Abstract
Recent advances in computational technology have allowed us to simulate biomolecular processes on timescales that begin to reach the rates of peptide aggregation phenomena. Molecular dynamics simulations have evolved into a mature technique to the extent that they can be employed as a highly productive tool to gain meaningful insights into the structure, dynamics and molecular mechanisms of protein aggregation. In this chapter, we describe the basics of explicit solvent all-atom molecular dynamics simulations and its applications for studying early stages of aggregation processes of two short pentapeptides: KLVFF and FVFLM, related to Alzheimer’s disease and preeclampsia, respectively. We focus on certain important problems in the field of protein aggregation that explicit solvent all-atom molecular dynamics simulation studies could resolve. This includes how fibril formation rates depend on a number of factors such as the presence of short peptides and population of fibril-prone conformations. Specific applications of atomistic simulations in explicit solvent to address these two issues are discussed.
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References
Alder, B.J., Wainwright, T.E.: Phase transition for a hard sphere system. J. Chem. Phys. 27(5), 1208–1209 (1957)
Anfinsen, C.B.: Principles that govern folding of protein chains. Science 181(4096), 223–230 (1973)
Balbach, J.J., Ishii, Y., Antzutkin, O.N., Leapman, R.D., Rizzo, N.W., Dyda, F., Reed, J., Tycko, R.: Amyloid fibril formation by Abeta(16–22), a seven-residue fragment of the Alzheimer’s beta-amyloid peptide, and structural characterization by solid state NMR. Biochemistry 39(45), 13748–13759 (2000)
Barz, B., Wales, D.J., Strodel, B.: A kinetic approach to the sequence-aggregation relationship in disease-related protein assembly. J. Phys. Chem. B 118(4), 1003–1011 (2014)
Berendsen, H.J.C, Postma, J.P.M., van Gunsteren, W.F., Hermans, J.: Interaction models for water in relation to protein hydration. Intermolecular Forces 14, 331–442 (1981)
Berg, B.A., Neuhaus, T.: Multicanonical algorithms for 1st order phase-transitions. Phys. Lett. B 267(2), 249–253 (1991)
Berhanu, W.M., Alred, E.J., Hansmann, U.H.E.: Stability of Osaka mutant and wild-type fibril models. J. Phys. Chem. B 119(41), 13063–13070 (2015)
Bernhardt, N.A., Xi, W.H., Wang, W., Hansmann, U.H.E.: Simulating protein fold switching by replica exchange with tunneling (vol 12, pg 5656, 2016). J. Chem. Theory Comput. 13(1), 393–394 (2017)
Bhavaraju, M., Hansmann, U.H.E.: Effect of single point mutations in a form of systemic amyloidosis. Protein Sci. 24(9), 1451–1462 (2015)
Blaszczyk, M., Kurcinski, M., Kouza, M., Wieteska, L., Debinski, A., Kolinski, A., Kmiecik, S.: Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods 93, 72–83 (2016)
Blokhuis, A.M., Groen, E.J.N., Koppers, M., van den Berg, L.H., Pasterkamp, R.J.: Protein aggregation in amyotrophic lateral sclerosis. Acta Neuropathol. 125(6), 777–794 (2013)
Boczko, E.M., Brooks, C.L.: First-Principles calculation of the folding free-energy of a 3-helix bundle protein. Science 269(5222), 393–396 (1995)
Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S., Karplus, M.: Charmm—A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4(2), 187–217 (1983)
Buhimschi, I., Jing, H.W., Axe, M., Ray, W., Zhao, G.M., Huang, C.S., Song, Y., Wysocki, V., Buhimschi, C.: Shotgun proteomics of the urine misfoldome identifies molecular signatures of preeclampsia subphenotypes. Am. J. Obstet. Gynecol. 212(1), S34 (2015)
Buhimschi, I.A., Nayeri, U.A., Zhao, G., Shook, L.L., Pensalfini, A., Funai, E.F., Bernstein, I.M., Glabe, C.G., Buhimschi, C.S.: Protein misfolding, congophilia, oligomerization, and defective amyloid processing in preeclampsia. Sci. Transl. Med. 6(245), 245–292 (2014)
Bussi, G., Donadio, D., Parrinello, M.: Canonical sampling through velocity rescaling. J. Chem. Phys. 126(1), 014101 (2007)
Case, D.A., Cheatham, T.E., Darden, T., Gohlke, H., Luo, R., Merz, K.M., Onufriev, A., Simmerling, C., Wang, B., Woods, R.J.: The Amber biomolecular simulation programs. J. Comput. Chem. 26(16), 1668–1688 (2005)
Castillo, V., Grana-Montes, R., Sabate, R., Ventura, S.: Prediction of the aggregation propensity of proteins from the primary sequence: aggregation properties of proteomes. Biotechnol. J. 6(6), 674–685 (2011)
Chafekar, S.M., Malda, H., Merkx, M., Meijer, E.W., Viertl, D., Lashuel, H.A., Baas, F., Scheper, W.: Branched KLVFF tetramers strongly potentiate inhibition of beta-amyloid aggregation. ChemBioChem 8(15), 1857–1864 (2007)
Chen, W.T., Hong, C.J., Lin, Y.T., Chang, W.H., Huang, H.T., Liao, J.Y., Chang, Y.J., Hsieh, Y.F., Cheng, C.Y., Liu, H.C., Chen, Y.R., Cheng, I.H.: Amyloid-beta (Abeta) D7H mutation increases oligomeric Abeta42 and alters properties of Abeta-zinc/copper assemblies. PLoS ONE 7(4), e35807 (2012)
Chiti, F., Dobson, C.M.: Protein misfolding, amyloid formation, and human disease: a summary of progress over the last decade. Annu. Rev. Biochem. 86(86), 27–68 (2017)
Coskuner, O., Wise-Scira, O., Perry, G., Kitahara, T.: The structures of the E22 delta mutant-type amyloid-beta alloforms and the impact of E22 delta mutation on the structures of the wild-type amyloid-beta alloforms. ACS Chem. Neurosci. 4(2), 310–320 (2013)
Darden, T., York, D., Pedersen, L.: Particle mesh Ewald—An N.log(N) method for Ewald sums in large systems. J. Chem. Phys. 98(12), 10089–10092 (1993)
Di Fede, G., Catania, M., Morbin, M., Rossi, G., Suardi, S., Mazzoleni, G., Merlin, M., Giovagnoli, A.R., Prioni, S., Erbetta, A., Falcone, C., Gobbi, M., Colombo, L., Bastone, A., Beeg, M., Manzoni, C., Francescucci, B., Spagnoli, A., Cantu, L., Del Favero, E., Levy, E., Salmona, M., Tagliavini, F.: A recessive mutation in the APP gene with dominant-negative effect on amyloidogenesis. Science 323(5920), 1473–1477 (2009)
Dobson, C.M.: Protein folding and misfolding. Nature 426(6968), 884–890 (2003)
Frenkel, D., Smit, B.: Understanding Molecular Simulation: From Algorithms to Applications. Elsevier (1996)
Frydman-Marom, A., Rechter, M., Shefler, I., Bram, Y., Shalev, D.E., Gazit, E.: Cognitive-performance recovery of Alzheimer’s disease model mice by modulation of early soluble amyloidal assemblies. Angew. Chem. Int. Ed. Engl. 48(11), 1981–1986 (2009)
Garbuzynskiy, S.O., Lobanov, M.Y., Galzitskaya, O.V.: FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence. Bioinformatics 26(3), 326–332 (2010)
Gazit, E.: Self assembly of short aromatic peptides into amyloid fibrils and related nanostructures. Prion 1(1), 32–35 (2007)
Gordon, D.J., Tappe, R., Meredith, S.C.: Design and characterization of a membrane permeable N-methyl amino acid-containing peptide that inhibits Abeta(1–40) fibrillogenesis. J. Peptide Res. 60(1), 37–55 (2002)
Hamaguchi, T., Ono, K., Yamada, M.: Anti-amyloidogenic therapies: strategies for prevention and treatment of Alzheimer’s disease. Cell. Mol. Life Sci. 63(13), 1538–1552 (2006)
Hansmann, U.H.E.: Parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 281(1–3), 140–150 (1997)
Hess, B., Bekker, H., Berendsen, H.J.C., Fraaije, J.G.E.M.: LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18(12), 1463–1472 (1997)
Hess, B., Kutzner, C., van der Spoel, D., Lindahl, E.: GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 4(3), 435–447 (2008)
Hornak, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A., Simmerling, C.: Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins-Struct. Funct. Bioinf. 65(3), 712–725 (2006)
Hukushima, K., Nemoto, K.: Exchange Monte Carlo method and application to spin glass simulations. J. Phys. Soc. Jpn. 65(6), 1604–1608 (1996)
Jing, H.W., Zhao, G.M., Axe, M., Buhimschi, C.S., Wysocki, V., Buhimschi, I.A.: Protein enrichment using Congo red (CR) affinity enhances characterization of the urine misfoldome in preeclampsia (PE). Am. J. Obstet. Gynecol. 214(1), S408 (2016)
Jorgensen, W.L., Chandrasekhar, J., Madura, J.D., Impey, R.W., Klein, M.L.: Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79(2), 926–935 (1983)
Jorgensen, W.L., Tiradorives, J.: The opls potential functions for proteins-energy minimizations for crystals of cyclic-peptides and crambin. J. Am. Chem. Soc. 110(6), 1657–1666 (1988)
Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A.E., Kolinski, A.: Coarse-grained protein models and their applications. Chem. Rev. 116(14), 7898–7936 (2016)
Kolinski, A.: Protein modeling and structure prediction with a reduced representation. Acta Biochim. Pol. 51(2), 349–371 (2004)
Kouza, M., Banerji, A., Kolinski, A., Buhimschi, I.A., Kloczkowski, A.: Oligomerization of FVFLM peptides and their ability to inhibit beta amyloid peptides aggregation: consideration as a possible model. Phys. Chem. Chem. Phys. 19(4), 2990–2999 (2017)
Kouza, M., Co, N.T., Nguyen, P.H., Kolinski, A., Li, M.S.: Preformed template fluctuations promote fibril formation: Insights from lattice and all-atom models. J. Chem. Phys. 142(14), 04B610_1 (2015)
Kouza, M., Faraggi, E., Kolinski, A., Kloczkowski, A.: The GOR method of protein secondary structure prediction, and its application as protein aggregation prediction tool. In: Zhou, Y., Kloczkowski, A., Faraggi, E., Yang, Y. (eds.) Prediction of Protein Secondary Structure. vol. 1484, pp. 7–24. Humana Press, New York (2017)
Kouza, M., Hansmann, U.H.E.: Velocity scaling for optimizing replica exchange molecular dynamics. J. Chem. Phys. 134(4), 01B630 (2011)
Kouza, M., Hu, C.K., Li, M.S.: New force replica exchange method and protein folding pathways probed by force-clamp technique. J. Chem. Phys. 128(4), 01B618 (2008)
Kouza, M., Hu, C.K., Li, M.S., Kolinski, A.: A structure-based model fails to probe the mechanical unfolding pathways of the titin I27 domain. Journal of Chemical Physics 139(6), 08B615 (2013)
Kouza, M., Hu, C.K., Zung, H., Li, M.S.: Protein mechanical unfolding: Importance of non-native interactions. J. Chem. Phys. 131(21), 12B608 (2009)
Kouza, M., Lan, P.D., Gabovich, A.M., Kolinski, A., Li, M.S.: Switch from thermal to force-driven pathways of protein refolding. J. Chem. Phys. 146(13), 135101 (2017)
Kubelka, J., Hofrichter, J., Eaton, W.A.: The protein folding ‘speed limit’. Curr. Opin. Struct. Biol. 14(1), 76–88 (2004)
Li, M.S., Co, N.T., Reddy, G., Hu, C.K., Straub, J.E., Thirumalai, D.: Factors governing fibrillogenesis of polypeptide chains revealed by lattice models. Phys. Rev. Lett. 105(21), 218101 (2010)
Lindorff-Larsen, K., Maragakis, P., Piana, S., Shaw, D.E.: Picosecond to millisecond structural dynamics in human ubiquitin. J. Phys. Chem. B 120(33), 8313–8320 (2016)
Liwo, A., He, Y., Scheraga, H.A.: Coarse-grained force field: general folding theory. Phys. Chem. Chem. Phys. 13(38), 16890–16901 (2011)
Lu, J.X., Qiang, W., Yau, W.M., Schwieters, C.D., Meredith, S.C., Tycko, R.: Molecular structure of beta-amyloid fibrils in Alzheimer’s disease brain tissue. Cell 154(6), 1257–1268 (2013)
Lu, Y., Wei, G.H., Derreumaux, P.: Effects of G33A and G33I mutations on the structures of monomer and dimer of the amyloid-beta fragment 29–42 by replica exchange molecular dynamics simulations. J. Phys. Chem. B 115(5), 1282–1288 (2011)
Luhrs, T., Ritter, C., Adrian, M., Riek-Loher, D., Bohrmann, B., Doeli, H., Schubert, D., Riek, R.: 3D structure of Alzheimer’s amyloid-beta(1–42) fibrils. Proc. Natl. Acad. Sci. U S A 102(48), 17342–17347 (2005)
Marrink, S.J., Risselada, H.J., Yefimov, S., Tieleman, D.P., de Vries, A.H.: The MARTINI force field: coarse grained model for biomolecular simulations. J. Phys. Chem. B 111(27), 7812–7824 (2007)
Mazor, Y., Gilead, S., Benhar, I., Gazit, E.: Identification and characterization of a novel molecular-recognition and self-assembly domain within the islet amyloid polypeptide. J. Mol. Biol. 322(5), 1013–1024 (2002)
Mccammon, J.A., Gelin, B.R., Karplus, M.: Dyn. Folded Proteins. Nature 267(5612), 585–590 (1977)
Micheletti, C., Laio, A., Parrinello, M.: Reconstructing the density of states by history-dependent metadynamics. Phys. Rev. Lett. 92(17), 170601 (2004)
Moreno-Gonzalez, I., Soto, C.: Misfolded protein aggregates: mechanisms, structures and potential for disease transmission. Semin. Cell Dev. Biol. 22(5), 482–487 (2011)
Morriss-Andrews, A., Shea, J.E.: Simulations of protein aggregation: insights from atomistic and coarse-grained models. J. Phys. Chem. Lett. 5(11), 1899–1908 (2014)
Morriss-Andrews, A., Shea, J.E.: Computational studies of protein aggregation: methods and applications. Annu. Rev. Phys. Chem. 66(66), 643–666 (2015)
Nam, H.B., Kouza, M., Hoang, Z., Li, M.S.; Relationship between population of the fibril-prone conformation in the monomeric state and oligomer formation times of peptides: Insights from all-atom simulations. J. Chem. Phys. 132(16), 04B613 (2010)
Nguyen, P.H., Li, M.S., Stock, G., Straub, J.E., Thirumalai, D.: Monomer adds to preformed structured oligomers of Abeta-peptides by a two-stage dock-lock mechanism. Proc. Natl. Acad. Sci. U S A 104(1), 111–116 (2007)
Ono, K., Condron, M.M., Teplow, D.B.: Effects of the English (H6R) and Tottori (D7N) familial Alzheimer disease mutations on amyloid beta-protein assembly and toxicity. J. Biol. Chem. 285(30), 23184–23195 (2010)
Peter, E.K., Pivkin, I.V., Shea, J.E.: A canonical replica exchange molecular dynamics implementation with normal pressure in each replica. J. Chem. Phys. 145(4), 044903 (2016)
Petkova, A.T., Yau, W.M., Tycko, R.: Experimental constraints on quaternary structure in Alzheimer’s beta-amyloid fibrils. Biochemistry 45(2), 498–512 (2006)
Phillips, J.C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R.D., Kale, L., Schulten, K.: Scalable molecular dynamics with NAMD. J. Comput. Chem. 26(16), 1781–1802 (2005)
Proctor, E.A., Fee, L., Tao, Y.Z., Redler, R.L., Fay, J.M., Zhang, Y.L., Lv, Z.J., Mercer, I.P., Deshmukh, M., Lyubchenko, Y.L., Dokholyan, N.V.: Nonnative SOD1 trimer is toxic to motor neurons in a model of amyotrophic lateral sclerosis. Proc. Natl. Acad. Sci. U S A 113(3), 614–619 (2016)
Pronk, S., Pall, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., van der Spoel, D., Hess, B., Lindahl, E.: GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7), 845–854 (2013)
Rhee, Y.M., Sorin, E.J., Jayachandran, G., Lindahl, E., Pande, V.S.: Simulations of the role of water in the protein-folding mechanism. Proc. Natl. Acad. Sci. U S A 101(17), 6456–6461 (2004)
Rief, M., Gautel, M., Oesterhelt, F., Fernandez, J.M., Gaub, H.E.: Reversible unfolding of individual titin immunoglobulin domains by AFM. Science 276(5315), 1109–1112 (1997)
Rojas, A.V., Liwo, A., Scheraga, H.A.: A study of the alpha-helical intermediate preceding the aggregation of the amino-terminal fragment of the beta amyloid peptide (Abeta(1–28)). J. Phys. Chem. B 115(44), 12978–12983 (2011)
Scheraga, H.A., Khalili, M., Liwo, A.: Protein-folding dynamics: overview of molecular simulation techniques. Annu. Rev. Phys. Chem. 58, 57–83 (2007)
Scott, W.R.P., Hunenberger, P.H., Tironi, I.G., Mark, A.E., Billeter, S.R., Fennen, J., Torda, A.E., Huber, T., Kruger, P., van Gunsteren, W.F.: The GROMOS biomolecular simulation program package. J. Phys. Chem. A 103(19), 3596–3607 (1999)
Selkoe, D.J.: Alzheimer’s disease: genes, proteins, and therapy. Physiol. Rev. 81(2), 741–766 (2001)
Shakhnovich, E.: Protein folding thermodynamics and dynamics: Where physics, chemistry, and biology meet. Chem. Rev. 106(5), 1559–1588 (2006)
Siwy, C.M., Lockhart, C., Klimov, D.K.: Is the conformational ensemble of Alzheimer’s Abeta 10–40 peptide force field dependent? Plos Computat. Biol. 13(1), e1005314 (2017)
Sugita, Y., Okamoto, Y.: Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314(1–2), 141–151 (1999)
Tartaglia, G.G., Vendruscolo, M.: The Zyggregator method for predicting protein aggregation propensities. Chem. Soc. Rev. 37(7), 1395–1401 (2008)
Tenidis, K., Waldner, M., Bernhagen, J., Fischle, W., Bergmann, M., Weber, M., Merkle, M.L., Voelter, W., Brunner, H., Kapurniotu, A.: Identification of a penta- and hexapeptide of islet amyloid polypeptide (IAPP) with amyloidogenic and cytotoxic properties. J. Mol. Biol. 295(4), 1055–1071 (2000)
Thirumalai, D., Reddy, G., Straub, J.E.: Role of water in protein aggregation and amyloid polymorphism. Acc. Chem. Res. 45(1), 83–92 (2012)
Tjernberg, L.O., Lilliehook, C., Callaway, D.J.E., Naslund, J., Hahne, S., Thyberg, J., Terenius, L., Nordstedt, C.: Controlling amyloid beta-peptide fibril formation with protease-stable ligands (vol 272, pg 12601, 1997). J. Biol. Chem. 272(28), 17894–17895 (1997)
Tjernberg, L.O., Naslund, J., Lindqvist, F., Johansson, J., Karlstrom, A.R., Thyberg, J., Terenius, L., Nordstedt, C.: Arrest of beta-amyloid fibril formation by a pentapeptide ligand. J. Biol. Chem. 271(15), 8545–8548 (1996)
Tomiyama, T., Nagata, T., Shimada, H., Teraoka, R., Fukushima, A., Kanemitsu, H., Takuma, H., Kuwano, R., Imagawa, M., Ataka, S., Wada, Y., Yoshioka, E., Nishizaki, T., Watanabe, Y., Mori, H.: A new amyloid mu variant favoring oligomerization in Alzheimer’s-type dementia. Ann. Neurol. 63(3), 377–387 (2008)
Tong, M., Cheng, S.B., Chen, Q., DeSousa, J., Stone, P.R., James, J.L., Chamley, L.W., Sharma, S.: Aggregated transthyretin is specifically packaged into placental nano-vesicles in preeclampsia. Sci. Rep. 7, 6694 (2017)
Viet, M.H., Ngo, S.T., Lam, N.S., Li, M.S.: Inhibition of aggregation of amyloid peptides by beta-sheet breaker peptides and their binding affinity. J. Phys. Chem. B 115(22), 7433–7446 (2011)
Viet, M.H., Nguyen, P.H., Derreumaux, P., Li, M.S.: Effect of the English familial disease mutation (H6R) on the monomers and dimers of Abeta40 and Abeta42. ACS Chem. Neurosci. 5(8), 646–657 (2014)
Viet, M.H., Nguyen, P.H., Ngo, S.T., Li, M.S., Derreumaux, P.: Effect of the Tottori familial disease mutation (D7N) on the monomers and dimers of Abeta40 and Abeta42. ACS Chem. Neurosci. 4(11), 1446–1457 (2013)
Wabik, J., Kmiecik, S., Gront, D., Kouza, M., Kolinski, A.: Combining coarse-grained protein models with replica-exchange all-atom molecular dynamics. Int. J. Mol. Sci. 14(5), 9893–9905 (2013)
Walti, M.A., Ravotti, F., Arai, H., Glabe, C.G., Wall, J.S., Bockmann, A., Guntert, P., Meier, B.H., Riek, R.: Atomic-resolution structure of a disease-relevant Abeta(1–42) amyloid fibril. Proc. Natl. Acad. Sci. U S A 113(34), E4976–E4984 (2016)
Wang, J.N., Zhu, W.L., Li, G.H., Hansmann, U.H.E.: Velocity-scaling optimized replica exchange molecular dynamics of proteins in a hybrid explicit/implicit solvent. J. Chem. Phys. 135(8), 084115 (2011)
Wu, C., Shea, J.E.: Coarse-grained models for protein aggregation. Curr. Opin. Struct. Biol. 21(2), 209–220 (2011)
Xi, W.H., Hansmann, U.H.E.: Ring-like N-fold models of Abeta(42) fibrils. Sci. Rep. 7, 40787 (2017)
Xi, W.H., Vanderford, E.K., Hansmann, U.H.E.: Out-of-register Abeta(42) assemblies as models for neurotoxic oligomers and fibrils. J. Chem. Theory Comput. 14(2), 1099–1110 (2018)
Xi, W.H., Wang, W.H., Abbott, G., Hansmann, U.H.E.: Stability of a recently found triple-beta-stranded Abeta 1–42 fibril motif. J. Phys. Chem. B 120(20), 4548–4557 (2016)
Xiao, Y.L., Ma, B.Y., McElheny, D., Parthasarathy, S., Long, F., Hoshi, M., Nussinov, R., Ishii, Y.: Abeta(1–42) fibril structure illuminates self-recognition and replication of amyloid in Alzheimer’s disease. Nat. Struct. Mol. Biol. 22(6), 499 (2015)
Yan, L.M., Velkova, A., Tatarek-Nossol, M., Andreetto, E., Kapurniotu, A.: LAPP mimic blocks Abeta cytotoxic self-assembly: cross-suppression of amyloid toxicity of Abeta and IAPP suggests a molecular link between Alzheimer’s disease and type II diabetes. Angew. Chem. Int. Ed. 46(8), 1246–1252 (2007)
Yasar, F., Bernhardt, N.A., Hansmann, U.H.E.: Replica-exchange-with-tunneling for fast exploration of protein landscapes. J. Chem. Phys. 143(22), 224102 (2015)
Kouza, M., Co, N.T., Li, M.S., Kmiecik, S., Kolinski, A., Kloczkowski, A., Buhimschi, I.A.: Kinetics and mechanical stability of the fibril state control fibril formation time of polypeptide chains: A computational study. J. Chem. Phys. 148, 215106 (2018)
Acknowledgements
The authors thank Girik Malik for critical reading of the manuscript. M. K. acknowledges the Polish Ministry of Science and Higher Education for financial support through “Mobilnosc Plus” Program No. 1287/MOB/IV/2015/0. A. Kol. and M. K. would like to acknowledge support from the National Science Center grant [MAESTRO 2014/14/A/ST6/00088]. IAB acknowledges support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) R01HD084628 and The Research Institute at Nationwide Children’s Hospital’s John E. Fisher Endowed Chair for Neonatal and Perinatal Research. A. Klo. acknowledges support from National Science Foundation grant DBI 1661391, and Bridge funds provided by The Research Institute at Nationwide Children’s Hospital. This research was supported in part by the High Performance Computing Facility at The Research Institute at Nationwide Children’s Hospital.
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Kouza, M., Kolinski, A., Buhimschi, I.A., Kloczkowski, A. (2019). Explicit-Solvent All-Atom Molecular Dynamics of Peptide Aggregation. In: Liwo, A. (eds) Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes. Springer Series on Bio- and Neurosystems, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-95843-9_16
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