22. Bibliography

1

James A. Maier, Carmenza Martinez, Koushik Kasavajhala, Lauren Wickstrom, Kevin E. Hauser, and Carlos Simmerling. Ff14sb: improving the accuracy of protein side chain and backbone parameters from ff99sb. Journal of Chemical Theory and Computation, 11(8):3696–3713, 2015.

2

Karl N. Kirschner, Austin B. Yongye, Sarah M. Tschampel, Jorge González-Outeiriño, Charlisa R Daniels, B. Lachele Foley, and Robert J. Woods. Glycam06: a generalizable biomolecular force field. carbohydrates. Journal of Computational Chemistry, 29:622–655, 2007.

3

William L. Jorgensen, Jayaraman Chandrasekhar, Jeffry D. Madura, Roger W. Impey, and Michael L. Klein. Comparison of simple potential functions for simulating liquid water. Journal of Chemical Physics, 79:926–935, 1983.

4

Lee-Ping Wang, Todd J. Martinez, and Vijay S. Pande. Building force fields: an automatic, systematic, and reproducible approach. Journal of Physical Chemistry Letters, 5:1885–1891, 2014.

5

Hans W. Horn, William C. Swope, Jed W. Pitera, Jeffry D. Madura, Thomas J. Dick, Greg L. Hura, and Teresa Head-Gordon. Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. Journal of Chemical Physics, 120:9665–9678, 2004.

6

H. J. C. Berendsen, J. R. Grigera, and T. P. Straatsma. The missing term in effective pair potentials. Journal of Physical Chemistry, 91:6269–6271, 1987.

7

Saeed Izadi, Ramu Anandakrishnan, and Alexey V Onufriev. Building water models: a different approach. The journal of Physical Chemistry Letters, 5(21):3863–3871, 2014. doi:10.1021/jz501780a.

8

Saeed Izadi and Alexey V Onufriev. Accuracy limit of rigid 3-point water models. The Journal of Chemical Physics, 145(7):074501, 2016. doi:10.1063/1.4960175.

9

Robert B. Best, Xiao Zhu, Jihyun Shim, Pedro E. M. Lopes, Jeetain Mittal, Michael Feig, and Alexander D. MacKerell. Optimization of the additive charmm all-atom protein force field targeting improved sampling of the backbone φ, ψ, and side-chain χ1 and χ2 dihedral angles. Journal of Chemical Theory and Computation, 8(9):3257–3273, 2012.

10

D.J. Price and C.L. III Brooks. A modified tip3p water potential for simulation with ewald summation. Journal of Chemical Physics, 121:10096–10103, 2004.

11

J.L.F. Abascal and C. Vega. A general purpose model for the condensed phases of water: tip4p/2005. Journal of Chemical Physics, 123:234505, 2005.

12

Michael W. Mahoney and William L. Jorgensen. A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions. Journal of Chemical Physics, 112:8910–8922, 2000.

13

S.W. Rick. A reoptimization of the five-site water potential (tip5p) for use with ewald sums. Journal of Chemical Physics, 120:6085–6093, 2004.

14

Pedro E. M. Lopes, Jing Huang, Jihyun Shim, Yun Luo, Hui Li, Benoît Roux, and Alexander D. MacKerell. Polarizable force field for peptides and proteins based on the classical Drude oscillator. Journal of Chemical Theory and Computation, 9(12):5430–5449, 2013.

15

Gregory D. Hawkins, Christopher J. Cramer, and Donald G. Truhlar. Pairwise solute descreening of solute charges from a dielectric medium. Chemical Physics Letters, 246(1-2):122–129, 1995.

16

Alexey Onufriev, Donald Bashford, and David A. Case. Exploring protein native states and large-scale conformational changes with a modified generalized Born model. Proteins, 55(22):383–394, 2004.

17

John Mongan, Carlos Simmerling, J. Andrew McCammon, David A. Case, and Alexey Onufriev. Generalized Born model with a simple, robust molecular volume correction. Journal of Chemical Theory and Computation, 3(1):156–169, 2007.

18

Hai Nguyen, Daniel R. Roe, and Carlos Simmerling. Improved generalized Born solvent model parameters for protein simulations. Journal of Chemical Theory and Computation, 9(4):2020–2034, 2013.

19

J Srinivasan, M. W. Trevathan, P. Beroza, and D. A. Case. Application of a pairwise generalized Born model to proteins and nucleic acids: inclusion of salt effects. Theor. Chem. Acc., 101:426–434, 1999.

20

Yue Shi, Zhen Xia, Jiajing Zhang, Robert Best, Chuanjie Wu, Jay W. Ponder, and Pengyu Ren. Polarizable atomic multipole-based AMOEBA force field for proteins. Journal of Chemical Theory and Computation, 9(9):4046–4063, 2013.

21

Michael J. Schnieders and Jay W. Ponder. Polarizable atomic multipole solutes in a generalized Kirkwood continuum. Journal of Chemical Theory and Computation, 3:2083–2097, 2007.

22

P. Ren and Jay W. Ponder. A consistent treatment of inter- and intramolecular polarization in molecular mechanics calculations. Journal of Computational Chemistry, 23:1497–1506, 2002.

23

P.A. Kollman, R. Dixon, W. Cornell, T. Fox, C. Chipot, and A. Pohorille. Computer Simulation of Biomolecular Systems, pages 83–96. Volume 3. Elsevier, 1997.

24

J. Wang, P. Cieplak, and P.A. Kollman. How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? Journal of Computational Chemistry, 21:1049–1074, 2000.

25

V. Hornak, R. Abel, A. Okur, B. Strockbine, A. Roitberg, and C. Simmerling. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins, 65:712–725, 2006.

26

K. Lindorff-Larsen, S. Piana, K. Palmo, P. Maragakis, J. Klepeis, R.O. Dror, and D.E. Shaw. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins, 78:1950–1958, 2010.

27

D.W. Li and R. Brüschweiler. NMR-based protein potentials. Angewandte Chemie International Edition, 49:6778–6780, 2010.

28

C. Duan, Y.; Wu, S. Chowdhury, M.C. Lee, G. Xiong, W. Zhang, R. Yang, P. Cieplak, R. Luo, and T. Lee. A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. Journal of Computational Chemistry, 24:1999–2012, 2003.

29

Jay W. Ponder. TINKER - Software Tools for Molecular Design, 4.2. 2004.

30

Guillaume Lamoureux, Edward Harder, Igor V. Vorobyov, Benoit Roux, and Alexander D. MacKerell Jr. A polarizable model of water for molecular dynamics simulations of biomolecules. Chemical Physics Letters, 418(1-3):245–249, 2006.

31

Andrew C. Simmonett, Frank C. Pickard, Yihan Shao, Thomas E. Cheatham, and Bernard R. Brooks. Efficient treatment of induced dipoles. Journal of Chemical Physics, 143(7):074115, 2015.

32

Joseph E. Bascon and Michael R. Shirts. Effects of temperature control algorithms on transport properties and kinetics in molecular dynamics simulations. Journal of Chemical Theory and Computation, 9:2887–2899, 2013. doi:10.1021/ct400109a.

33

M. Tuckerman, Bruce J. Berne, and Glenn J. Martyna. Reversible multiple time scale molecular dynamics. Journal of Chemical Physics, 97(3):1990–2001, 1992.

34

E Marinari and G Parisi. Simulated tempering: a new monte carlo scheme. Europhysics Letters (EPL), 19(6):451–458, 1992. doi:10.1209/0295-5075/19/6/002.

35

Alessandro Barducci, Giovanni Bussi, and Michele Parrinello. Well-tempered metadynamics: a smoothly converging and tunable free-energy method. Physical Review Letters, 100:020603, 2008. doi:10.1103/PhysRevLett.100.020603.

36

Donald Hamelberg, Cesar Augusto F. de Oliveira, and J. Andrew McCammon. Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. Journal of Chemical Physics, 127(15):155102, 2007.

37

I. R. Craig and David E. Manolopoulos. Quantum statistics and classical mechanics: real time correlation functions from ring polymer molecular dynamics. Journal of Chemical Physics, 121:3368–3373, 2004.

38

M. Parrinello and A. Rahman. Study of an F center in molten KCl. Journal of Chemical Physics, 80(2):860–867, 1984.

39

Thomas E. Markland and David E. Manolopoulos. An efficient ring polymer contraction scheme for imaginary time path integral simulations. Journal of Chemical Physics, 2008.

40

Randall W. Hall and B. J. Berne. Nonergodicity in path integral molecular dynamics. Journal of Chemical Physics, 1984.

41

M. Ceriotti, M. Parrinello, Thomas E. Markland, and David E. Manolopoulos. Efficient stochastic thermostatting of path integral molecular dynamics. Journal of Chemical Physics, 2010.

42

Guillaume Lamoureux and Benoit Roux. Modeling induced polarization with classical Drude oscillators: theory and molecular dynamics simulation algorithm. Journal of Chemical Physics, 119(6):3025–3039, 2003.

43

B. T. Thole. Molecular polarizabilities calculated with a modified dipole interaction. Chemical Physics, 59(3):341–350, 1981.

44

Michael R. Shirts, David L. Mobley, John D. Chodera, and Vijay S. Pande. Accurate and efficient corrections for missing dispersion interactions in molecular simulations. Journal of Physical Chemistry B, 111:13052–13063, 2007.

45

Ilario G. Tironi, René Sperb, Paul E. Smith, and Wilfred F. van Gunsteren. A generalized reaction field method for molecular dynamics simulations. Journal of Chemical Physics, 102(13):5451–5459, 1995.

46

Abdulnour Y. Toukmaji and John A. Board Jr. Ewald summation techniques in perspective: a survey. Computer Physics Communications, 95:73–92, 1996.

47

Ulrich Essmann, Lalith Perera, Max L. Berkowitz, Tom Darden, Hsing Lee, and Lee G. Pedersen. A smooth particle mesh Ewald method. Journal of Chemical Physics, 103(19):8577–8593, 1995.

48

Christian L. Wennberg, Teemu Murtola, Szilárd Páll, Mark J. Abraham, Berk Hess, and Erik Lindahl. Direct-space corrections enable fast and accurate Lorentz–Berthelot combination rule Lennard-Jones lattice summation. Journal of Chemical Theory and Computation, 11(12):5737–5746, 2015.

49

Michael Schaefer, Christian Bartels, and Martin Karplus. Solution conformations and thermodynamics of structured peptides: molecular dynamics simulation with an implicit solvation model. Journal of Molecular Biology, 284(3):835–848, 1998.

50

Jay W. Ponder. Personal communication.

51

R. Everaers and M. R. Ejtehadi. Interaction potentials for soft and hard ellipsoids. Physical Review E, 67:041710, 2003.

52

Hans C. Andersen. Molecular dynamics simulations at constant pressure and/or temperature. Journal of Chemical Physics, 72(4):2384–2393, 1980.

53

Kim-Hung Chow and David M. Ferguson. Isothermal-isobaric molecular dynamics simulations with Monte Carlo volume sampling. Computer Physics Communications, 91:283–289, 1995.

54

Johan Åqvist, Petra Wennerström, Martin Nervall, Sinisa Bjelic, and Bjørn O. Brandsdal. Molecular dynamics simulations of water and biomolecules with a Monte Carlo constant pressure algorithm. Chemical Physics Letters, 384:288–294, 2004.

55

Sander Vandenhaute, Sven M. J. Rogge, and Veronique Van Speybroeck. Large-scale molecular dynamics simulations reveal new insights into the phase transition mechanisms in mil-53(al). Frontiers in Chemistry, 9:699, 2021. doi:10.3389/fchem.2021.718920.

56

Jesús A. Izaguirre, Chris R. Sweet, and Vijay S. Pande. Multiscale dynamics of macromolecules using Normal Mode Langevin. Pacific Symposium on Biocomputing, 15:240–251, 2010.

57

Zhijun Zhang, Xinzijian Liu, Kangyu Yan, Mark E. Tuckerman, and Jian Liu. Unified efficient thermostat scheme for the canonical ensemble with holonomic or isokinetic constraints via molecular dynamics. The Journal of Physical Chemistry A, 123(28):6056–6079, 2019. doi:10.1021/acs.jpca.9b02771.

58

Glenn J. Martyna, Michael L. Klein, and Mark Tuckerman. Nosé–hoover chains: the canonical ensemble via continuous dynamics. Journal of Chemical Physics, 97:2635–2643, 1992.

59

Dong C. Liu and Jorge Nocedal. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45:503–528, 1989.

60

Blas P. Uberuaga, Marian Anghel, and Arthur F. Voter. Synchronization of trajectories in canonical molecular-dynamics simulations: observation, explanation, and exploitation. Journal of Chemical Physics, 120(14):6363–6374, 2004.

61

Daniel J. Sindhikara, Seonah Kim, Arthur F. Voter, and Adrian E. Roitberg. Bad Seeds Sprout Perilous Dynamics: Stochastic Thermostat Induced Trajectory Synchronization in Biomolecules. Journal of Chemical Theory and Computation, 5(6):1624–1631, April 2009.