Raguette, Lauren and Huang, He and Migues, Angela N. and Bickel, John and Wang, Yuzhang and Pincay, Jorge and Wu, Qin and Simmerling, Carlos},
	month = jan,
	year = {2020},
	note = {Publisher: American Chemical Society},
	pages = {528--552},
}

@article{huang_charmm36m_2017,
	title = {{CHARMM36m}: an improved force field for folded and intrinsically disordered proteins},
	volume = {14},
	issn = {1548-7091, 1548-7105},
	shorttitle = {{CHARMM36m}},
	url = {https://www.nature.com/articles/nmeth.4067},
	doi = {10.1038/nmeth.4067},
	language = {en},
	number = {1},
	urldate = {2024-10-08},
	journal = {Nat. Methods},
	author = {Huang, Jing and Rauscher, Sarah and Nawrocki, Grzegorz and Ran, Ting and Feig, Michael and De Groot, Bert L and Grubmüller, Helmut and MacKerell, Alexander D},
	month = jan,
	year = {2017},
	pages = {71--73},
}

@article{lin_further_2020,
	title = {Further {Optimization} and {Validation} of the {Classical} {Drude} {Polarizable} {Protein} {Force} {Field}},
	volume = {16},
	copyright = {https://doi.org/10.15223/policy-029},
	issn = {1549-9618, 1549-9626},
	url = {https://pubs.acs.org/doi/10.1021/acs.jctc.0c00057},
	doi = {10.1021/acs.jctc.0c00057},
	language = {en},
	number = {5},
	urldate = {2024-10-08},
	journal = {J. Chem. Theory Comput.},
	author = {Lin, Fang-Yu and Huang, Jing and Pandey, Poonam and Rupakheti, Chetan and Li, Jing and Roux, Benoı̂t and MacKerell, Alexander D.},
	month = may,
	year = {2020},
	pages = {3221--3239},
}

@incollection{lemkul_preparing_2021,
	address = {New York, NY},
	title = {Preparing and {Analyzing} {Polarizable} {Molecular} {Dynamics} {Simulations} with the {Classical} {Drude} {Oscillator} {Model}},
	isbn = {978-1-07-161468-6},
	url = {https://doi.org/10.1007/978-1-0716-1468-6_13},
	abstract = {Molecular dynamics (MD) simulations performed with force fields that include explicit electronic polarization are becoming more prevalent in the field. The increasing emergence of these simulations is a result of continual refinement against a range of theoretical and empirical target data, optimization of software algorithms for higher performance, and availability of graphical processing unit hardware to further accelerate the simulations. Polarizable MD simulations are likely to be most impactful in biomolecular systems in which heterogeneous environments or unique microenvironments exist that would lead to inaccuracies in simulations performed with fixed-charge, nonpolarizable force fields. The further adoption of polarizable MD simulations will benefit from tutorial material that specifically addresses preparing and analyzing their unique features. In this chapter, we introduce common protocols for preparing routine biomolecular systems containing proteins, including both a globular protein in aqueous solvent and a transmembrane model peptide in a phospholipid bilayer. Details and example input files are provided for preparation of the simulation system using CHARMM, performing the simulations with OpenMM, and analyzing interesting dipole moment properties in CHARMM.},
	language = {en},
	urldate = {2024-10-24},
	booktitle = {Computational {Design} of {Membrane} {Proteins}},
	publisher = {Springer US},
	author = {Lemkul, Justin A.},
	editor = {Moreira, Irina S. and Machuqueiro, Miguel and Mourão, Joana},
	year = {2021},
	doi = {10.1007/978-1-0716-1468-6_13},
	pages = {219--240}
}

@article{case_ambertools_2023,
	title = {{AmberTools}},
	volume = {63},
	issn = {1549-9596},
	url = {https://doi.org/10.1021/acs.jcim.3c01153},
	doi = {10.1021/acs.jcim.3c01153},
	abstract = {AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.},
	number = {20},
	urldate = {2024-10-24},
	journal = {J. Chem. Inf. Model.},
	author = {Case, David A. and Aktulga, Hasan Metin and Belfon, Kellon and Cerutti, David S. and Cisneros, G. Andrés and Cruzeiro, Vinícius Wilian D. and Forouzesh, Negin and Giese, Timothy J. and Götz, Andreas W. and Gohlke, Holger and Izadi, Saeed and Kasavajhala, Koushik and Kaymak, Mehmet C. and King, Edward and Kurtzman, Tom and Lee, Tai-Sung and Li, Pengfei and Liu, Jian and Luchko, Tyler and Luo, Ray and Manathunga, Madushanka and Machado, Matias R. and Nguyen, Hai Minh and O’Hearn, Kurt A. and Onufriev, Alexey V. and Pan, Feng and Pantano, Sergio and Qi, Ruxi and Rahnamoun, Ali and Risheh, Ali and Schott-Verdugo, Stephan and Shajan, Akhil and Swails, Jason and Wang, Junmei and Wei, Haixin and Wu, Xiongwu and Wu, Yongxian and Zhang, Shi and Zhao, Shiji and Zhu, Qiang and Cheatham, Thomas E. III and Roe, Daniel R. and Roitberg, Adrian and Simmerling, Carlos and York, Darrin M. and Nagan, Maria C. and Merz, Kenneth M. Jr.},
	month = oct,
	year = {2023},
	pages = {6183--6191},
}

@article{salomon-ferrer_overview_2013,
	title = {An overview of the {Amber} biomolecular simulation package},
	volume = {3},
	copyright = {Copyright © 2012 John Wiley \& Sons, Ltd.},
	issn = {1759-0884},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1121},
	doi = {10.1002/wcms.1121},
	abstract = {Molecular dynamics (MD) allows the study of biological and chemical systems at the atomistic level on timescales from femtoseconds to milliseconds. It complements experiment while also offering a way to follow processes difficult to discern with experimental techniques. Numerous software packages exist for conducting MD simulations of which one of the widest used is termed Amber. Here, we outline the most recent developments, since version 9 was released in April 2006, of the Amber and AmberTools MD software packages, referred to here as simply the Amber package. The latest release represents six years of continued development, since version 9, by multiple research groups and the culmination of over 33 years of work beginning with the first version in 1979. The latest release of the Amber package, version 12 released in April 2012, includes a substantial number of important developments in both the scientific and computer science arenas. We present here a condensed vision of what Amber currently supports and where things are likely to head over the coming years. Figure 1 shows the performance in ns/day of the Amber package version 12 on a single-core AMD FX-8120 8-Core 3.6GHz CPU, the Cray XT5 system, and a single GPU GTX680. © 2012 John Wiley \& Sons, Ltd. This article is categorized under: Software {\textgreater} Molecular Modeling},
	language = {en},
	number = {2},
	urldate = {2024-11-06},
	journal = {Wiley Interdiscip. Rev. Comput. Mol. Sci.},
	author = {Salomon-Ferrer, Romelia and Case, David A. and Walker, Ross C.},
	year = {2013},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1121},
	pages = {198--210}
}

@incollection{do_gaussian_2024,
	title = {Gaussian {Accelerated} {Molecular} {Dynamics} in {Drug} {Discovery}},
	isbn = {978-3-527-84074-8},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9783527840748.ch2},
	abstract = {This chapter summarizes recent methodological developments and application studies of Gaussian accelerated molecular dynamics (GaMD) in drug discovery. GaMD is an unconstrained enhanced sampling technique that allows for exploration of the conformational space of proteins and understanding of complex biological interactions that often occur on millisecond and longer timescales. The boost potential in GaMD usually exhibits a Gaussian distribution, enabling accurate reweighting of the simulations using cumulant expansion to the second order. Recently developed “selective GaMD” algorithms such as ligand GaMD (LiGaMD), peptide GaMD (Pep-GaMD), and protein–protein interaction GaMD (PPI-GaMD) have enabled microsecond-timescale all-atom simulations to characterize the binding thermodynamics and kinetics of small molecules, flexible peptides, and proteins. GaMD has been successfully applied to reveal the mechanisms of ligand/drug binding to various biomolecules (including GPCRs, nucleic acids, and human ACE2 receptors), as well as generating protein structures for virtual screening in drug discovery.},
	language = {en},
	urldate = {2024-11-06},
	booktitle = {Computational {Drug} {Discovery}},
	publisher = {John Wiley \& Sons, Ltd},
	author = {Do, Hung N. and Wang, Jinan and Joshi, Keya and Koirala, Kushal and Miao, Yinglong},
	year = {2024},
	doi = {10.1002/9783527840748.ch2},
	pages = {21--43}
}

@article{gracia_carmona_flexible_2023,
	title = {Flexible {Gaussian} {Accelerated} {Molecular} {Dynamics} to {Enhance} {Biological} {Sampling}},
	volume = {19},
	issn = {1549-9618},
	url = {https://doi.org/10.1021/acs.jctc.3c00619},
	doi = {10.1021/acs.jctc.3c00619},
	abstract = {Molecular dynamics simulations often struggle to obtain sufficient sampling to study complex molecular events due to high energy barriers separating the minima of interest. Multiple enhanced sampling techniques have been developed and improved over the years to tackle this issue. Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that works by adding a biasing potential, lifting the energy landscape up, and decreasing the height of its barriers. GaMD allows one to increase the sampling of events of interest without the need of a priori knowledge of the system or the relevant coordinates. All required acceleration parameters can be obtained from a previous search run. Upon its development, several improvements for the methodology have been proposed, among them selective GaMD in which the boosting potential is selectively applied to the region of interest. There are currently four selective GaMD methods that have shown promising results. However, all of these methods are constrained on the number, location, and scenarios in which this selective boosting potential can be applied to ligands, peptides, or protein–protein interactions. In this work, we showcase a GROMOS implementation of the GaMD methodology with a fully flexible selective GaMD approach that allows the user to define, in a straightforward way, multiple boosting potentials for as many regions as desired. We show and analyze the advantages of this flexible selective approach on two previously used test systems, the alanine dipeptide and the chignolin peptide, and extend these examples to study its applicability and potential to study conformational changes of glycans and glycosylated proteins.},
	number = {18},
	urldate = {2024-11-06},
	journal = {J. Chem. Theory Comput.},
	author = {Gracia Carmona, Oriol and Oostenbrink, Chris},
	month = sep,
	year = {2023},
	note = {Publisher: American Chemical Society},
	pages = {6521--6531},
	file = {Full Text PDF:C\:\\Users\\hmich\\Zotero\\storage\\VM8AUARY\\Gracia Carmona and Oostenbrink - 2023 - Flexible Gaussian Accelerated Molecular Dynamics to Enhance Biological Sampling.pdf:application/pdf},
}

@article{wang_peptide_2020,
	title = {Peptide {Gaussian} accelerated molecular dynamics ({Pep}-{GaMD}): {Enhanced} sampling and free energy and kinetics calculations of peptide binding},
	volume = {153},
	issn = {0021-9606},
	shorttitle = {Peptide {Gaussian} accelerated molecular dynamics ({Pep}-{GaMD})},
	url = {https://doi.org/10.1063/5.0021399},
	doi = {10.1063/5.0021399},
	abstract = {Peptides mediate up to 40\% of known protein–protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method “Pep-GaMD,” which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.},
	number = {15},
	urldate = {2024-11-06},
	journal = {J. Chem. Phys.},
	author = {Wang, Jinan and Miao, Yinglong},
	month = oct,
	year = {2020},
	pages = {154109},
	file = {Full Text PDF:C\:\\Users\\hmich\\Zotero\\storage\\WS8AWU44\\Wang and Miao - 2020 - Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) Enhanced sampling and free energy and ki.pdf:application/pdf;Snapshot:C\:\\Users\\hmich\\Zotero\\storage\\IHTCV556\\315493.html:text/html},
}

@article{wang_proteinprotein_2022,
	title = {Protein–{Protein} {Interaction}-{Gaussian} {Accelerated} {Molecular} {Dynamics} ({PPI}-{GaMD}): {Characterization} of {Protein} {Binding} {Thermodynamics} and {Kinetics}},
	volume = {18},
	issn = {1549-9618},
	shorttitle = {Protein–{Protein} {Interaction}-{Gaussian} {Accelerated} {Molecular} {Dynamics} ({PPI}-{GaMD})},
	url = {https://doi.org/10.1021/acs.jctc.1c00974},
	doi = {10.1021/acs.jctc.1c00974},
	abstract = {Protein–protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed “PPI-GaMD”, selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein’s flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.},
	number = {3},
	urldate = {2024-11-06},
	journal = {J. Chem. Theory Comput.},
	author = {Wang, Jinan and Miao, Yinglong},
	month = mar,
	year = {2022},
	note = {Publisher: American Chemical Society},
	pages = {1275--1285},
	file = {Full Text PDF:C\:\\Users\\hmich\\Zotero\\storage\\WXVWY7UG\\Wang and Miao - 2022 - Protein–Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD) Characterization of.pdf:application/pdf},
}

@article{wang_ligand_2023,
	title = {Ligand {Gaussian} {Accelerated} {Molecular} {Dynamics} 2 ({LiGaMD2}): {Improved} {Calculations} of {Ligand} {Binding} {Thermodynamics} and {Kinetics} with {Closed} {Protein} {Pocket}},
	volume = {19},
	issn = {1549-9618},
	shorttitle = {Ligand {Gaussian} {Accelerated} {Molecular} {Dynamics} 2 ({LiGaMD2})},
	url = {https://doi.org/10.1021/acs.jctc.2c01194},
	doi = {10.1021/acs.jctc.2c01194},
	abstract = {Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.},
	number = {3},
	urldate = {2024-11-06},
	journal = {J. Chem. Theory Comput.},
	author = {Wang, Jinan and Miao, Yinglong},
	month = feb,
	year = {2023},
	note = {Publisher: American Chemical Society},
	pages = {733--745},
	file = {Full Text PDF:C\:\\Users\\hmich\\Zotero\\storage\\U5X63DBX\\Wang and Miao - 2023 - Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2) Improved Calculations of Ligand Binding.pdf:application/pdf},
}

@article{miao_ligand_2020,
	title = {Ligand {Gaussian} {Accelerated} {Molecular} {Dynamics} ({LiGaMD}): {Characterization} of {Ligand} {Binding} {Thermodynamics} and {Kinetics}},
	volume = {16},
	issn = {1549-9618},
	shorttitle = {Ligand {Gaussian} {Accelerated} {Molecular} {Dynamics} ({LiGaMD})},
	url = {https://doi.org/10.1021/acs.jctc.0c00395},
	doi = {10.1021/acs.jctc.0c00395},
	abstract = {Calculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method, ligand Gaussian accelerated molecular dynamics (LiGaMD), which selectively boosts the ligand nonbonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD\_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host–guest and protein–ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD\_Dual simulations. The calculated guest binding free energies agreed excellently with experimental data with {\textless}1.0 kcal/mol errors. Compared with converged microsecond-time scale conventional molecular dynamics simulations, the sampling errors of LiGaMD\_Dual simulations were also {\textless}1.0 kcal/mol. Accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers’ rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a powerful enhanced sampling approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.},
	number = {9},
	urldate = {2024-11-06},
	journal = {J. Chem. Theory Comput.},
	author = {Miao, Yinglong and Bhattarai, Apurba and Wang, Jinan},
	month = sep,
	year = {2020},
	note = {Publisher: American Chemical Society},
	pages = {5526--5547},
	file = {Full Text PDF:C\:\\Users\\hmich\\Zotero\\storage\\86GRFVXT\\Miao et al. - 2020 - Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD) Characterization of Ligand Binding Thermody.pdf:application/pdf},
}

@article{celerse_efficient_2022-1,
	title = {An {Efficient} {Gaussian}-{Accelerated} {Molecular} {Dynamics} ({GaMD}) {Multilevel} {Enhanced} {Sampling} {Strategy}: {Application} to {Polarizable} {Force} {Fields} {Simulations} of {Large} {Biological} {Systems}},
	volume = {18},
	shorttitle = {An {Efficient} {Gaussian}-{Accelerated} {Molecular} {Dynamics} ({GaMD}) {Multilevel} {Enhanced} {Sampling} {Strategy}},
	url = {https://hal.science/hal-03360596},
	doi = {10.1021/acs.jctc.1c01024},
	abstract = {We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple–timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual–water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2–CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS–GaMD capabilities but also the introduction of the new Adaptive Sampling–US–GaMD (ASUS–GaMD) scheme. The highly parallel ASUS–GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD–US and US.},
	number = {2},
	urldate = {2024-11-07},
	journal = {J. Chem. Theory Comput.},
	author = {Célerse, F. and Inizan, T. Jaffrelot and Lagardère, L. and Adjoua, O. and Monmarché, P. and Miao, Y. and Derat, E. and Piquemal, J.-P.},
	month = jan,
	year = {2022},
	note = {Publisher: American Chemical Society},
	pages = {968--977},
	file = {HAL PDF Full Text:C\:\\Users\\hmich\\Zotero\\storage\\XLVUSKCI\\Célerse et al. - 2022 - An Efficient Gaussian-Accelerated Molecular Dynamics (GaMD) Multilevel Enhanced Sampling Strategy A.pdf:application/pdf},
}

@article{roe_ptraj_2013,
	title = {{PTRAJ} and {CPPTRAJ}: {Software} for {Processing} and {Analysis} of {Molecular} {Dynamics} {Trajectory} {Data}},
	volume = {9},
	issn = {1549-9618},
	shorttitle = {{PTRAJ} and {CPPTRAJ}},
	url = {https://doi.org/10.1021/ct400341p},
	doi = {10.1021/ct400341p},
	abstract = {We describe PTRAJ and its successor CPPTRAJ, two complementary, portable, and freely available computer programs for the analysis and processing of time series of three-dimensional atomic positions (i.e., coordinate trajectories) and the data therein derived. Common tools include the ability to manipulate the data to convert among trajectory formats, process groups of trajectories generated with ensemble methods (e.g., replica exchange molecular dynamics), image with periodic boundary conditions, create average structures, strip subsets of the system, and perform calculations such as RMS fitting, measuring distances, B-factors, radii of gyration, radial distribution functions, and time correlations, among other actions and analyses. Both the PTRAJ and CPPTRAJ programs and source code are freely available under the GNU General Public License version 3 and are currently distributed within the AmberTools 12 suite of support programs that make up part of the Amber package of computer programs (see http://ambermd.org). This overview describes the general design, features, and history of these two programs, as well as algorithmic improvements and new features available in CPPTRAJ.},
	number = {7},
	urldate = {2024-11-07},
	journal = {J. Chem. Theory Comput.},
	author = {Roe, Daniel R. and Cheatham, Thomas E. III},
	month = jul,
	year = {2013},
	note = {Publisher: American Chemical Society},
	pages = {3084--3095},
	file = {Full Text PDF:C\:\\Users\\hmich\\Zotero\\storage\\H6VNUR5I\\Roe and Cheatham - 2013 - PTRAJ and CPPTRAJ Software for Processing and Analysis of Molecular Dynamics Trajectory Data.pdf:application/pdf},
}

@article{bhakat_collective_2022,
	title = {Collective variable discovery in the age of machine learning: reality, hype and everything in between},
	volume = {12},
	issn = {2046-2069},
	shorttitle = {Collective variable discovery in the age of machine learning},
	url = {https://pubs.rsc.org/en/content/articlelanding/2022/ra/d2ra03660f},
	doi = {10.1039/D2RA03660F},
	abstract = {Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations.},
	language = {en},
	number = {38},
	urldate = {2024-11-08},
	journal = {RSC Adv.},
	author = {Bhakat, Soumendranath},
	month = aug,
	year = {2022},
	note = {Publisher: The Royal Society of Chemistry},
	pages = {25010--25024},
	file = {Full Text PDF:C\:\\Users\\hmich\\Zotero\\storage\\UYR3JI26\\Bhakat - 2022 - Collective variable discovery in the age of machine learning reality, hype and everything in betwee.pdf:application/pdf;Supplementary Information PDF:C\:\\Users\\hmich\\Zotero\\storage\\6ND4P6HN\\Bhakat - 2022 - Collective variable discovery in the age of machine learning reality, hype and everything in betwee.pdf:application/pdf},
}

@article{numpy,
 title         = {Array programming with {NumPy}},
 author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant},
 year          = {2020},
 month         = sep,
 journal       = {Nature},
 volume        = {585},
 number        = {7825},
 pages         = {357--362},
 doi           = {10.1038/s41586-020-2649-2},
 publisher     = {Springer Science and Business Media {LLC}},
 url           = {https://doi.org/10.1038/s41586-020-2649-2}
}

@ARTICLE{scipy,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nat. Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}

@article{hwang_charmm_2024,
    author = {Hwang, Wonmuk and Austin, Steven L. and Blondel, Arnaud and Boittier, Eric D. and Boresch, Stefan and Buck, Matthias and Buckner, Joshua and Caflisch, Amedeo and Chang, Hao-Ting and Cheng, Xi and Choi, Yeol Kyo and Chu, Jhih-Wei and Crowley, Michael F. and Cui, Qiang and Damjanovic, Ana and Deng, Yuqing and Devereux, Mike and Ding, Xinqiang and Feig, Michael F. and Gao, Jiali and Glowacki, David R. and Gonzales, James E. II and Hamaneh, Mehdi Bagerhi and Harder, Edward D. and Hayes, Ryan L. and Huang, Jing and Huang, Yandong and Hudson, Phillip S. and Im, Wonpil and Islam, Shahidul M. and Jiang, Wei and Jones, Michael R. and Käser, Silvan and Kearns, Fiona L. and Kern, Nathan R. and Klauda, Jeffery B. and Lazaridis, Themis and Lee, Jinhyuk and Lemkul, Justin A. and Liu, Xiaorong and Luo, Yun and MacKerell, Alexander D. Jr. and Major, Dan T. and Meuwly, Markus and Nam, Kwangho and Nilsson, Lennart and Ovchinnikov, Victor and Paci, Emanuele and Park, Soohyung and Pastor, Richard W. and Pittman, Amanda R. and Post, Carol Beth and Prasad, Samarjeet and Pu, Jingzhi and Qi, Yifei and Rathinavelan, Thenmalarchelvi and Roe, Daniel R. and Roux, Benoit and Rowley, Christopher N. and Shen, Jana and Simmonett, Andrew C. and Sodt, Alexander J. and T{\"o}pfer, Kai and Upadhyay, Meenu and van der Vaart, Arjan and Vazquez-Salazar, Luis Itza and Venable, Richard M. and Warrensford, Luke C. and Woodcock, H. Lee and Wu, Yujin and Brooks, Charles L. III and Brooks, Bernard R. and Karplus, Martin},
    title = {CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed},
    journal = {J. Phys. Chem. B},
    volume = {128},
    number = {41},
    pages = {9976-10042},
    year = {2024},
    doi = {10.1021/acs.jpcb.4c04100},
    note ={PMID: 39303207},
    URL = {https://doi.org/10.1021/acs.jpcb.4c04100},
    eprint = {https://doi.org/10.1021/acs.jpcb.4c04100}
}

@article{feller_langevinpiston_1995,
    author = {Feller, Scott E. and Zhang, Yuhong and Pastor, Richard W. and Brooks, Bernard R.},
    title = {Constant pressure molecular dynamics simulation: The Langevin piston method},
    journal = {J. Chem. Phys.},
    volume = {103},
    number = {11},
    pages = {4613-4621},
    year = {1995},
    month = {09},
    abstract = {A new method for performing molecular dynamics simulations under constant pressure is presented. In the method, which is based on the extended system formalism introduced by Andersen, the deterministic equations of motion for the piston degree of freedom are replaced by a Langevin equation; a suitable choice of collision frequency then eliminates the unphysical ‘‘ringing’’ of the volume associated with the piston mass. In this way it is similar to the ‘‘weak coupling algorithm’’ developed by Berendsen and co‐workers to perform molecular dynamics simulation without piston mass effects. It is shown, however, that the weak coupling algorithm induces artifacts into the simulation which can be quite severe for inhomogeneous systems such as aqueous biopolymers or liquid/liquid interfaces.},
    issn = {0021-9606},
    doi = {10.1063/1.470648},
    url = {https://doi.org/10.1063/1.470648},
    eprint = {https://pubs.aip.org/aip/jcp/article-pdf/103/11/4613/19044319/4613\_1\_online.pdf},
}

@article{darden_pme_1993,
    author = {Darden, Tom and York, Darrin and Pedersen, Lee},
    title = {Particle mesh Ewald: An $N \cdot log(N)$ method for Ewald sums in large systems},
    journal = {J. Chem. Phys.},
    volume = {98},
    number = {12},
    pages = {10089-10092},
    year = {1993},
    month = {06},
    abstract = {An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented. The method is based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms. Timings and accuracies are presented for three large crystalline ionic systems.},
    issn = {0021-9606},
    doi = {10.1063/1.464397},
    url = {https://doi.org/10.1063/1.464397},
    eprint = {https://pubs.aip.org/aip/jcp/article-pdf/98/12/10089/19327766/10089\_1\_online.pdf},
}

@article{lamoureux_swm4_2006,
    title = {A polarizable model of water for molecular dynamics simulations of biomolecules},
    journal = {Chem. Phys. Lett.},
    volume = {418},
    number = {1},
    pages = {245-249},
    year = {2006},
    issn = {0009-2614},
    doi = {https://doi.org/10.1016/j.cplett.2005.10.135},
    url = {https://www.sciencedirect.com/science/article/pii/S0009261405017069},
    author = {Guillaume Lamoureux and Edward Harder and Igor V. Vorobyov and Benoît Roux and Alexander D. MacKerell},
    abstract = {The SWM4-DP polarizable water model [G. Lamoureux, A.D. MacKerell, Jr., B. Roux, J. Chem. Phys. 119 (2003) 5185], based on classical Drude oscillators, is re-optimized for negatively charged Drude particles. The new model, called SWM4-NDP, will be incorporated into a polarizable biomolecular force field currently in development. It is calibrated to reproduce important properties of the neat liquid at room temperature and pressure: vaporization enthalpy, density, static dielectric constant and self-diffusion constant. In this Letter, we also show that it yields the correct liquid shear viscosity and free energy of hydration.}
}

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	file = {ACS Full Text Snapshot:/home/mdpoleto/snap/zotero-snap/common/Zotero/storage/4T5F3DNE/j100059a038.html:text/html},
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	note = {Publisher: American Institute of Physics},
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}