Scalable molecular dynamics with NAMD.
PDF (2.9M)
+5 authors
Journal: 2005/December - Journal of Computational Chemistry
ISSN: 0192-8651
Abstract:
NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.
Open in
PUBMED | DOI | PMC | Google Scholar | Wikipedia
Relations:
Content
Citations
(3K+)
References
(53)
Chemicals
(4)
Processes
(2)
Anatomy
(1)
Affiliates
(1)
Similar articles
Articles by the same authors
Discussion board
J Comput Chem 26(16): 1781-1802
PMID: 16222654

Scalable Molecular Dynamics with NAMD

Beckman Institute, University of Illinois at Urbana-Champaign
UMR CNRS/UHP 7565, Université Henri Poincaré, 54506 Vandœuvre–lès–Nancy cedex, France
Department of Computer Science and Beckman Institute, University of Illinois at Urbana-Champaign
To whom correspondence should be addressed
Copyright notice

Abstract

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This paper, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Next, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, e.g., the Tcl scripting language. Finally, the paper provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

Keywords: biomolecular simulation, molecular dynamics, parallel computing
Abstract

References

  • 1. Kalé Laxmikant, Skeel Robert, Bhandarkar Milind, Brunner Robert, Gursoy Attila, Krawetz Neal, Phillips James, Shinozaki Aritomo, Varadarajan Krishnan, Schulten KlausNAMD2: Greater scalability for parallel molecular dynamics. J Comp Phys. 1999;151:283–312.[PubMed][Google Scholar]
  • 2. Humphrey William, Dalke Andrew, Schulten KlausVMD – Visual Molecular Dynamics. J Mol Graphics. 1996;14:33–38.[PubMed][Google Scholar]
  • 3. Nelson Mark, Humphrey William, Gursoy Attila, Dalke Andrew, Kalé Laxmikant, Skeel Robert, Schulten Klaus, Kufrin RichardMDScope – A visual computing environment for structural biology. Comput Phys Commun. 1995;91(1–3):111–134.[PubMed][Google Scholar]
  • 4. Nelson Mark, Humphrey William, Gursoy Attila, Dalke Andrew, Kalé Laxmikant, Skeel Robert D, Schulten KlausNAMD – A parallel, object-oriented molecular dynamics program. Int J Supercomp Appl High Perform Comp. 1996;10:251–268.[PubMed][Google Scholar]
  • 5. Kosztin Dorina, Bishop Thomas C, Schulten KlausBinding of the estrogen receptor to DNA: The role of waters. Biophys J. 1997;73:557–570.[Google Scholar]
  • 6. Villa Elizabeth, Balaeff Alexander, Schulten KlausStructural dynamics of the Lac repressor-DNA complex revealed by a multiscale simulation. Proc Natl Acad Sci USA. 2005;102:6783–6788.[Google Scholar]
  • 7. MacKerell AD, Jr, Bashford D, Bellott M, Dunbrack RL, Jr, Evanseck J, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher IWE, Roux B, Schlenkrich M, Smith J, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J, Yin D, Karplus MAll-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B. 1998;102:3586–3616.[PubMed][Google Scholar]
  • 8. Weiner SP, Kollman PA, Case DA, Singh UC, Ghio C, Alagona G, Profeta J, Weiner PA new force field for molecular mechanical simulation of nucleic acids and proteins. J Am Chem Soc. 1984;106:765–784.[PubMed][Google Scholar]
  • 9. Berendsen HJC, van der Spoel D, van Drunen RGROMACS: A message passing parallel molecular dynamics implementation. Comput Phys Commun. 1995;91:43–56.[PubMed][Google Scholar]
  • 10. Weiner Paul K, Kollman Peter A. AMBER: Assisted model building with energy refinement. A general program for modeling molecules and their interactions. J Comp Chem. 1981;2(3):287–303.[PubMed]
  • 11. Ewald PDie Berechnung optischer und elektrostatischer Gitterpotentiale. Ann Phys. 1921;64:253–287.[PubMed][Google Scholar]
  • 12. de Leeuw SW, Perram JW, Smith ER. Simulation of electrostatic systems in periodic boundary conditions. I. Lattice sums and dielectric constants. Proc R Soc Lond A. 1980;373:27–56.[PubMed]
  • 13. Grubmüller H, Heller H, Windemuth A, Schulten KGeneralized Verlet algorithm for efficient molecular dynamics simulations with long-range interactions. Molecular Simulation. 1991;6:121–142.[PubMed][Google Scholar]
  • 14. Loncharich Richard J, Brooks Bernhard RThe effects of truncating long-range forces on protein dynamics. Proteins: Stru Func and Genetics. 1989;6:32–45.[PubMed][Google Scholar]
  • 15. Feller Scott E, Pastor Richard W, Rojnuckarin Atipat, Bogusz Stephen, Brooks Bernard REffect of electrostatic force truncation on interfacial and transport properties of water. J Phys Chem. 1996;100:17011–17020.[PubMed][Google Scholar]
  • 16. Bergdorf M, Peter C, Hünenberger PHInfluence of cut-off truncation and artificial periodicity of electrostatic interactions in molecular simulations of solvated ions: A continuum electrostatics study. J Chem Phys. 2003;119:9129–9144.[PubMed][Google Scholar]
  • 17. Kastenholz MA, Hünenberger PHInfluence of artificial periodicity and ionic strength in molecular dynamics simulations of charged biomolecules employing lattice-sum methods. J Phys Chem B. 2004;108:774–788.[PubMed][Google Scholar]
  • 18. Weber W, Hünenberger PH, McCammon JAMolecular dynamics simulations of a polyalanine octapeptide under Ewald boundary conditions: Influence on artificial periodicity of peptide conformation. J Phys Chem B. 2000;104:3668–3675.[PubMed][Google Scholar]
  • 19. Perram JW, Petersen HG, de Leeuw SWAn algorithm for the simulation of condensed matter which grows as the 3/2 power of the number of particles. Mol Phys. 1988;65:875–889.[PubMed][Google Scholar]
  • 20. Darden Tom A, York Darrin M, Pedersen Lee GParticle mesh Ewald: An Nlog(N) method for Ewald sums in large systems. J Chem Phys. 1993 November 15;98(10):10089.[PubMed][Google Scholar]
  • 21. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pederson LA smooth particle mesh Ewald method. J Chem Phys. 1995;103:8577–8593.[PubMed][Google Scholar]
  • 22. Hairer Ernst, Lubich Christian, Wanner Gerhard Geometric Numerical Integration, volume 31 of Springer Series in Computational Mathematics. Springer-Verlag; Berlin: 2002. [PubMed][Google Scholar]
  • 23. Aksimentiev Aleksij, Schulten KlausImaging alpha-hemolysin with molecular dynamics: Ionic conductance, osmotic permeability and the electrostatic potential map. Biophys J. 2005;88:3745–3761.[Google Scholar]
  • 24. Hockney RW, Eastwood JW Computer Simulation Using Particles. McGraw-Hill; New York: 1981. [PubMed][Google Scholar]
  • 25. Skeel Robert D, Tezcan Ismail, Hardy David JMultiple grid methods for classical molecular dynamics. J Comput Chem. 2002 April 30;23(6):673–684.[PubMed][Google Scholar]
  • 26. Brandt A, Lubrecht AAMultilevel matrix multiplication and fast solution of integral equations. J Comput Phys. 1990;90:348–370.[PubMed][Google Scholar]
  • 27. Arnol’d VI Mathematical Methods of Classical Mechanics. 2. Springer-Verlag; New York: 1989. [PubMed][Google Scholar]
  • 28. Reich SebastianBackward error analysis for numerical integrators. SIAM J Numer Anal. 1999;36(5):1549–1570.[PubMed][Google Scholar]
  • 29. Harvey SCTreatment of electrostatic effects in macromolecular modeling. Proteins: Struct, Func, Gen. 1989;5:78–92.[PubMed][Google Scholar]
  • 30. Dahlquist G, Björck Å Numerical Methods. Prentice Hall, Englewood Cliffs; New Jersey: 1974. [PubMed][Google Scholar]
  • 31. Frenkel Daan, Smit Berend Understanding Molecular Simulation From Algorithms to Applications. Academic Press; California: 2002. [PubMed][Google Scholar]
  • 32. Gear CW Numerical Initial Value Problems in Ordinary Differential Equations. Prentice-Hall; Englewood Cliffs, N. J.: 1971. [PubMed][Google Scholar]
  • 33. Tuckerman M, Berne BJ, Martyna GJReversible multiple time scale molecular dynamics. J Chem Phys. 1992;97(3):1990–2001.[PubMed][Google Scholar]
  • 34. Grubmüller Helmut Master’s thesis. Physik-Dept. der Tech. Univ.; Munich, Germany: 1989. Dynamiksimulation sehr großer Makromoleküle auf einem Parallelrechner. [PubMed][Google Scholar]
  • 35. Bishop Thomas C, Skeel Robert D, Schulten KlausDifficulties with multiple time stepping and the fast multipole algorithm in molecular dynamics. J Comp Chem. 1997;18:1785–1791.[PubMed][Google Scholar]
  • 36. Ma Q, Izaguirre JA, Skeel RDVerlet-I/R-RESPA/IMPULSE is limited by nonlinear instabilities. SIAM J Sci Comput. 2003;24:1951–1973.[PubMed][Google Scholar]
  • 37. Kubo R, Toda M, Hashitsume N Statistical Physics II: Nonequilibrium Statistical Mechanics. 2. Springer. 1991. [PubMed][Google Scholar]
  • 38. Brünger A, Brooks CB, Karplus MStochastic boundary conditions for molecular dynamics simulations of ST2 water. Chem Phys Lett. 1984;105:495–500.[PubMed][Google Scholar]
  • 39. Mishra Bimal, Schlick Tamar. The notion of error in Langevin dynamics: I. Linear analysis. J Chem Phys. 1996 July 1;105(1):299–318.[PubMed]
  • 40. Wang Wei, Skeel Robert DAnalysis of a few numerical integration methods for the Langevin equation. Mol Phys. 2003;101(14):2149–2156.[PubMed][Google Scholar]
  • 41. Park Sanghyun, Schulten KlausCalculating potentials of mean force from steered molecular dynamics simulations. J Chem Phys. 2004;120:5946–5961.[PubMed][Google Scholar]
  • 42. Tuckerman M, Liu Y, Ciccotti G, Martyna GJNon-Hamiltonian molecular dynamics: generalizing Hamiltonian phase space principles to non-Hamiltonian systems. J Chem Phys. 2001;115:1678–1702.[PubMed][Google Scholar]
  • 43. Bhandarkar M, Brunner R, Chipot C, Dalke A, Dixit S, Grayson P, Gullingsrud J, Gursoy A, Hardy D, Humphrey W, Hurwitz D, Krawetz N, Nelson M, Phillips J, Shinozaki A, Zheng G, Zhu F. NAMD user’s guide version 2.5..[PubMed]
  • 44. Feller Scott E, Zhang Yuhong, Pastor Richard W, Brooks Bernard RConstant pressure molecular dynamics simulation: The Langevin piston method. J Chem Phys. 1995 September 15;103(11):4613–4621.[PubMed][Google Scholar]
  • 45. Hoover WGCanonical dynamics: Equilibrium phase-space distributions. Phys Rev A. 1985;31:1695–1697.[PubMed][Google Scholar]
  • 46. Hoover WGConstant-pressure equations of motion. Phys Rev A. 1986;34(3):2499–2500.[PubMed][Google Scholar]
  • 47. Hoover WG Computational Statistical Mechanics. Elsevier; Amsterdam: 1991. [PubMed][Google Scholar]
  • 48. Quigley D, Probert MIJLangevin dynamics in constant pressure extended systems. J Chem Phys. 2004;120:11432–11441.[PubMed][Google Scholar]
  • 49. Izrailev Sergei, Stepaniants Sergey, Isralewitz Barry, Kosztin Dorina, Lu Hui, Molnar Ferenc, Wriggers Willy, Schulten Klaus. Steered molecular dynamics. In: Deuflhard P, Hermans J, Leimkuhler B, Mark AE, Reich S, Skeel RD, editors. Computational Molecular Dynamics: Challenges, Methods, Ideas, volume 4 of Lecture Notes in Computational Science and Engineering. Springer-Verlag; Berlin: 1998. pp. 39–65. [PubMed]
  • 50. Isralewitz Barry, Baudry Jerome, Gullingsrud Justin, Kosztin Dorina, Schulten KlausSteered molecular dynamics investigations of protein function. Journal of Molecular Graphics and Modeling. 2001;19:13–25. Also in Protein Flexibility and Folding, L. A. Kuhn and M. F. Thorpe, editors, Biological Modeling Series (Elsevier) [[PubMed][Google Scholar]
  • 51. Isralewitz Barry, Gao Mu, Schulten KlausSteered molecular dynamics and mechanical functions of proteins. Curr Op Struct Biol. 2001;11:224–230.[PubMed][Google Scholar]
  • 52. Stone John, Gullingsrud Justin, Grayson Paul, Schulten Klaus. A system for interactive molecular dynamics simulation. In: Hughes John F, Séquin Carlo H., editors. 2001 ACM Symposium on Interactive 3D Graphics. New York: ACM SIGGRAPH; 2001. pp. 191–194. [PubMed]
  • 53. Grayson Paul, Tajkhorshid Emad, Schulten KlausMechanisms of selectivity in channels and enzymes studied with interactive molecular dynamics. Biophys J. 2003;85:36–48.[Google Scholar]
  • 54. Gullingsrud Justin, Braun Rosemary, Schulten KlausReconstructing potentials of mean force through time series analysis of steered molecular dynamics simulations. J Comp Phys. 1999;151:190–211.[PubMed][Google Scholar]
  • 55. Sotomayor Marcos, Corey David P, Schulten KlausIn search of the hair-cell gating spring: Elastic properties of ankyrin and cadherin repeats. Structure. 2005;13:669–682.[PubMed][Google Scholar]
  • 56. Craig David, Gao Mu, Schulten Klaus, Vogel ViolaStructural insights into how the MIDAS ion stabilizes integrin binding to an RGD peptide under force. Structure. 2004;12:2049–2058.[PubMed][Google Scholar]
  • 57. Aksimentiev Aleksij, Balabin Ilya A, Fillingame Robert H, Schulten KlausInsights into the molecular mechanism of rotation in the Fo sector of ATP synthase. Biophys J. 2004;86:1332–1344.[Google Scholar]
  • 58. Zhu Fangqiang, Tajkhorshid Emad, Schulten KlausTheory and simulation of water permeation in aquaporin-1. Biophys J. 2004;86:50–57.[Google Scholar]
  • 59. Gullingsrud Justin, Schulten KlausGating of MscL studied by steered molecular dynamics. Biophys J. 2003;85:2087–2099.[Google Scholar]
  • 60. Bayas MV, Schulten Klaus, Leckband DForced detachment of the CD2-CD58 complex. Biophys J. 2003;84:2223–2233.[Google Scholar]
  • 61. Gao Mu, Wilmanns Matthias, Schulten KlausSteered molecular dynamics studies of titin I1 domain unfolding. Biophys J. 2002;83:3435–3445.[Google Scholar]
  • 62. Jensen Morten Ø, Park Sanghyun, Tajkhorshid Emad, Schulten KlausEnergetics of glycerol conduction through aquaglyceroporin GlpF. Proc Natl Acad Sci USA. 2002;99:6731–6736.[Google Scholar]
  • 63. Kosztin Dorina, Izrailev Sergei, Schulten KlausUnbinding of retinoic acid from its receptor studied by steered molecular dynamics. Biophys J. 1999;76:188–197.[Google Scholar]
  • 64. Lu Hui, Schulten KlausSteered molecular dynamics simulations of force-induced protein domain unfolding. Proteins: Struct, Func, Gen. 1999;35:453–463.[PubMed][Google Scholar]
  • 65. Stepaniants Sergey, Izrailev Sergei, Schulten KlausExtraction of lipids from phospholipid membranes by steered molecular dynamics. J Mol Mod. 1997;3:473–475.[PubMed][Google Scholar]
  • 66. Izrailev Sergei, Crofts Antony R, Berry Edward A, Schulten KlausSteered molecular dynamics simulation of the Rieske subunit motion in the cytochrome bc1 complex. Biophys J. 1999;77:1753–1768.[Google Scholar]
  • 67. Izrailev Sergei, Stepaniants Sergey, Balsera Manel, Oono Yoshi, Schulten KlausMolecular dynamics study of unbinding of the avidin-biotin complex. Biophys J. 1997;72:1568–1581.[Google Scholar]
  • 68. Isralewitz Barry, Izrailev Sergei, Schulten KlausBinding pathway of retinal to bacterio-opsin: A prediction by molecular dynamics simulations. Biophys J. 1997;73:2972–2979.[Google Scholar]
  • 69. Grubmüller Helmut, Heymann Berthold, Tavan PaulLigand binding and molecular mechanics calculation of the streptavidin-biotin rupture force. Science. 1996;271:997–999.[PubMed][Google Scholar]
  • 70. Kosztin Dorina, Izrailev Sergei, Schulten KlausUnbinding of retinoic acid from its receptor studied by steered molecular dynamics. Biophys J. 1999;76:188–197.[Google Scholar]
  • 71. Torrie GM, Valleau JPNonphysical sampling distributions in Monte Carlo free energy estimation: Umbrella sampling. J Comput Phys. 1977;23:187–199.[PubMed][Google Scholar]
  • 72. Roux BThe calculation of the potential of mean force using computer simulations. Comput Phys Comm. 1995;91:275–282.[PubMed][Google Scholar]
  • 73. Schulten Klaus, Schulten Zan, Szabo AttilaDynamics of reactions involving diffusive barrier crossing. J Chem Phys. 1981;74:4426–4432.[PubMed][Google Scholar]
  • 74. Lu Hui, Krammer André, Isralewitz Barry, Vogel Viola, Schulten Klaus. Computer modeling of force-induced titin domain unfolding. In: Pollack Jerry, Granzier Henk., editors. Elastic Filaments of the Cell, chapter 1. Kluwer Academic/Plenum Publishers; New York, NY: 2000. pp. 143–162. [PubMed]
  • 75. Lu Hui, Schulten KlausThe key event in force-induced unfolding of titin’s immunoglobulin domains. Biophys J. 2000;79:51–65.[Google Scholar]
  • 76. Jarzynski CNonequilibrium equality for free energy differences. Phys Rev Lett. 1997;78:2690–2693.[PubMed][Google Scholar]
  • 77. Jarzynski CEquilibrium free-energy differences from nonequilibrium measurements: A master equation approach. Phys Rev E. 1997;56:5018–5035.[PubMed][Google Scholar]
  • 78. Liphardt J, Dumont S, Smith S, Tinoco I, Bustamante CEquilibrium information from nonequilibrium measurements in an experimental test of Jarzynski’s equality. 2002;296:1832.[PubMed]
  • 79. Hummer G, Rasaiah JC, Noworyta JPWater conduction through the hydrophobic channel of a carbon nanotube. Nature. 2001;414:188–190.[PubMed][Google Scholar]
  • 80. Marcinkiewicz J. Math Z. 1939;44:612.[PubMed]
  • 81. Park Sanghyun, Khalili-Araghi Fatemeh, Tajkhorshid Emad, Schulten KlausFree energy calculation from steered molecular dynamics simulations using Jarzynski’s equality. J Chem Phys. 2003;119:3559–3566.[PubMed][Google Scholar]
  • 82. Kumar S, Bouzida D, Swendsen RH, Kollman PA, Rosenberg JM. The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comp Chem. 1992;13:1011–1021.[PubMed]
  • 83. Boczko EM, Brooks CL., III Constant-temperature free energy surfaces for physical and chemical processes. J Phys Chem. 1993;97:4509–4513.[PubMed]
  • 84. Shea JE, Brooks CL., III From folding theories to folding proteins: A review and assessment of simulation studies of protein folding and unfolding. Annu Rev Phys Chem. 2001;52:499–535.[PubMed]
  • 85. Aksimentiev Aleksij, Balabin Ilya A, Fillingame Robert H, Schulten KlausInsights into the molecular mechanism of rotation in the Fo sector of ATP synthase. Biophys J. 2004;86:1332–1344.[Google Scholar]
  • 86. Villa Elizabeth, Balaeff Alexander, Mahadevan L, Schulten KlausMulti-scale method for simulating protein-DNA complexes. Multiscale Model Simul. 2004;2:527–553.[PubMed][Google Scholar]
  • 87. Taylor RM., II VRPN: Virtual Reality Peripheral Network. 1998. .[PubMed]
  • 88. Dachille Frank, Qin Hong, Kaufman Arie, El-Sana JihadHaptic sculpting of dynamic surfaces. 1999 Symposium on Interactive 3D Graphics. 1999:103–110.[PubMed][Google Scholar]
  • 89. Cohen Jordi, Grayson Paul. Autoimd user’s guide, 2003..[PubMed]
  • 90. den Otter WK, Briels WJThe calculation of free-energy differences by constrained molecular dynamics simulations. J Chem Phys. 1998;109:4139–4146.[PubMed][Google Scholar]
  • 91. Zwanzig RW. High-temperature equation of state by a perturbation method. i. nonpolar gases. J Chem Phys. 1954;22:1420–1426.[PubMed]
  • 92. Darve E, Pohorille ACalculating free energies using average force. J Chem Phys. 2001;115:9169–9183.[PubMed][Google Scholar]
  • 93. Hénin Jérôme, Chipot ChristopheOvercoming free energy barriers using unconstrained molecular dynamics simulations. J Chem Phys. 2004;121(7):2904–2914.[PubMed][Google Scholar]
  • 94. Gao J, Kuczera K, Tidor B, Karplus MHidden thermodynamics of mutant proteins: A molecular dynamics analysis. Science. 1989;244:1069–1072.[PubMed][Google Scholar]
  • 95. Fleming KG, Ackerman AL, Engelman DMThe effect of point mutations on the free energy of transmembrane α-helix dimerization. J Mol Biol. 1997;272:266–275.[PubMed][Google Scholar]
  • 96. Hénin J, Pohorille A, Chipot C. Insights into the recognition and association of trans-membrane α-helices. the free energy of α-helix dimerization in glycophorin a. J Am Chem Soc. 2005;127:8478–8484.[PubMed]
  • 97. Allen MP, Tildesley DJ Computer Simulation of Liquids. Oxford University Press; New York: 1987. [PubMed][Google Scholar]
  • 98. Kalé LV, Krishnan Sanjeev. Charm++: Parallel programming with message-driven objects. In: Wilson Gregory V, Lu Paul., editors. Parallel Programming using C++ MIT Press; 1996. pp. 175–213. [PubMed]
  • 99. Kalé Laxmikant V LACSI 2002. Albuquerque; Oct, 2002. The virtualization model of parallel programming: Runtime optimizations and the state of art. [PubMed][Google Scholar]
  • 100. Brunner Robert K, Kalé Laxmikant V Parallel and Distributed Computing for Symbolic and Irregular Applications. World Scientific Publishing; 2000. Handling application-induced load imbalance using parallel objects; pp. 167–181. [PubMed][Google Scholar]
  • 101. Phillips James C, Brunner Robert, Shinozaki Aritomo, Bhandarkar Milind, Krawetz Neal, Kalé Laxmikant, Skeel Robert D, Schulten Klaus. Avoiding algorithmic obfuscation in a message-driven parallel MD code. In: Deuflhard P, Hermans J, Leimkuhler B, Mark A, Reich S, Skeel RD, editors. Computational Molecular Dynamics: Challenges, Methods, Ideas, volume 4 of Lecture Notes in Computational Science and Engineering. Springer-Verlag; 1998. pp. 472–482. [PubMed]
  • 102. Ousterhout J Tcl and the Tk Toolkit. Addison-Wesley, Reading; Massachusetts: 1994. [PubMed][Google Scholar]
  • 103. Sugita Yuji, Okamoto YukoReplica-exchange molecular dynamics method for protein folding. Chem Phys Lett. 1999;314:141–151.[PubMed][Google Scholar]
  • 104. Kale Laxmikant V, Zheng Gengbin, Lee Chee Wai, Kumar SameerScaling applications to massively parallel machines using projections performance analysis tool. Future Generation Computer Systems Special Issue on: Large-Scale System Performance Modeling and Analysis. 2005 number to appear. [PubMed][Google Scholar]
  • 105. Fabrizio Petrini, Kerbyson Darren J, Pakin Scott Proceedings of the IEEE/ACM SC2003 Conference, Technical Paper 301. IEEE Press; 2003. The case of the missing supercomputer performance: Achieving optimal performance on the 8,192 processors of ASCI Q. [PubMed][Google Scholar]
  • 106. Phillips James, Zheng Gengbin, Kumar Sameer, Kale Laxmikant Proceedings of the IEEE/ACM SC2002 Conference, Technical Paper 277. IEEE Press; 2002. NAMD: Biomolecular simulation on thousands of processors. [PubMed][Google Scholar]
  • 107. Hershko A, Heller H, Elias S, Ciechanover AComponents of ubiquitin-protein ligase system. J Biol Chem. 1983;258:8206–8214.[PubMed][Google Scholar]
  • 108. Haas AL, Rose IAThe mechanism of ubiquitin activating enzyme. J Biol Chem. 1982;257:10329–10337.[PubMed][Google Scholar]
  • 109. Hershko A, Ciechanover AThe ubiquitin system. Ann Rev Biochem. 1998;67:425–479.[PubMed][Google Scholar]
  • 110. Carrion-Vazquez M, Li H, Lu H, Marszalek PE, Oberhauser AF, Fernandez JMThe mechanical stability of ubiquitin is linkage dependent. Nature Struct Biol. 2003;10:738–743.[PubMed][Google Scholar]
  • 111. Fernandez JM, Li HForce-clamp spectroscopy monitors the folding trajectory of a single protein. Science. 2004;303:1674–1678.[PubMed][Google Scholar]
  • 112. Zhu Fangqiang, Tajkhorshid Emad, Schulten KlausMolecular dynamics study of aquaporin-1 water channel in a lipid bilayer. FEBS Lett. 2001;504:212–218.[PubMed][Google Scholar]
  • 113. Jensen Morten Ø, Tajkhorshid Emad, Schulten KlausThe mechanism of glycerol conduction in aquaglyceroporins. Structure. 2001;9:1083–1093.[PubMed][Google Scholar]
  • 114. Tajkhorshid Emad, Nollert Peter, Jensen Morten Ø, Miercke Larry JW, O’Connell Joseph, Stroud Robert M, Schulten KlausControl of the selectivity of the aquaporin water channel family by global orientational tuning. Science. 2002;296:525–530.[PubMed][Google Scholar]
  • 115. Zhu Fangqiang, Tajkhorshid Emad, Schulten KlausPressure-induced water transport in membrane channels studied by molecular dynamics. Biophys J. 2002;83:154–160.[Google Scholar]
  • 116. Roux Benoit, Schulten KlausComputational studies of membrane channels. Structure. 2004;12:1343–1351.[PubMed][Google Scholar]
  • 117. Tajkhorshid Emad, Cohen Jordi, Aksimentiev Aleksij, Sotomayor Marcos, Schulten Klaus. Towards understanding membrane channels. In: Martinac Boris, Kubalski Andrzej., editors. Bacterial ion channels and their eukaryotic homologues. ASM Press; Washington, DC, : 2005. pp. 153–190. [PubMed]
  • 118. Jensen Morten Ø, Tajkhorshid Emad, Schulten KlausElectrostatic tuning of permeation and selectivity in aquaporin water channels. Biophys J. 2003;85:2884–2899.[Google Scholar]
  • 119. Zhu Fangqiang, Tajkhorshid Emad, Schulten KlausCollective diffusion model for water permeation through microscopic channels. Phys Rev Lett. 2004;93:224501. (4 pages).[PubMed][Google Scholar]
  • 120. Lewis Mitchell, Chang Geoffrey, Horton Nancy C, Kercher Michele A, Pace Helen C, Schumacher Maria A, Brennan Richard G, Lu PonzyCrystal structure of the lactose operon re-pressor and its complexes with DNA and inducer. Science. 1996;271:1247–1254.[PubMed][Google Scholar]
  • 121. Friedman Alan M, Fischmann Thierry O, Steitz Thomas ACrystal structure of lac repressor core tetramer and its implications for DNA looping. Science. 1995;268:1721–1727.[PubMed][Google Scholar]
  • 122. Ptashne Mark A Genetic Switch. 2. Cell Press & Blackwell Scientific Publications; Cambridge, MA: 1992. [PubMed][Google Scholar]
  • 123. Müller-Hill B The lac operon. Walter de Gruyter; New York: 1996. [PubMed][Google Scholar]
  • 124. Balaeff Alexander, Koudella Christophe R, Mahadevan L, Schulten KlausModeling DNA loops using continuum and statistical mechanics. Phil Trans R Soc Lond A. 2004;362:1355–1371.[PubMed][Google Scholar]
  • 125. Balaeff Alexander, Mahadevan L, Schulten KlausStructural basis for cooperative DNA binding by CAP and Lac repressor. Structure. 2004;12:123–132.[PubMed][Google Scholar]
  • 126. The RCSB Protein Data Bank..[PubMed]
  • 127. Edelman Laurence M, Cheong Raymond, Kahn Jason DFluorescence resonance energy transfer over ≈130 basepairs in hyperstable lac repressor-DNA loops. Biophys J. 2003;84:1131–1145.[Google Scholar]
  • 128. Bhandarkar Milind, Budescu Gila, Humphrey William, Izaguirre Jesus A, Izrailev Sergei, Kalé Laxmikant V, Kosztin Dorina, Molnar Ferenc, Phillips James C, Schulten Klaus. Bio-CoRE: A collaboratory for structural biology. In: Bruzzone Agostino G, Uchrmacher Adelinde, Page Ernest H., editors. Proceedings of the SCS International Conference on Web-Based Modeling and Simulation. San Francisco, California: 1999. –242.pp. 251 [PubMed]
Collaboration tool especially designed for Life Science professionals.Drag-and-drop any entity to your messages.