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Biomolecular modeling and simulation: a field coming of age

Published online by Cambridge University Press:  12 January 2011

Tamar Schlick ,
Rosana Collepardo-Guevara ,
Leif Arthur Halvorsen ,
Segun Jung  and
Xia Xiao
Tamar Schlick*
Affiliation:
Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, NY 10003, USA Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA
Rosana Collepardo-Guevara
Affiliation:
Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, NY 10003, USA
Leif Arthur Halvorsen
Affiliation:
Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, NY 10003, USA
Segun Jung
Affiliation:
Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, NY 10003, USA
Xia Xiao
Affiliation:
Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, NY 10003, USA
*
*Author for correspondence: T. Schlick, Email: [email protected]
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Abstract

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field's history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right.

Type
Review Article
Information
Quarterly Reviews of Biophysics , Volume 44 , Issue 2 , May 2011 , pp. 191 - 228
Copyright
Copyright © Cambridge University Press 2011

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References

11. References

Abrams, C. F. & Vanden-Eijnden, E. (2010). Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics. Proceedings of the National Academy of Sciences, USA 107, 4961–4966.CrossRefGoogle ScholarPubMed
Alder, B. J. & Wainwright, T. E. (1959). Studies in molecular dynamics. I. General method. Journal of Chemical Physics 31, 459–466.Google Scholar
Alexander, P. A., He, Y., Chen, Y., Orban, J. & Bryan, P. N. (2007). The design and characterization of two proteins with 88% sequence identity but different structure and function. Proceedings of the National Academy of Sciences, USA 104, 11963–11968.CrossRefGoogle ScholarPubMed
Altman, R., Radmer, R. & Glazer, D. (2009). Improving structure-based function prediction using molecular dynamics. Structure 17, 919–929.Google Scholar
Amir-Aslani, A. (2008). Toxicogenomic predictive modeling: emerging opportunities for more efficient drug discovery and development. Tech. Forecast. Soc. Change 75, 905–932.CrossRefGoogle Scholar
Arora, K. & Schlick, T. (2004). In Silico evidence for DNA polymerase β's substrate-induced conformational change. Biophysics Journal 87, 3088–3099.CrossRefGoogle ScholarPubMed
Arya, G. & Schlick, T. (2006). Role of histone tails in chromatin folding revealed by a mesoscopic oligonucleosome model. Proceedings of the National Academy of Sciences, USA 103, 16236–16241.CrossRefGoogle ScholarPubMed
Arya, G. & Schlick, T. (2009). A tale of tails: how histone tails mediate chromatin compaction in different salt and linker histone environments. Journal of Physical Chemistry A 113, 4045–4059.CrossRefGoogle ScholarPubMed
Baker, D., Kuhlman, B., Dantas, G., Ireton, G., Varani, G. & Stoddard, B. (2003). Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368.Google Scholar
Barth, E. & Schlick, T. (1998). Overcoming stability limitations in biomolecular dynamics: I. combining force splitting via extrapolation with Langevin dynamics in ln. Journal of Chemical Physics 109, 1617–1632.CrossRefGoogle Scholar
Beachy, M. D., Chasman, D., Murphy, R. B., Halgren, T. A. & Friesner, R. A. (1997). Accurate ab initio quantum chemical determination of the relative energetics of peptide conformations and assessment of empirical force fields. Journal of the American Chemical Society 119, 5908–5920.CrossRefGoogle Scholar
Bebenek, K., Garcia-Diaz, M., Foley, M. C., Pedersen, L. C., Schlick, T. & Kunkel, T. A. (2008). Substrate-induced DNA strand misalignment during catalytic cycling by DNA polymerase λ. EMBO Reports 9, 459–464.CrossRefGoogle ScholarPubMed
Becker, O. M., Dhanoa, D. S., Marantz, Y., Chen, D., Shacham, S., Cheruku, S., Heifetz, A., Mohanty, P., Fichman, M. & Sharadendu, A. (2006). An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. Journal of Medical Chemistry 49, 3116–3135.CrossRefGoogle ScholarPubMed
Ben-David, M., Noivirt-Brik, P., Paz, A., Prilusky, J., Sussman, J. L. & Levy, Y. (2009). Assessment of CASP8 structure predictions for template free targets. Proteins 9, 50–65.CrossRefGoogle Scholar
Berendsen, H. J. C., van der Spoel, D. & van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communication 91, 43–56.CrossRefGoogle Scholar
Berezhkovskii, A., Hummer, G. & Szabo, A. (2009). Reactive flux and folding pathways in network models of coarse-grained protein dynamics. Journal of Chemical Physics 130, 205102.Google Scholar
Best, R. B., Buchete, N.-V. & Hummer, G. (2008). Are current molecular dynamics force fields too helical? Biophysics Journal 95, L07–L09.CrossRefGoogle ScholarPubMed
Best, R. B. & Hummer, G. (2006). Diffusive model of protein folding dynamics with Kramers turnover in rate. Phys. Rev. Lett. 96, 228104.CrossRefGoogle ScholarPubMed
Bezdek, J. (1993). Fuzzy models – what are they, and why? IEEE Transactions on Fuzzy Systems 1, 1–5.CrossRefGoogle Scholar
Bolhuis, P. G., Chandler, D., Dellago, C. & Geissler, P. L. (2002). Transition path sampling: throwing ropes over rough mountain passes, in the dark. Annual Review of Physical Chemistry 53, 291–318.CrossRefGoogle ScholarPubMed
Borman, S. (1998). Reducing time to drug discovery. Chemical and Engineering News 77, 33–48.CrossRefGoogle Scholar
Borman, S. (2010). Human genome sequence milestone. Chemical and Engineering News 88, 30–32.CrossRefGoogle Scholar
Borrell, B. (2009). Fraud rocks protein community. Nature 462, 970.CrossRefGoogle ScholarPubMed
Borrero, E. E. & Escobedo, F. A. (2008). Optimizing the sampling and staging for simulations of rare events via forward flux sampling schemes. Journal of Chemical Physics 129, 024115.CrossRefGoogle ScholarPubMed
Bowers, K. J., Chow, E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., Klepeis, J. L., Kolossvary, I., Moraes, M. A., Sacerdoti, F. D., Salmon, J. K., Shan, Y. & Shaw, D. E. (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters. In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida ACM Press, New York.CrossRefGoogle Scholar
Bowman, G. R., Beauchamp, K. A., Boxer, G. & Pande, V. S. (2009). Progress and challenges in the automated construction of Markov state models for full protein systems. Journal of Chemical Physics 131, 124101.Google Scholar
Boyd, D. B. (1998). Rational drug design: controlling the size of the Haystack. Modern Drug Discovery 1, 41–47.Google Scholar
Brady, S. F., Stauffer, K. J., Lumma, W. C., Smith, G. M., Ramjit, H. G., Lewis, S. D., Lucas, B. J., Gardell, S. J., Lyle, E. A., Appleby, S. D., Cook, J. J., Holahan, M. A., Stranieri, M. T., Lynch, J. J. Jr., Lin, J. H., Chen, I.-W., Vastag, K., Naylor-Olsen, A. M. & Vacca, J. P. (1998). Discovery and development of the novel potent orally active thrombin inhibitor N-(9-hydroxy-9-fluorenecarboxy)prolyl trans-4-aminocyclohexylmethyl amide (L-372,460): co-application of structure-based design and rapid multiple analogue synthesis on solid support. Journal of Medical Chemistry 41, 401–406.Google Scholar
Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S. & Karplus, M. (1983). CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry 4, 187–217.CrossRefGoogle Scholar
Burge, S., Parkinson, G. N., Hazel, P., Todd, A. K. & Neidle, S. (2006). Quadruplex DNA: sequence, topology and structure. Nucleic Acids Research 34, 5402–5415.CrossRefGoogle ScholarPubMed
Campbell, H., Parkinson, G. N., Reszka, A. P. & Neidle, S. (2008). Structural basis of DNA quadruplex recognition by an acridine drug. Journal of the American Chemical Society 130, 6722–6724.CrossRefGoogle ScholarPubMed
Case, D. A. (2002). Molecular dynamics and NMR spin relaxation in proteins. Accounts of Chemical Research 35, 325–331.CrossRefGoogle ScholarPubMed
Cate, J. H., Gooding, A. R., Podell, E., Zhou, K., Golden, B. L., Kundrot, C. E., Cech, T. R. & Doudna, J. A. (1996). Crystal structure of a group I ribozyme domain: principles of RNA packing. Science 273, 1678–1785.CrossRefGoogle ScholarPubMed
Cervantes, C. F., Markwick, P. R., Sue, S. C., McCammon, J. A., Dyson, H. J. & Komives, E. A. (2009). Functional dynamics of the folded ankyrin repeats of IκBα revealed by nuclear magnetic resonance. Biochemistry 48, 8023–8031.CrossRefGoogle Scholar
Chen, J. & Brooks, C. L. III, (2008). Implicit modeling of nonpolar solvation for simulating protein folding and conformational transitions. Physical Chemistry Chemical Physics 10, 471–481.CrossRefGoogle ScholarPubMed
Chennamsetty, N., Voynov, V., Kayser, V., Helk, B. & Trout, B. L. (2009). Design of therapeutic proteins with enhanced stability. Proceedings of the National Academy of Sciences, USA 106, 11937–11942.CrossRefGoogle ScholarPubMed
Collins, F. S. (2010). Opportunities for research and NIH. Science 327, 36–37.CrossRefGoogle ScholarPubMed
Collins, J. R., Burt, S. K. & Erickson, J. W. (1995). Flap opening in HIV-1 protease simulated by activated' molecular dynamics. Nature Structural and Molecular Biology 2, 334–338.Google Scholar
Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z. & Players, F. (2010). Predicting protein structures with a multiplayer online game. Nature 466, 756–760.CrossRefGoogle ScholarPubMed
Csermely, P., Agoston, V. & Pongor, S. (2005). The efficiency of multi-target drugs: The network approach might help drug design. Trends in Pharmacological Sciences 26, 178–182.CrossRefGoogle Scholar
Darden, T., York, D. & Pedersen, L. (1993). Particle mesh Ewald: an N log(N) method for Ewald sums in large systems. Journal of Chemical Physics 98, 10089–10092.CrossRefGoogle Scholar
Daura, X., Gademann, K., Jaun, B., Seebach, D., Gunsteren, W. F. V. & Mark, A. (1999). Peptide folding: when simulation meets experiment. Angewandte Chemie (International edition in English) 38, 236–240.3.0.CO;2-M>CrossRefGoogle Scholar
Daura, X., Jaun, B., Seebach, D., Gunsteren, W. F. V. & Mark, A. (1998). Reversible peptide folding in solution by molecular dynamics simulation. Journal of Molecular Biology 280, 925–932.CrossRefGoogle ScholarPubMed
Davey, C. A., Sargent, D. F., Luger, K., Mäeder, A. W. & Richmond, T. J. (2002). Solvent mediated interactions in the structure of the nucleosome core particle at 1.9 Å resolution. Journal of Molecular Biology 319, 1097–1113.CrossRefGoogle ScholarPubMed
Day, R., Paschek, D. & Garcia, A. E. (2010). Microsecond simulations of the folding/unfolding thermodynamics of the Trp-cage miniprotein. Proteins 78, 1889–1899.CrossRefGoogle ScholarPubMed
de Laplace, P. S. (1820). Oeuvres complètes de Laplace. In Théorie Analytique des Probabilités, vol. VII, 3rd edn.Paris, France: Gauthier-Villars.Google Scholar
Dellago, C. & Bolhuis, P. G. (2007). Transition path sampling simulations of biological systems. Topics in Current Chemistry 268, 291–317.CrossRefGoogle Scholar
Dill, K. A., Ozkan, S. B., Shell, M. S. & Weikl, T. R. (2008). The protein folding problem. Annual Reviews of Biophysics 37, 289–316.CrossRefGoogle ScholarPubMed
Dirac, P. A. M. (1929). Quantum mechanics of many-electron systems. Proceedings of the Royal Society of London A 123, 714–733.Google Scholar
DOE (2009). Opportunities in Biology at the Extreme Scale of Computing. http://www.er.doe.gov/ascr/ProgramDocuments/Docs/BiologyReport.pdf.Google Scholar
Dooley, A. J., Shindo, N., Taggart, B., Park, J. G. & Pang, Y. P. (2006). From genome to drug lead: Identification of a small-molecule inhibitor of the SARS virus. Bioorganic and Medicinal Chemistry Letters 16, 830–833.Google Scholar
Dorigo, B., Schalch, T., Kulangara, A., Duda, S., Schroeder, R. R. & Richmond, T. J. (2004). Nucleosome arrays reveal the two-start organization of the chromatin fiber. Science 306, 1571–1573.CrossRefGoogle ScholarPubMed
Drie, J. H. V. (2007). Computer-aided drug design: The next 20 years. Journal of Computer-Aided Molecular Design 21, 591–601.Google Scholar
Dror, R. O., Arlow, D. H., Borhani, D. W., Jensen, M. Ø., Piana, S. & Shaw, D. E. (2009). Identification of two distinct inactive conformations of the 2-adrenergic receptor reconciles structural and biochemical observations. Proceedings of the National Academy of Sciences, USA 106, 4689–4694.CrossRefGoogle ScholarPubMed
Duan, Y. & Kollman, P. A. (1998). Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 282, 740–744.CrossRefGoogle Scholar
Duan, Y., Kollman, P. A. & Harvey, S. C. (2000). Protein folding and beyond. In Chemistry for the 21st Century (eds. Keinam, E. & Schechter, I.). Weinheim, Germany: Wiley-VCH.Google Scholar
Duan, Y., Wang, L. & Kollman, P. A. (1998). The early stage of folding of Villin headpiece subdomain observed in a 200-nanosecond fully solvate molecular dynamics simulation. Proceedings of the National Academy of Sciences, USA 95, 9897–9902.CrossRefGoogle Scholar
Duan, Z.-H. & Krasny, R. (2000). An Ewald summation based multipole method. Journal of Chemical Physics 113, 3492–3495.Google Scholar
Earl, D. J. & Deem, M. W. (2008). Monte Carlo simulations. Methods in Molecular Biology 443, 25–36.CrossRefGoogle ScholarPubMed
Economist (1998). The 1998 Nobel Prizes. 15 October, p. 97.Google Scholar
Ensign, D. L., Kasson, P. M. & Pande, V. S. (2007). Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the Villin headpiece. Journal of Molecular Biology 374, 806–816.Google Scholar
Ensign, D. L. & Pande, V. S. (2009). The Fip35 WW domain folds with structural and mechanistic heterogeneity in molecular dynamics simulations. Biophysics Journal 96, L53–L55.CrossRefGoogle ScholarPubMed
Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H. & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. Journal of Chemical Physics 103, 8577–8593.CrossRefGoogle Scholar
Faraldo-Gomez, J. & Roux, B. (2007). On the importance of a funneled energy landscape for the assembly and regulation of multidomain Src tyrosine kinases. Proceedings of the National Academy of Sciences, USA 104, 13643–13648.Google Scholar
Felsenfeld, G. & Groudine, M. (2003). Controlling the double helix. Nature 421, 448–453.CrossRefGoogle ScholarPubMed
Finch, J. T., Lutter, L. C., Rhodes, D., Brown, A. S., Rushton, B., Levitt, M. & Klug, A. (1977). Structure of nucleosome core particles of chromatin. Nature 269, 29–36.Google Scholar
Fitch, B. G., Rayshubskiy, A., Eleftheriou, M., Ward, T. J. C., Giampapa, M., Pitman, M. C. & Germain, R. S. (2006). Blue matter: approaching the limits of concurrency for classical molecular dynamics. In Supercomputing, 2006. SC'06. Proceedings of the ACM/IEEE SC 2006 Conference, p. 44.CrossRefGoogle Scholar
Foley, M. & Schlick, T. (2009). The relationship between conformational changes in Pol λ's active site upon binding incorrect nucleotides and mismatch incorporation rates. Journal of Physical Chemistry B 113, 13035–13047.CrossRefGoogle ScholarPubMed
Frauenfelder, H., Sligar, S. G. & Wolynes, P. G. (1991). The energy landscapes and motions of proteins. Science 254, 1598–1603.CrossRefGoogle ScholarPubMed
Freddolino, P. L., Arkhipov, A. S., Larson, S. B., McPherson, A. & Schulten, K. (2006). Molecular dynamics simulations of the complete satellite tobacco mosaic virus. Structure 14, 437–449.Google Scholar
Freddolino, P. L., Liu, F., Gruebele, M. & Schulten, K. (2008). Ten-microsecond molecular dynamics simulation of a fast-folding WW domain. Biophysics Journal 94, L75–L77.CrossRefGoogle ScholarPubMed
Freddolino, P. L., Park, S., Roux, B. & Schulten, K. (2009). Force field bias in protein folding simulations. Biophysics Journal 96, 3772–3780.Google Scholar
Freddolino, P. L. & Schulten, K. (2009). Common structural transitions in explicit-solvent simulations of villin headpiece folding. Biophysics Journal 97, 2338–2347.CrossRefGoogle ScholarPubMed
Gan, H. H., Perlow, R. A., Roy, S., Ko, J., Wu, M., Huang, J., Yan, S., Nicoletta, A., Vafai, J., Sun, D., Wang, L., Noah, J. E., Pasquali, S. & Schlick, T. (2002). Analysis of protein sequence/structure similarity relationships. Biophysics Journal 83, 2781–2791.Google Scholar
Garcia, A. E. & Pascheck, D. (2008). Simulation of the pressure and temperature folding/unfolding equilibrium of a small RNA hairpin. Journal of the American Chemical Society 130, 815–817.CrossRefGoogle ScholarPubMed
Ghosh, S., Nie, A., An, J. & Huang, Z. (2006). Structure-based virtual screening of chemical libraries for drug discovery. Current Opinion in Chemical Biology 10, 194–202.Google Scholar
Goldgur, Y., Craigie, R., Cohen, G. H., Fujiwara, T., Yoshinaga, T., Fujishita, T., Sugimoto, H., Endo, T., Murai, H. & Davies, D. R. (1999). Structure of the HIV-1 integrase catalytic domain complexed with an inhibitor: A platform for antiviral drug design. Proceedings of the National Academy of Sciences, USA 96, 13040–13043.Google Scholar
Goldschmidt, L., Teng, P. K., Riek, R. & Eisenberg, D. (2010). Identifying the amylome, proteins capable of forming amyloid-like fibrils. Proceedings of the National Academy of Sciences, USA 107, 3487–3492.Google Scholar
Golosov, A. A., Warren, J. J., Beese, L. S. & Karplus, M. (2010). The mechanism of the translocation step in DNA replication by DNA polymerase I: a computer simulation. Structure 18, 83–93.Google Scholar
Grant, B. J., Gorfe, A. A. & McCammon, J. A. (2010). Large conformational changes in proteins: signaling and other functions. Current Opinion in Structural Biology 20, 142–147.Google Scholar
Greengard, L. & Rokhlin, V. (1987). A fast algorithm for particle simulation. Journal of Computational Physics 73, 325–348.CrossRefGoogle Scholar
Greengard, L. & Rokhlin, V. (1997). A new version of the fast multipole method for the Laplace equation in three dimensions. Acta Numerica 6, 229–269.Google Scholar
Grigoryev, S. A., Arya, G., Correll, S., Woodcock, C. L. & Schlick, T. (2009). Evidence for heteromorphic chromatin fibers from analysis of nucleosome interactions. Proceedings of the National Academy of Sciences, USA 106, 13317–13322.CrossRefGoogle ScholarPubMed
Grossfield, A., Pitman, M. C., Feller, S. E., Soubias, O. & Gawrisch, K. (2008). Internal hydration increases during activation of the G-protein-coupled receptor rhodopsin. Journal of Molecular Biology 381, 478–486.CrossRefGoogle ScholarPubMed
Haider, S., Parkinson, G. N. & Neidle, S. (2008). Molecular dynamics and principal components analysis of human telomeric quadruplex multimers. Biophysics Journal 95, 296–311.Google Scholar
Haider, S. M. & Neidle, S. (2009). A molecular model for drug binding to tandem repeats of telomeric G-quadruplexes. Biochemical Society Transactions 37, 583–588.CrossRefGoogle ScholarPubMed
Halgren, T. A. (1999). MMFF VII. Characterization of MMFF94, MMFF94s, and other widely available force fields for conformational energies and for interaction energies and geometries. Journal of Computational Chemistry 20, 730–748.3.0.CO;2-T>CrossRefGoogle ScholarPubMed
Hamelberg, D. & McCammon, J. A. (2005). Fast peptidyl cis–trans isomerization within the flexible gly-rich flaps of HIV-1 protease. Journal of the American Chemical Society 127, 13778–13779.Google Scholar
Hamelberg, D., Mongan, J. & McCammon, J. A. (2004). Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. Journal of Chemical Physics 120, 11919–11929.Google Scholar
Hare, S., Gupta, S. S., Valkov, E., Engelman, A. & Cherepanov, P. (2010). Retroviral intasome assembly and inhibition of DNA strand transfer. Nature 464, 232–236.Google Scholar
Hayden, E. C. (2010). Life is complicated. Nature 464, 664–667.CrossRefGoogle Scholar
Hazuda, D. J., Anthony, N. J., Gomez, R. P., Jolly, S. M., Wai, J. S., Zhuang, L., Fisher, T. E., Embrey, M., Guare, J. P. Jr., Egbertson, M. S., Vacca, J. P., Huff, J. R., Felock, P. J., Witmer, M. V., Stillmock, K. A., Danovich, R., Grobler, J., Miller, M. D., Espeseth, A. S., Jin, L., Chen, I. W., Lin, J. H., Kassahun, K., Ellis, J. D., Wong, B. K., Xu, W., Pearson, P. G., Schleif, W. A., Cortese, R., Emini, E., Summa, V., Holloway, M. K. & Young, S. D. (2004). A naphthyridine carboxamide provides evidence for discordant resistance between mechanistically identical inhibitors of HIV-1 integrase. Proceedings of the National Academy of Sciences, USA 101, 11233–11238.Google Scholar
Henzler-Wildman, K. A., Thai, V., Lei, M., Ott, M., Wolf-Watz, M., Fenn, T., Pozharski, E., Wilson, M. A., Petsko, G. A. & Karplus, M. (2007). Intrinsic motions along an enzymatic reaction trajectory. Nature 450, 838–844.Google Scholar
Hess, B., Kutzner, C., van der Spoel, D. & Lindahl, E. (2008). GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation 4, 435–447.CrossRefGoogle ScholarPubMed
Hornak, V., Okur, A., Rizzo, R. C. & Simmerling, C. (2006). HIV-1 protease flaps spontaneously open and reclose in molecular dynamics simulations. Proceedings of the National Academy of Sciences, USA 103, 915–920.Google Scholar
Hornak, V. & Simmerling, C. (2007). Targeting structural flexibility in HIV-1 protease inhibitor binding. Drug Discovery Today 12, 132–138.CrossRefGoogle ScholarPubMed
Hu, H., Elstner, M. & Hermans, J. (2003). Comparison of a QM/MM force field and molecular mechanics force fields in simulations of alanine and glycine ‘Dipeptides’ (Ace-Ala-Nme and Ace-Gly-Nme) in water in relation to the problem of modeling the unfolded peptide backbone in solution. Proteins: Structure, Function, and Genetics 50, 451–463.CrossRefGoogle Scholar
IMAG (2009). IMAG Futures Meeting: The Impact of Modeling on Biomedical Research. http://www.imagwiki.org/mediawiki/index.php?title=IFM_Agenda.Google Scholar
Izrailev, S., Crofts, A. R., Berry, E. A. & Schulten, K. (1999). Steered molecular dynamics simulation of the Rieske subunit motion in the cytochrome bc 1 complex. Biophysics Journal 77, 1753–1768.Google Scholar
Jack, A. & Levitt, M. (1978). Refinement of large structures by simultaneous minimization of energy and R factor. Acta Crystallographica A 34, 931–935.CrossRefGoogle Scholar
Jiang, L., Althoff, E. A., Clemente, F. R., Doyle, L., Röthlisberger, D., Zanghellini, A., Gallaher, J. L., Betker, J. L., Tanaka, F., Barbas, C. F. III, Hilvert, D., Houk, K. N., Stoddard, B. L. & Baker, D. (2008). De novo computational design of retro-aldol enzymes. Science 319, 1387–1391.Google Scholar
Jorgensen, W. L., Chandrasekar, J., Madura, J., Impey, R. & Klein, M. (1983). Comparison of simple potential functions for simulating liquid water. Journal of Chemical Physics 79, 926–935.CrossRefGoogle Scholar
Kamerlin, S. C. L., Haranczyk, M. & Warshel, A. (2009). Progress in ab initio QM/MM free-energy simulations of electrostatic energies in proteins: accelerated QM/MM studies of pK, redox reactions and solvation free energies. Journal of Physical Chemistry B 113, 1253–1272.CrossRefGoogle Scholar
Karplus, M. & Kuriyan, J. (2005). Molecular dynamics and protein function. Proceedings of the National Academy of Sciences, USA 102, 6679–6685.Google Scholar
Kaur, H., Garg, A. & Raghava, G. P. S. (2007). PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. Protein and Peptide Letters 14, 626–631.Google Scholar
Kelley, N. W., Huang, X., Tam, S., Spiess, C., Frydman, J. & Pande, V. S. (2009). The predicted structure of the headpiece of the Huntingtin protein and its implications on Huntingtin aggregation. Journal of Molecular Biology 388, 919–927.Google Scholar
Khelashvili, G., Grossfield, A., Feller, S. E., Pitman, M. C. & Weinstein, H. (2009). Structural and dynamic effects of cholesterol at preferred sites of interaction with Rhodopsin identified from microsecond length molecular dynamics simulations. Proteins 76, 403–417.CrossRefGoogle ScholarPubMed
Kim, D. E., Chivian, D. & Baker, D. (2004a). Protein structure prediction and analysis using the Robetta server. Nucleic Acids Research 32, W526–W531.CrossRefGoogle ScholarPubMed
Kim, N., Izzo, J. A. & Schlick, T. (2010). RNAs with Novel Technologies: Predictions and Confirmations, Submitted.Google Scholar
Kim, N., Shiffeldrim, N., Gan, H. & Schlick, T. (2004b). Candidates for novel RNA topologies. Journal of Molecular Biology 341, 1129–1144.CrossRefGoogle ScholarPubMed
Kitano, H. (2007). A robustness-based approach to systems-oriented drug design. Nature Reviews Drug Discovery 6, 202–210.Google Scholar
Klein, M. L. & Shinoda, W. (2008). Large-scale molecular dynamics simulations of self-assembling systems. Science 321, 798–800.CrossRefGoogle ScholarPubMed
Konnert, J. H. & Hendrickson, W. A. (1980). A restrained-parameter thermal-factor refinement procedure. Acta Crystallographica A 36, 344–350.Google Scholar
Kornberg, R. & Thomas, J. O. (1974). Chromatin structure: oligomers of histones. Science 184, 865–868.Google Scholar
Kruithof, M., Chen, F.-T., Routh, A., Logie, C., Rhodes, D. & van Noort, J. (2009). Single-molecule force microscopy reveals a highly compliant Helical folding for the 30-nm chromatin fiber. Nature Structural and Molecular Biology 16, 534–540.CrossRefGoogle Scholar
Kryshtafovych, A., Fidelis, K. & Moult, J. (2009). CASP8 results in context of previous experiments. Proteins: Structure, Function, and Genetics Supplement 9, 217–228.Google Scholar
Labuda, L. P., Pushechnikov, A. & Disney, M. D. (2009). Small molecule microarrays of RNA-focused peptoids help identify inhibitors of a pathogenic group I intron. ACS Chemical Biology 4, 299–307.Google Scholar
Lee, E. H., Hsin, J., Sotomayor, M., Comellas, G. & Schulten, K. (2009). Discovery through the computational microscope. Structure 17, 1295–1306.Google Scholar
Lei, H. & Duan, Y. (2007). Improved sampling methods for molecular simulation. Current Opinion in Structural Biology 17, 187–191.CrossRefGoogle ScholarPubMed
Leimkuhler, B. & Reich, S. (2004). Simulating Hamiltonian dynamics (Cambridge Monographs on Applied and Computational Mathematics). Cambridge, UK: Cambridge University Press.Google Scholar
Lin, J. H., Perryman, A. L., Schames, J. R. & McCammon, J. A. (2002). Computational drug design accommodating receptor flexibility: the relaxed complex scheme. Journal of the American Chemical Society 124, 5632–5633.CrossRefGoogle ScholarPubMed
Lindahl, E., Hess, B. & van der Spoel, D. (2001). GROMACS 3.0: A package for molecular simulation and trajectory analysis. Journal of Molecular Modeling 7, 306–317.Google Scholar
Liwo, A., Czaplewski, C., Oldziej, S. & Scheraga, H. A. (2008). Computational techniques for efficient conformational sampling of proteins. Current Opinion in Structural Biology 18, 134–139.Google Scholar
Luger, K., Mäder, A. W., Richmond, R. K., Sargent, D. F. & Richmond, T. J. (1997). Crystal structure of the nucleosome core particle at 2·8 Å resolution. Nature 389, 251–260.Google Scholar
MacCallum, J. L., Hua, L., Schnieders, M. J., Pande, V. S., Jacobson, M. P. & Dill, K. A. (2009). Assessment of the protein-structure refinement category in CASP8. Proteins 77 (Suppl. 9), 66–80.Google Scholar
Maddox, J. (1989). Statistical mechanics by numbers. Nature 334, 561.CrossRefGoogle Scholar
Maisuradze, G. G., Senet, P., Czaplewski, C., Liwo, A. & Scheraga, H. A. (2010). Investigation of protein folding by coarse-grained molecular dynamics with the UNRES force field. Journal of Physical Chemistry A 114, 4471–4485.Google Scholar
Mandziuk, M. & Schlick, T. (1995). Resonance in the dynamics of chemical systems simulated by the implicit-midpoint scheme. Chemical Physics Letters 237, 525–535.CrossRefGoogle Scholar
Maragakis, P., Lindorff-Larsen, K., Eastwood, M. P., Dror, R. O., Klepeis, J. L., Arkin, I. T., Jensen, M. Ø., Xu, H., Trbovic, N., Friesner, R. A., Palmer, A. G. III & Shaw, D. E. (2008). Microsecond molecular dynamics simulation shows effect of slow loop dynamics on backbone amide order parameter of proteins. Journal of Physical Chemistry B 112, 6155–6158.Google Scholar
Markwick, P. R., Bouvignies, G., Salmon, L., McCammon, J. A., Nilges, M. & Blackledge, M. (2009). Toward a unified representation of protein structural dynamics in solution. Journal of the American Chemical Society 131, 16968–16975.Google Scholar
Markwick, P. R., Cervantes, C. F., Abel, B. L., Komives, E. A., Blackledge, M. & McCammon, J. A. (2010). Enhanced conformational space sampling improves the prediction of chemical shifts in proteins. Journal of the American Chemical Society 132, 1220–1221.CrossRefGoogle ScholarPubMed
Maupetit, J., Derreumaux, P. & Tufféry, P. (2009). A fast method for large-scale de novo peptide and miniprotein structure prediction. Journal of Computational Chemistry 31, 726–738.Google Scholar
Mayor, U., Guydosh, N. R., Johnson, C. M., Grossmann, J. G., Sato, S., Jas, G. S., Freund, S. M., Alonso, D. O., Daggett, V. & Fersht, A. R. (2003). The complete folding pathway of a protein from nanoseconds to microseconds. Nature 421, 863–867.Google Scholar
McCammon, J. A., Gelin, B. R. & Karplus, M. (1977). Dynamics of folded proteins. Nature 267, 585–590.CrossRefGoogle ScholarPubMed
Michel, F. & Westhof, E. (1990). Modelling of the three-dimensional architecture of group I catalytic introns based on comparative sequence analysis. Journal of Molecular Biology 216, 585–610.Google Scholar
Miller, M., Jaskolski, M., Rao, J. K., Leis, J. & Wlodawer, A. (1989). Crystal structure of a retroviral protease proves relationship to aspartic protease family. Nature 337, 576–579.Google Scholar
Mittal, J. & Best, R. B. (2010). Tackling force-field bias in protein folding simulations: folding of Villin HP35 and Pin WW domains in explicit water. Biophysics Journal 99, L26–L28.Google Scholar
Morrone, J. A., Zhou, R. & Berne, B. J. (2010). Molecular dynamics with multiple time scales: How to avoid pitfalls. Journal of Chemical Theory and Computation 6, 1798–1804.Google Scholar
Mowery, D. C., Nelson, R. R., Sampat, B. N. & Ziedonis, A. A. (2004). Ivory Tower and Industrial Innovation: University-Industry Technology Transfer Before and After the Bayh–Dole Act. Stanford, CA: Stanford University Press.Google Scholar
Munos, B. (2009). Lessons from 60 years of pharmaceutical innovation. Nature Rev. 8, 959–968.Google Scholar
NAS (2009). A new biology for the 21st century: Ensuring the United States leads the coming biology revolution. http://www.nap.edu/catalog/12764.htmlGoogle Scholar
Navia, M. A., Fitzgerald, P. M., McKeever, B. M., Leu, C. T., Heimbach, J. C., Herber, W. K., Sigal, I. S., Darke, P. L. & Springer, J. P. (1989). Three-dimensional structure of aspartyl protease from human immunodeficiency virus HIV-1. Nature 337, 615–620.Google Scholar
Neidigh, J. W., Fesinmeyer, R. M. & Andersen, N. H. (2002). Designing a 20-residue protein. Nature Structural and Molecular Biology 9, 425–430.Google Scholar
Neidle, S. & Parkinson, G. (2002). Telomere maintenance as a target for anticancer drug discovery. Nature 1, 383–392.Google Scholar
Neidle, S., Read, M., Harrison, J., Romagnoli, B., Tanious, F., Gowan, S., Reszka, A., Wilson, D. & Kelland, L. (2001). Structure-based design of selective and potent G quadruplex-mediated telomerase inhibitors. Proceedings of the National Academy of Sciences, USA 98, 4844–4849.Google Scholar
Nicholls, A. (2010). Computational Biology and Bioinformatics Seminar, Columbia University Medical Center.Google Scholar
Noé, F. & Fischer, S. (2008). Transition networks for modeling the kinetics of conformational change in macromolecules. Current Opinion in Structural Biology 8, 154–162.Google Scholar
Noé, F., Horenko, I., Schütte, C. & Smith, J. C. (2007). Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states. Journal of Chemical Physics 126, 155102.Google Scholar
Noé, F., Schutte, C., Vanden-Eijnden, E., Reich, L. & Weikl, T. R. (2009). Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations. Proceedings of the National Academy of Sciences, USA 106, 19011–19016.Google Scholar
O'Donohue, M. F., Burgess, A. W., Treutlein, H. R. & Walkinshaw, M. D. (1995). Modeling conformational changes in cyclosporin A. Protein Science 4, 2191–2202.Google Scholar
Okumoto, S., Looger, L. L., Micheva, K. D., Reimer, R. J., Smith, S. J. & Frommer, W. B. (2005). Detection of glutamate release from neurons by genetically encoded surface-displayed FRET nanosensors. Proceedings of the National Academy of Sciences, USA 102, 8740–8745.CrossRefGoogle ScholarPubMed
Ozkan, S. B., Wu, G. A., Chodera, J. D. & Dill, K. A. (2007). Protein folding by zipping and assembly. Proceedings of the National Academy of Sciences, USA 104, 11987–11992.Google Scholar
Pan, A. C. & Roux, B. (2008). Building Markov state models along pathways to determine free energies and rates of transitions. Journal of Chemical Physics 129, 064107.Google Scholar
Pearl, L. H. & Taylor, W. R. (1987). A structural model for the retroviral proteases. Nature 329, 351–354.Google Scholar
Pearlman, D. A., Case, D. A., Caldwell, J. W., Ross, W. S., Cheatham, T. E. III, Debolt, S., Ferguson, D., David, S., Seibel, G. & Kollman, P. (1995). AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Computational Physics Communications 91, 1–41.Google Scholar
Pérez, A., Luque, J. & Orozco, M. (2007). Dynamics of B-DNA on the Microsecond time scale. Journal of the American Chemical Society 129, 14739–14745.CrossRefGoogle ScholarPubMed
Perryman, A. L., Forli, S., Morris, G. M., Burt, C., Cheng, Y., Palmer, M. J., Whitby, K., McCammon, J. A., Phillips, C. & Olson, A. J. (2010). A dynamic model of HIV integrase inhibition and drug resistance. Journal of Molecular Biology 397, 600–615.Google Scholar
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L. & Schulten, K. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry 26, 1781–1802.Google Scholar
Pitera, J. W., Swope, W. C. & Abraham, F. F. (2008). Observation of noncooperative folding thermodynamics in simulations of 1BBL. Biophysics Journal 94, 4837–4846.CrossRefGoogle ScholarPubMed
Pollack, A. (1998). Drug testers turn to ‘Virtual Patients’ as Guinea Pigs. The New York Times.Google Scholar
Ponder, J. W., Wu, C., Ren, P., Pande, V. S., Chodera, J. D., Schnieders, M. J., Haque, I., Mobley, D. L., Lambrecht, D. S., DiStasio, R. A. Jr., Head-Gordon, M., Clark, G. N. I., Johnson, M. E. & Head-Gordon, T. (2010). Current status of the AMOEBA polarizable force field. Journal of Physical Chemistry B 114, 2540–2564.Google Scholar
Price, D. J. & Brooks, C. L. III (2002). Modern protein force fields behave comparably in molecular dynamics simulations. Journal of Computational Chemistry 23, 1045–1057.Google Scholar
Procacci, P., Marchi, M. & Martyna, G. J. (1998). Electrostatic calculations and multiple time scales in molecular dynamics simulation of flexible molecular systems. Journal of Chemical Physics 108, 8799–8803.Google Scholar
Qian, X. & Schlick, T. (2002). Efficient multiple-timestep integrators with distance-based force splitting for particle-mesh-Ewald molecular dynamics simulations. Journal of Chemical Physics 116, 5971–5983.CrossRefGoogle Scholar
Radhakrishnan, R., Arora, K., Wang, Y., Beard, W. A., Wilson, S. H. & Schlick, T. (2006). Regulation of DNA repair fidelity by molecular checkpoints: ‘Gates’ in DNA polymerase β's substrate selection. Biochemistry 45, 15142–15156.Google Scholar
Radhakrishnan, R. & Schlick, T. (2004). Orchestration of cooperative events in DNA synthesis and repair mechanism unraveled by transition path sampling of DNA polymerase β's closing. Proceedings of the National Academy of Sciences, USA 101, 5970–5975.Google Scholar
Radhakrishnan, R. & Schlick, T. (2005). Fidelity discrimination in DNA polymerase β: differing closing profiles for a mismatched G:A versus matched G:C base pair. Journal of the American Chemical Society 127, 13245–13252.Google Scholar
Radhakrishnan, R. & Schlick, T. (2006). Correct and incorrect nucleotide incorporation pathways in DNA polymerase β's. Biochemical and Biophysics Research Communication 350, 521–529.Google Scholar
Rahman, A. & Stillinger, F. H. (1971). Molecular dynamics study of liquid water. Journal of Chemical Physics 55, 3336–3359.CrossRefGoogle Scholar
Rahman, A. & Stillinger, F. H. (1974). Improved simulation of liquid water by molecular dynamics. Journal of Chemical Physics 60, 1545–1557.Google Scholar
Raman, S., Vernon, R., Thompson, J., Tyka, M., Sadreyev, R., Pei, J., Kim, D., Kellogg, E., DiMaio, F., Lange, O., Kinch, L., Sheffler, W., Kim, B.-H., Das, R., Grishin, N. V. & Baker, D. (2009). Structure prediction for CASP8 with all-atom refinement using rosetta. Proteins Supplement 9, 89–99.Google Scholar
Robinson, P. J. J., Fairall, L., Huynh, V. A. T. & Rhodes, D. (2006). EM measurements define the dimensions of the ‘30-nm’ chromatin fiber: Evidence for a compact, interdigitated structure. Proceedings of the National Academy of Sciences, USA 103, 6506–6511.Google Scholar
Roe, D. R., Okur, A., Wickstrom, L., Hornak, V. & Simmerling, C. (2007). Secondary structure bias in generalized born solvent models: comparison of conformational ensembles and free energy of solvent polarization from explicit and implicit solvation. Journal of Physical Chemistry B 111, 1846–1857.Google Scholar
Roitberg, A. E., Okur, A. & Simmerling, C. (2007). Coupling of replica exchange simulations to a non-Boltzmann structure reservoir. Journal of Physical Chemistry B 111, 2415–2418.Google Scholar
Ryckaert, J. P., Ciccotti, G. & Berendsen, H. J. C. (1977). Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. Journal of Computational Physics 23, 327–341.Google Scholar
Saito, M. (1992). Molecular dynamics simulations of proteins in water without the truncation of long-range Coulomb interactions. Molecular Simulation 8, 321–333.Google Scholar
Sandak, B. (2001). Multiscale fast summation of long-range charge and dipolar interactions. Journal of Computational Chemistry 22, 717–731.Google Scholar
Schaefer, H. F. (1986). Methylene: A paradigm for computational quantum chemistry. Science 231, 1100–1107.Google Scholar
Schalch, T., Duda, S., Sargent, D. F. & Richmond, T. J. (2005). X-ray structure of a tetranucleosome and its implications for the chromatin fibre. Nature 436, 138–141.CrossRefGoogle ScholarPubMed
Schames, J. R., Henchman, R. H., Siegel, J. S., Sotriffer, C. A., Ni, H. & McCammon, J. A. (2004). Discovery of a novel binding trench in HIV integrase. Journal of Medical Chemistry 47, 1879–1881.Google Scholar
Schlick, T. (2009a). From macroscopic to mesoscopic models of chromatin folding. In Bridging The Scales in Science in Engineering (ed. Fish, J.), pp. 514–535. New York: Oxford University Press.Google Scholar
Schlick, T. (2009b). Monte Carlo, harmonic approximation, and coarse-graining approaches for enhanced sampling of biomolecular structure. F1000 Biology Reports 1, 48.Google Scholar
Schlick, T. (2009c). Molecular-dynamics based approaches for enhanced sampling of long-time, large-scale conformational changes in biomolecules. F1000 Biology Reports 1, 51.Google Scholar
Schlick, T. (2010). Molecular Odeling: An Interdisciplinary Guide, 2nd edn.New York: Springer-Verlag.Google Scholar
Schlick, T., Barth, E. & Mandziuk, M. (1997). Biomolecular dynamics at long timesteps: bridging the timescale gap between simulation and experimentation. Annual Review of Biophysics and Biomolecular Structure 26, 179–220.Google Scholar
Schlick, T., Mandziuk, M., Skeel, R. & Srinivas, K. (1998). Nonlinear resonance artifacts in molecular dynamics simulations. Journal of Computational Physics 139, 1–29.Google Scholar
Schlick, T. & Perisic, O. (2009). Mesoscale simulations of two nucleosome-repeat length oligonucleosomes. Physical Chemistry Chemical Physics 11, 10729–10737.Google Scholar
Schlick, T., Skeel, R. D., Brünger, A. T., Kalé, L. V., Board, J. A. Jr.Hermans, J. & Schulten, K. (1999). Algorithmic challenges in computational molecular biophysics. Journal of Computational Physics 151, 9–48.Google Scholar
Schnabel, J. (2010). The dark side of proteins. Nature 464, 828–829.Google Scholar
Schröder, G., Levitt, M. & Brunger, A. T. (2010). Super-resolution biomolecular crystallography with low-resolution data. Nature 464, 1218–1222.Google Scholar
Schwede, T., Sali, A., Honig, B., Levitt, M., Berman, H. M., Jones, D., Brenner, S. E., Burley, S. K., Das, R., Dokholyan, N. V., Dunbrack, R. L. Jr., Fidelis, K., Fiser, A., Godzik, A., Huang, Y. J., Humblet, C., Jacobson, M. P., Joachimiak, A., Krystek, S. R. Jr., Kortemme, T., Kryshtafovych, A., Montelione, G. T., Moult, J., Murray, D., Sanchez, R., Sosnick, T. R., Standley, D. M., Stouch, T., Vajda, S., Vasquez, M., Westbrook, J. D. & Wilson, I. A. (2009). Outcome of a workshop on applications of protein models in biomedical research. Structure 17, 151–159.Google Scholar
Science.aily (2008). Computer Game's High Score Could Earn The Nobel Prize In Medicine. 9 May.Google Scholar
Scott, W. R. & Schiffer, C. A. (2000). Curling of flap tips in HIV-1 protease as a mechanism for substrate entry and tolerance of drug resistance. Structure 8, 1259–1265.CrossRefGoogle ScholarPubMed
Shaw, D. E., Deneroff, M. M., Dror, R. O., Kuskin, J. S., Larson, R. H., Salmon, J. K., Young, C., Batson, B., Bowers, K. J., Chao, J. C., Eastwood, M. P., Gagliardo, J., Grossman, J., Ho, C. R., Ierardi, D. J., Kolossváry, I., Klepeis, J. L., Layman, T., McLeavey, C., Moraes, M. A., Mueller, R., Priest, E. C., Shan, Y., Spengler, J., Theobald, M., Towles, B. & Wang, S. C. (2007). Anton: a special-purpose machine for molecular dynamics simulation. In Proceedings of the 34th annual international symposium on Computer architecture, pp. 1–12, ACM, San Diego, CA.CrossRefGoogle Scholar
Shaw, D. E., Dror, R. O., Salmon, J. K., Grossman, J. P., Mackenzie, K. M., Bank, J. A., Young, C., Deneroff, M. M., Batson, B., Bowers, K. J., Chow, E., Eastwood, M. P., Ierardi, D. J., Klepeis, J. L., Kuskin, J. S., Larson, R. H., Lindorff-Larsen, K., Maragakis, P., Moraes, M. A., Piana, S., Shan, Y. & Towles, B. (2009). Millisecond-scale molecular dynamics simulations on anton. In SC ‘09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, pp. 1–11, ACM, San Diego, CA.Google Scholar
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y. & Wriggers, W. (2010). Atomic-level characterization of the structural dynamics of proteins. Science 330, 341–346.Google Scholar
Sheffler, W. & Baker, D. (2008). RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation. Protein Science 18, 229–239.Google Scholar
Shirts, M. & Pande, V. (2000). Screen savers of the World Unite! Science 290, 1903–1904.Google Scholar
Simmerling, C., Strockbine, B. & Roitberg, A. E. (2002). All-atom structure prediction and folding simulations of a stable protein. Journal of the American Chemical Society 124, 11258–11259.Google Scholar
Skeel, R. D., Tezcan, I. & Hardy, D. J. (2002). Multiple grid methods for classical molecular dynamics. Journal of Computational Chemistry 23, 673–684.Google Scholar
Snir, M. (2004). A note on N-body computations with cutoffs. Theory of Computing Systems 37, 295–318.CrossRefGoogle Scholar
Snow, C. D., Nguyen, H., Pande, V. S. & Gruebele, M. (2002). Absolute comparison of simulated and experimental protein folding dynamics. Nature 420, 102–106.Google Scholar
Stehr, R., Kepper, N., Rippe, K. & Wedemann, G. (2008). The effect of the internucleosomal interaction potential on the folding of the chromatin fiber. Biophysics Journal 95, 3677–3691.Google Scholar
Stuart, S. J., Zhou, R. & Berne, B. J. (1996). Molecular dynamics with multiple time scales: the selection of efficient reference system propagators. Journal of Chemical Physics 105, 1426–1436.Google Scholar
Sugita, Y. & Okamoto, Y. (1999). Replica-exchange molecular dynamics methods for protein folding. Chemical Physics Letters 314, 141–151.Google Scholar
Sun, J., Zhang, Q. & Schlick, T. (2005). Electrostatic mechanism of nucleosomal array folding revealed by computer simulation. Proceedings of the National Academy of Sciences, USA 102, 8180–8185.Google Scholar
Sweet, C. R., Petrine, P., Pande, V. S. & Izaguirre, J. A. (2008). Normal mode partitioning of Langevin dynamics for biomolecules. Journal of Chemical Physics 128, 145101.Google Scholar
Tanizaki, S., Clifford, J., Connelly, B. D. & Feig, M. (2008). Conformational sampling of peptides in cellular environments. Biophysics Journal 94, 747–759.Google Scholar
Thomas, A., Deshayes, S., Decaffmeyer, M., Eyck, M. V., Charloteaux, B. & Brasseur, R. (2009). PepLook: An Innovative in Silico Tool for Determination of Structure, Polymorphism and Stability of Peptides. New York: Springer-Verlag.Google Scholar
Tozzini, V. & McCammon, J. A. (2005). A coarse grained model for the dynamics of flap opening in HIV-1 protease. Chemical Physics Letters 413, 123–128.CrossRefGoogle Scholar
Tremethick, D. J. (2007). Higher-order structures of chromatin: The elusive 30 nm fiber. Cell 128, 651–654.Google Scholar
Tsui, V., Radhakrishnan, I., Wright, P. E. & Case, D. A. (2000). NMR and molecular dynamics studies of the hydration of a zinc finger-DNA complex. Journal of Molecular Biology 302, 1101–1117.Google Scholar
van Holde, K. & Zlatanova, J. (2007). Chromatin fiber structure, where is the problem now? Seminars in Cell and Developmental Biology 18, 651–658.Google Scholar
Vasquez, V., Sotomayor, M., Cordero-Morales, J., Schulten, K. & Perozo, E. (2008). A structural mechanism for MscS gating in lipid bilayers. Science 321, 1210–1214.Google Scholar
Voelz, V. A., Bowman, G. R., Beauchamp, K. & Pande, V. S. (2010). Molecular simulation of Ab initio protein folding for a millisecond folder NTL9(1–39). Journal of the American Chemical Society 132, 1526–1528.Google Scholar
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. (2004). Development and testing of a general AMBER force field. Journal of Computational Chemistry 25, 1157–1174.Google Scholar
Wang, W., Donini, O., Reyes, C. M. & Kollman, P. A. (2001). Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annual Review of Biophysics and Biomolecular Structure 30, 211–243.Google Scholar
Warshel, A. & Levitt, M. (1976). Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of carbonium ion in the reaction of lysozyme. Journal of Molecular Biology 103, 227–249.Google Scholar
Warshel, A. & Russell, S. T. (1984). Calculations of electrostatic interactions in biological systems and in solutions. Quarterly Review of Biophysics 17, 283–422.Google Scholar
Warshel, A., Sharma, P. K., Kato, M., Xiang, Y., Liu, H. & Olson, M. H. M. (2006). Electrostatic basis for enzyme catalysis. Chemical Reviews 106, 3210–3235.Google Scholar
Washburn, J. (2005). University Inc.: The Corporate Corruption of Higher Education. New York: Basic Books.Google Scholar
Weber, I. T., Miller, M., Jaskolski, M., Leis, J., Skalka, A. M. & Wlodawer, A. (1989). Molecular modeling of the HIV-1 protease and its substrate binding site. Science 243, 928–931.Google Scholar
Wensley, B. G., Batey, S., Bone, F. A., Chan, Z. M., Tumelty, N. R., Steward, A., Kwa, L. G., Borgia, A. & Clarke, J. (2010). Experimental evidence for a frustrated energy landscape in a three-helix-bundle protein family. Nature 463, 685–688.Google Scholar
Wlodawer, A., Miller, M., Jaskolski, M., Sathyanarayana, B. K., Baldwin, E., Weber, I. T., Selk, L. M., Clawson, L., Schneider, J. & Kent, S. B. (1989). Conserved folding in retroviral proteases: crystal structure of a synthetic HIV-1 protease. Science 245, 616–621.Google Scholar
Wolynes, P. G. (2005). Recent successes of the energy landscape theory of protein folding and function. Quarterly Review of Biophysics 38, 405–410.Google Scholar
Wong, H., Victor, J.-M. & Mozziconacci, J. (2007). An all-atom model of the chromatin fiber containing linker histones reveals a versatile structure tuned by the nucleosomal repeat length. PLoS ONE 2, e877.Google Scholar
Yang, W. Y. & Grueble, M. (2003). Folding at the speed limit. Nature 423, 193–197.CrossRefGoogle ScholarPubMed
York, D. & Yang, W. (1994). The fast Fourier Poisson method for calculating Ewald sums. Journal of Chemical Physics 101, 3298–3300.CrossRefGoogle Scholar
Young, M. A. & Beveridge, D. L. (1998). Molecular dynamics simulations of an Oligonucleotide duplex with adenine tracts phased by a full helix turn. Journal of Molecular Biology 281, 675–687.Google Scholar
Zagrovic, B., Sorin, E. J. & Pande, V. (2001). β-hairpin folding simulations in atomistic detail using an implicit solvent model. Journal of Molecular Biology 313, 151–169.Google Scholar
Zhang, Y. (2008). Progress and challenges in protein structure prediction. Current Opinion in Structural Biology 18, 342–348.Google Scholar
Zhou, J. Z. (2008). Structure-directed combinatorial library design. Current Opinion in Chemical Biology 12, 379–385.Google Scholar
Zhou, R., Harder, E., Xu, H. & Berne, B. J. (2001). Efficient multiple time step method for use with Ewald and particle mesh Ewald for large biomolecular systems. Journal of Chemical Physics 115, 2348–2358.Google Scholar