References
- Dehsorkhi A, Castelletto V, Hamley IW. Self-assembling amphiphilic peptides. J. Pept. Sci. 2014;20:453–467. doi:10.1002/psc.2633.
- Caplan MR, Lauffenburger DA. Nature’s complex copolymers: engineering design of oligopeptide materials. Ind. Eng. Chem. Res. 2001;41:403–412. doi:10.1021/ie010149z.
- Segers VF, Lee RT. Local delivery of proteins and the use of self-assembling peptides. Drug Discov. Today. 2007;12:561–568. Available from: http://www.sciencedirect.com/science/article/pii/S135964460700219X.
- Subramani K, Ahmed W. Chapter 13 -- self-assembly of proteins and peptides and their applications in bionanotechnology and dentistry. In: Subramani K, Ahmed W,editors. Micro and nano technologies. Boston: William Andrew Publishing; 2012. p. 209–224. Available from: http://www.sciencedirect.com/science/article/pii/B9781455778621000134.
- Hosseinkhani H, Hong PD, Yu DS. Self-assembled proteins and peptides for regenerative medicine. Chem Rev. 2013;113:4837–4861. doi:10.1021/cr300131h.
- Palmer LC, Newcomb CJ, Kaltz SR, et al. Biomimetic systems for hydroxyapatite mineralization inspired by bone and enamel. Chem. Rev. 2008;108:4754–4783. doi:10.1021/cr8004422.
- Hartgerink JD, Beniash E, Stupp SI. Self-assembly and mineralization of peptide-amphiphile nanofibers. Science. 2001;294:1684–1688.
- Aggeli A, Bell M, Boden N, et al. Responsive gels formed by the spontaneous self-assembly of peptides into polymeric [β] -sheet tapes. Nature. 1997;386:259–262. doi:10.1038/386259a0.
- Gauthier MA, Klok HA. Peptide/protein-polymer conjugates: synthetic strategies and design concepts. Chem. Commun. 2008;23:2591–2611. doi:10.1039/B719689J.
- Zelzer M, Ulijn RV. Next-generation peptide nanomaterials: molecular networks, interfaces and supramolecular functionality. Chem. Soc. Rev. 2010;39:3351–3357. doi:10.1039/C0CS00035C.
- Lowik DWPM, Leunissen EHP, van den Heuvel M, et al. Stimulus responsive peptide based materials. Chem. Soc. Rev. 2010;39:3394–3412. doi:10.1039/B914342B.
- Tsonchev S, Schatz GC, Ratner MA. Electrostatically-directed self-assembly of cylindrical peptide amphiphile nanostructures. J. Phys. Chem. B. 2004;108:8817–8822. doi:10.1021/jp037731g.
- Wall BD, Zacca AE, Sanders AM, et al. Supramolecular polymorphism: Tunable electronic interactions within π-conjugated peptide nanostructures dictated by primary amino acid sequence. Langmuir. 2014;30:5946–5956.
- Burroughes JH, Bradley DDC, Brown AR, et al. Light-emitting diodes based on conjugated polymers. Nature. 1990;347:539–541. doi:10.1038/347539a0.
- Bian L, Zhu E, Tang J, et al. Recent progress in the design of narrow bandgap conjugated polymers for high-efficiency organic solar cells. Prog. Polym. Sci. 2012;37:1292–1331.http://www.sciencedirect.com/science/article/pii/S0079670012000354.
- Guo X, Baumgarten M, Müllen K. Designing π-conjugated polymers for organic electronics. Prog. Polym. Sci. 2013;38:1832–1908. Available from: http://www.sciencedirect.com/science/article/pii/S0079670013001196.
- Kim SH, Parquette JR. A model for the controlled assembly of semiconductor peptides. Nanoscale. 2012;4:6940–6947. doi:10.1039/C2NR32140H.
- Hoeben FJM, Jonkheijm P, Meijer EW, et al. About supramolecular assemblies of π-conjugated systems. Chem. Rev. 2005;105:1491–1546. doi:10.1021/cr030070z.
- White House Office of Science and Technology Policy. Materials genome initiative for global competitiveness. 2011. Available from: https://www.whitehouse.gov/sites/default/files/microsites/ostp/materials_genome_initiative-final.pdf
- Ardoña HAM, Besar K, Togninalli M, et al. Sequence-dependent mechanical, photophysical and electrical properties of pi-conjugated peptide hydrogelators. J. Mater. Chem. C. 2015;3:6505–6514. doi:10.1039/C5TC00100E.
- Wall BD, Zhou Y, Mei S, et al. Variation of formal hydrogen-bonding networks within electronically delocalized π-conjugated oligopeptide nanostructures. Langmuir. 2014;30:11375–11385. doi:10.1021/la501999g.
- Marciel AB, Tanyeri M, Wall BD, et al. Fluidic-directed assembly of aligned oligopeptides with π-conjugated cores. Adv. Mater. 2013;25:6398–6404. doi:10.1002/adma.201302496.
- Vadehra GS, Wall BD, Diegelmann SR, et al. On-resin dimerization incorporates a diverse array of [π]-conjugated functionality within aqueous self-assembling peptide backbones. Chem. Commun. 2010;46:3947–3949. doi:10.1039/C0CC00301H.
- Wall BD, Tovar JD. Synthesis and characterization of π-conjugated peptide-based supramolecular materials. Pure Appl. Chem. 2012;84:1039–1045. doi:10.1351/PAC-CON-11-10-24.
- Sanders AM, Dawidczyk TJ, Katz HE, et al. Peptide-based supramolecular semiconductor nanomaterials via Pd-catalyzed solid-phase “dimerizations”. ACS Macro Lett. 2012;1:1326–1329.
- Ardoña HAM, Tovar JD. Peptide π-electron conjugates: organic electronics for biology? Bioconjugate Chem. 2015;26:2290–2302. doi:10.1021/acs.bioconjchem.5b00497.
- Diegelmann SR, Gorham JM, Tovar JD. One-dimensional optoelectronic nanostructures derived from the aqueous self-assembly of π-conjugated oligopeptides. J. Am. Chem. Soc. 2008;130:13840–13841. doi:10.1021/ja805491d.
- Wall BD, Diegelmann SR, Zhang S, et al. Aligned macroscopic domains of optoelectronic nanostructures prepared via shear-flow assembly of peptide hydrogels. Adv. Mater. 2011;23:5009–5014. doi:10.1002/adma.201102963.
- Mondal J, Zhu X, Cui Q, et al. Self-assembly of β-peptides: Insight from the pair and many-body free energy of association. J. Phys. Chem. C. 2010;114:13551–13556. doi:10.1021/jp1040772.
- Hess B, Kutzner C, van der Spoel D, et al. Gromacs 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008;4:435–447. doi:10.1021/ct700301q.
- Schüttelkopf AW, van Aalten DMF. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2004;60:1355–1363. doi:10.1107/S0907444904011679.
- MacKerell AD, Banavali NK. All-atom empirical force field for nucleic acids: II. Application to molecular dynamics simulations of DNA and RNA in solution. J. Comput. Chem. 2000;21:105–120. doi:10.1002/(SICI)1096-987X(20000130)21:2<105:AID-JCC3>3.0.CO;2-P.
- Berendsen HJC, Postma JPM, van Gunsteren WF, et al. Intermolecular forces. Dordrecht: Reidel; 1981.
- Van Gunsteren WF, Berendsen HJC. A leap-frog algorithm for stochastic dynamics. Mol. Simul. 1988;1:173–185. doi:10.1080/08927028808080941.
- Goga N, Rzepiela AJ, de Vries AH, et al. Efficient algorithms for Langevin and DPD dynamics. J. Chem. Theory Comput. 2012;8:3637–3649. doi:10.1021/ct3000876.
- Hockney RW, Eastwood JW. Computer simulation using particles. New York (NY): Taylor and Francis; 1988.
- Hess B, Bekker H, Berendsen HJC, et al. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 1997;18:1463–1472. doi:10.1002/(SICI)1096-987X(199709)18:12<1463:AID-JCC4>3.0.CO;2-H.
- Essmann U, Perera L, Berkowitz ML, et al. A smooth particle mesh Ewald method. J. Chem. Phys. 1995;103:8577–8593. Available from: http://scitation.aip.org/content/aip/journal/jcp/103/19/10.1063/1.470117.
- Allen MP, Tildesley DJ. Computer simulations of liquids. Oxford: Oxford University Press; 1989.
- Still WC, Tempczyk A, Hawley RC, et al. Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc. 1990;112:6127–6129. doi:10.1021/ja00172a038.
- Constanciel R, Contreras R. Self consistent field theory of solvent effects representation by continuum models: introduction of desolvation contribution. Theor. Chim. Acta. 1984;65:1–11. doi:10.1007/PL00020119.
- Fernández DP, Mulev Y, Goodwin ARH, et al. A database for the static dielectric constant of water and steam. J. Phys. Chem. Ref. Data. 1995;24:33–70. Available from: http://scitation.aip.org/content/aip/journal/jpcrd/24/1/10.1063/1.555977.
- Onufriev A, Bashford D, Case DA. Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins: Struct. Funct. Bioinf. 2004;55:383–394. doi:10.1002/prot.20033.
- Schaefer M, Bartels C, Karplus M. Solution conformations and thermodynamics of structured peptides: molecular dynamics simulation with an implicit solvation model. J. Mol. Biol. 1998;284:835–848. Available from: http://www.sciencedirect.com/science/article/pii/S0022283698921726.
- Endres RG. Accelerating all-atom protein folding simulations through reduced dihedral barriers. Mol. Simul. 2005;31:773–777. doi:10.1080/08927020500266128.
- Pak Y, Kim E, Jang S. Misfolded free energy surface of a peptide with αββ motif (1PSV) using the generalized born solvation model. J. Chem. Phys. Available from: http://scitation.aip.org/content/aip/journal/jcp/121/18/10.1063/1.1804159.
- Roe DR, Okur A, Wickstrom L, et al. Secondary structure bias in generalized born solvent models: comparison of conformational ensembles and free energy of solvent polarization from explicit and implicit solvation. J. Phys. Chem. B. 2007;111:1846–1857. doi:10.1021/jp066831u.
- Lwin TZ, Zhou R, Luo R. Is Poisson--Boltzmann theory insufficient for protein folding simulations? J. Chem. Phys. 2006;124:034902. Available from: http://scitation.aip.org/content/aip/journal/jcp/124/3/10.1063/1.2161202.
- Yaşar F, Jiang P, Hansmann UH. Multicanonical molecular dynamics simulations of the N-terminal domain of protein L9. Europhys. Lett. 2014;105:30008. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4169893/.
- Andreotti G, Cabeza de Vaca I, Poziello A, et al. Conformational response to ligand binding in phosphomannomutase2: insights into inborn glycosylation disorder. J. Biol. Chem. 2014;289:34900–34910. Available from: http://www.jbc.org/content/289/50/34900.full.pdf+html; http://www.jbc.org/content/289/50/34900.abstract.
- Shell MS, Ritterson R, Dill KA. A test on peptide stability of AMBER force fields with implicit solvation. J. Phys. Chem. B. 2008;112:6878–6886. doi:10.1021/jp800282x.
- Bureau HR, Merz DR, Hershkovits E, et al. Constrained unfolding of a helical peptide: implicit versus explicit solvents. PLoS ONE. 2015;10:e0127034. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4430545/.
- Torrie G, Valleau J. Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J. Comput. Phys. 1977;23:187–199. Available from: http://www.sciencedirect.com/science/article/pii/0021999177901218.
- Hub JS, de Groot BL, van der Spoel D. g\_wham-a free weighted histogram analysis implementation including robust error and autocorrelation estimates. J. Chem. Theory Comput. 2010;6:3713–3720. doi:10.1021/ct100494z.
- Brice AR, Dominy BN. Examining electrostatic influences on base-flipping: a comparison of TIP3P and GB solvent models. Commun. Comput. Phys. 2013;13:223–237. Available from: http://www.global-sci.com/freedownload/v13\_223.pdf.
- Allnér O, Nilsson L, Villa A. Magnesium ion-water coordination and exchange in biomolecular simulations. J. Chem. Theory Comput. 2012;8:1493–1502.
- Wang L, Hingerty BE, Srinivasan A, et al. Accurate representation of b-dna double helical structure with implicit solvent and counterions. Biophys. J. 2002;83:382–406. Available from: http://www.sciencedirect.com/science/article/pii/S0006349502751771.
- Spaeth JR, Kevrekidis IG, Panagiotopoulos AZ. A comparison of implicit- and explicit-solvent simulations of self-assembly in block copolymer and solute systems. J. Chem. Phys. 2011;134:164902. Available from: http://scitation.aip.org/content/aip/journal/jcp/134/16/10.1063/1.3580293.
- Maffeo C, Ngo TTM, Ha T, et al. A coarse-grained model of unstructured single-stranded dna derived from atomistic simulation and single-molecule experiment. J. Chem. Theory Comput. 2014;10:2891–2896. doi:10.1021/ct500193u.
- Zhang LY, Gallicchio E, Friesner RA, et al. Solvent models for protein-ligand binding: Comparison of implicit solvent poisson and surface generalized Born models with explicit solvent simulations. J. Comput. Chem. 2001;22:591–607. doi:10.1002/jcc.1031.
- Villa A, Peter C, van der Vegt NFA. Self-assembling dipeptides: conformational sampling in solvent-free coarse-grained simulation. Phys. Chem. Chem. Phys. 2009;11:2077–2086. doi:10.1039/B818144F.
- Sanghi T, Aluru NR. Coarse-grained potential models for structural prediction of carbon dioxide (CO\textsubscript{2}) in confined environments. J. Chem. Phys. 2012;136:024102. Available from: http://scitation.aip.org/content/aip/journal/jcp/136/2/10.1063/1.3674979.
- Noid WG. Perspective: coarse-grained models for biomolecular systems. J. Chem. Phys. 2013;139:090901.
- Rasaiah JC. Chapter A2.3, Statistical mechanics of strongly interacting systems: liquids and solids. Encyclopedia of chemical physics and physical chemistry: fundamentals. Bristol: Institute of Physics Publishing; 2001.
- Chandler D. Interfaces and the driving force of hydrophobic assembly. Nature. 2005;437:640–647. doi:10.1038/nature04162.
- Lum K, Chandler D, Weeks JD. Hydrophobicity at small and large length scales. J. Phys. Chem. B. 1999;103:4570–4577. doi:10.1021/jp984327m.
- Athawale MV, Goel G, Ghosh T, et al. Effects of lengthscales and attractions on the collapse of hydrophobic polymers in water. Proc. Nat. Acad. Sci. 2007;104:733–738. Available from: http://www.pnas.org/content/104/3/733.full.pdf; http://www.pnas.org/content/104/3/733.abstract.
- Kumar R, Schmidt JR, Skinner JL. Hydrogen bonding definitions and dynamics in liquid water. J. Chem. Phys. 2007;126:204107. Available from: http://scitation.aip.org/content/aip/journal/jcp/126/20/10.1063/1.2742385.
- Hess B, van der Spoel D, Lindahl E. Gromacs user manual. 4th ed. Royal Institute of Technology and Uppsala Univerity; 2013. Available from: ftp://ftp.gromacs.org/pub/manual/manual-4.6.5.pdf.
- Baker N, Bashford D, Case D. Implicit solvent electrostatics in biomolecular simulation. In: Leimkuhler B, Chipot C, Elber R, Laaksonen A, Mark A, Schlick T, Skeel R,editors. New algorithms for macromolecular simulation. Vol. 49, Lecture notes in computational science and engineering. Berlin Heidelberg: Springer; 2006. p. 263–295. 10.1007/3-540-31618-3\_15.
- Groot RD. Electrostatic interactions in dissipative particle dynamics-simulation of polyelectrolytes and anionic surfactants. J. Chem. Phys. 2003;118:11265–11277. Available from: http://scitation.aip.org/content/aip/journal/jcp/118/24/10.1063/1.1574800.
- Groot R, Rabone K. Mesoscopic simulation of cell membrane damage, morphology change and rupture by nonionic surfactants. Biophys. J. 2001;81:725–736. doi:10.1016/S0006-3495(01)75737-2.
- Marrink SJ, de Vries AH, Mark AE. Coarse grained model for semiquantitative lipid simulations. J. Phys. Chem. B. 2004;108:750–760. doi:10.1021/jp036508g.
- Frenkel D, Smit B. Understanding molecular simulation: from algorithms to applications. 2nd ed. San Diego (CA) : Academic Press; 2001.
- Hu YF, Lv WJ, Zhao S, et al. Effect of surfactant SDS on DMSO transport across water/hexane interface by molecular dynamics simulation. Chem. Eng. Sci. 2015;134:813–822.http://www.sciencedirect.com/science/article/pii/S0009250915004121.
- Gereben O, Pusztai L. Investigation of the structure of ethanol-water mixtures by molecular dynamics simulation I: analyses concerning the hydrogen-bonded pairs. J. Phys. Chem. B. 2015;119:3070–3084. doi:10.1021/jp510490y.
- Basconi JE, Shirts MR. Effects of temperature control algorithms on transport properties and kinetics in molecular dynamics simulations. J. Chem. Theory Comput. 2013;9:2887–2899. doi:10.1021/ct400109a.
- Swails J, York DM, Roitberg AE. Constant pH replica exchange molecular dynamics in explicit solvent using discrete protonation states: implementation, testing, and validation. J. Chem. Theory Comput. 2013;10:1341–1352. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3985686/.
- Coskuner O, Wise-Scira O. Arginine and disordered amyloid; β peptide structures: molecular level insights into the toxicity in alzheimer’s disease A. ACS Chem. Neurosci. 2013;4:1549–1558. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3867960/.
- Atzori A, Baker AE, Chiu M, et al. Effect of sequence and stereochemistry reversal on p53 peptide mimicry. PLoS ONE. 2013;8:e68723. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3726663/.
- Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J. Mol. Graphics. 1996;14:33–38.
- Karlin S. A first course in stochastic processes. Revised ed. San Diego (CA): Academic Press; 2014.
- Sriraman S, Kevrekidis IG, Hummer G. Coarse master equation from bayesian analysis of replica molecular dynamics simulations. J. Phys. Chem. B. 2005;109:6479–6484. doi:10.1021/jp046448u.
- Bowman GR. An overview and practical guide to building Markov state models. Chapter 2. In: Bowman GR, Pande VS, Noe F,editors. An introduction to markov state models and their application to long timescale molecular simulation. New York (NY): Springer; 2014. p. 7–22.
- Pande VS, Beauchamp K, Bowman GR. Everything you wanted to know about Markov state models but were afraid to ask. Methods. 2010;52:99–105. Available from: http://www.sciencedirect.com/science/article/pii/S1046202310001568.
- Swope WC, Pitera JW, Suits F. Describing protein folding kinetics by molecular dynamics simulations. 1. theory. J. Phys. Chem. B. 2004;108:6571–6581.
- Swope WC, Pitera JW, Suits F, et al. Describing protein folding kinetics by molecular dynamics simulations. 2. example applications to alanine dipeptide and a β-hairpin peptide. J. Phys. Chem. B. 2004;108:6582–6594.
- Chodera J, Swope W, Pitera J, et al. Long time protein folding dynamics from short time molecular dynamics simulations. Multiscale Model Simul. 2006;5:1214–1226. doi:10.1137/06065146X.
- Chodera JD, Noé F. Markov state models of biomolecular conformational dynamics. Curr. Opin. Struct. Biol. 2014;25:135–144.
- Inamura Y. Estimating continuous time transition matrices from discretely observed data. Bank of Japan working paper series. Vol. 06 - E07, 2006. Available from: http://www.boj.or.jp/en/research/wps_rev/wps_2006/data/wp06e07.pdf.
- Rubinstein M, Colby RH. Polymer physics. Oxford: Oxford University Press; 2003.
- Lawson J. Generalized Runge-Kutta processes for stable systems with large Lipschitz constants. SIAM J. Numer. Anal. 1967;4:372–380. doi:10.1137/0704033.
- Higham N. The scaling and squaring method for the matrix exponential revisited. SIAM J. Matrix Anal. & Appl. 2005;26:1179–1193. doi:10.1137/04061101X.
- Al-Mohy A, Higham N. A new scaling and squaring algorithm for the matrix exponential. SIAM. J. Matrix Anal. & Appl. 2009;31:970–989. doi:10.1137/09074721X.
- Kijima M. Markov processes for stochastic modeling. Chapter 2. CRC Press; 1997. p. 64. Available from: .
- Sarich M, Prinz JH, Schütte C. Markov model theory. In: Bowman GR, Pande VS, Noe F,editors. An introduction to Markov state models and their application to long timescale molecular simulation. Chapter 3. New York (NY): Springer; 2014. p. 23–44.
- Bowman GR, Pande VS, Noe F. Introduction and overview of this book. In: Bowman GR, Pande VS, Noe F,editors. An introduction to Markov state models and their application to long timescale molecular simulation. Chapter 1. New York (NY): Springer; 2014. p. 1–6.
- Schmit JD, Ghosh K, Dill K. What drives amyloid molecules to assemble into oligomers and fibrils? Biophys. J. 2011;100:450–458. doi:10.1016/j.bpj.2010.11.041.
- Yong W, Lomakin A, Kirkitadze MD, et al. Structure determination of micelle-like intermediates in amyloid; β -protein fibril assembly by using small angle neutron scattering. Proc. Natl. Acad. Sci. USA. 2002;99:150–154.
- Gillam JE, MacPhee CE. Modelling amyloid fibril formation kinetics: mechanisms of nucleation and growth. J. Phys. Condens. Matter. 2013;25:373101. Available from: http://stacks.iop.org/0953-8984/25/i=37/a=373101.
- Andrews JM, Roberts CJ. A Lumry-Eyring nucleated polymerization model of protein aggregation kinetics: 1. aggregation with pre-equilibrated unfolding. J. Phys. Chem. B. 2007;111:7897–7913. doi:10.1021/jp070212j.
- Li Y, Roberts CJ. Lumry-Eyring nucleated-polymerization model of protein aggregation kinetics. 2. competing growth via condensation and chain polymerization. J. Phys. Chem. B. 2009;113:7020–7032. doi:10.1021/jp8083088.
- Wagoner V, Cheon M, Chang I, et al. Computer simulation study of amyloid fibril formation by palindromic sequences in prion peptides. Proteins. 2011;79:2132–2145. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3448282/.
- Campbell M, Farrell S. Biochemistry. 8th ed. Stamford (CT): Cengage Learning; 2014.