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Exploring amyloid aggregates with coarse-grained protein simulations

Published online by Cambridge University Press:  05 April 2013

Philippe Derreumaux
Philippe Derreumaux*
Affiliation:
Laboratoire de Biochimie Théorique, UPR 9080 CNRS, Université Paris Diderot, Sorbonne Paris Cité, IBPC, 13 rue Pierre et Marie Curie, 75005, Paris, France Institut Universitaire de France, 103 Boulevard Saint-Michel, 75005, Paris.
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Abstract

Proteins are complex, yet elegant, machines fine-tuned by evolution to properly fulfill a variety of tasks in the crowded cellular environment. These are, however, very challenging numerically due to their dimension, number of degrees of freedom and the wide range of relevant time scales. With aging, some proteins misfold and form harmful amyloid aggregates associated with multiple neurodegenerative diseases, and in particular Alzheimer’s, which challenge our society today. Here, I present the coarse-grained OPEP (Optimized Potential for Efficient peptide structure Prediction) force field and what we can learn from OPEP simulations to get insights into the self-assembly of amyloid peptides.

Keywords

simulationbiologicalphse transformation
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1535: Symposium a – Proceedings of the Multiscale Materials Modeling 2012 Conference , 2013 , mmm2012-a-0342
Copyright
Copyright © Materials Research Society 2013 

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References

REFERENCES

Shaw, DE.; Maragakis, P.; Lindorff-Larsen, K.; Piana, S.; Dror, RO.; Eastwood, MP.; Bank, JA.; Jumper, JM.; Salmon, JK.; Shan, Y.; and Wriggers, W. Atomic-level characterization of the structural dynamics of proteins. Science 330, 341−346 (2010).CrossRefGoogle ScholarPubMed
Lindorff-Larsen, K.; Piana, S.; Dror, R. O.; and Shaw, D. E. How fast-folding proteins fold. Science, 334, 517–20. Science 334, 517−520 (2011).Google Scholar
Shan, Y, Kim, ET, Eastwood, MP, Dror, RO, Seeliger, MA, and Shaw, DE. How does a drug molecule find its target binding site? J Am Chem Soc.133(24):9181–3 (2011).CrossRefGoogle ScholarPubMed
Hansmann, U. H.; and Okamoto, Y. New Monte Carlo algorithms for protein folding. Curr. Opin. Struct. Biol. 9, 177−183 (1999).CrossRefGoogle ScholarPubMed
Limongelli, V, Marinelli, L, Cosconati, S, La Motta, C, Sartini, S, Mugnaini, L, Da Settimo, F, Novellino, E, and Parrinello, M. Sampling protein motion and solvent effect during ligand binding. Proc Natl Acad Sci U S A. 109(5):1467–72 (2012).Google ScholarPubMed
Dill, KA, and Chan, HS. From Levinthal to pathways to funnels. Nat Struct Biol 4:10–19 (1997); Shakhnovich E. Protein Folding Thermodynamics and Dynamics: Where physics, chemistry, and biology Meet. Chem Rev. 106:1559–1588 (2006).CrossRefGoogle ScholarPubMed
Clementi, C. Coarse-grained models of protein folding: toy models or predictive tools? Curr Opin Struct Biol. 17:1–6 (2007).Google Scholar
Derreumaux, P. Generating ensemble averages for small proteins from extended conformations by Monte Carlo simulations. Phys. Rev. Lett. 85, 206−209 (2000).CrossRefGoogle ScholarPubMed
Ding, F, LaRocque, JJ, and Dokholyan, NV. Direct observation of protein folding, aggregation, and a prion-like conformational conversion. J Biol Chem. 280(48):40235–40 (2005).CrossRefGoogle Scholar
Bellesia, G, and Shea, JE. Diversity of kinetic pathways in amyloid fibril formation. J Chem Phys. 131(11):111102 (2009); Rojas, A. V.; Liwo, A.; and Scheraga, H.A. A study of the α-helical intermediate preceding the aggregation of the amino-terminal fragment of the β amyloid peptide (Aβ(1-28)) J Phys Chem B, 115, 12978–12983 (2011) ; Bereau, T. and Deserno, M. Generic coarse-grained model for protein folding and aggregation. J Chem Phys 130, 235106(2009).CrossRefGoogle ScholarPubMed
Devane, R.; Shinoda, W.; Moore, P. B.; and Klein, M.L. A Transferable Coarse Grain Non-bonded Interaction Model For Amino Acids. J. Chem. Theory Comput. 5, 2115−2124 (2009).CrossRefGoogle ScholarPubMed
Monticelli, L.; Kandasamy, S. K.; Periole, X.; Larson, R. G.; Tieleman, D. P.; and Marrink, S.-J. The MARTINI coarse grained force field: extension to proteins. J. Chem. Theory Comput. 4, 819−834 (2008).CrossRefGoogle ScholarPubMed
Maupetit, J.; Tuffery, P.; and Derreumaux, P. A coarse-grained protein force field for folding and structure prediction. Proteins 69, 394−408 (2007).Google ScholarPubMed
Chebaro, Y, Pasquali, S, and Derreumaux, P. The coarse-grained OPEP force field for non-amyloid and amyloid proteins. J. Phys. Chem. B 116(30):8741–52 (2012).CrossRefGoogle ScholarPubMed
Derreumaux, P. J. Chem. Phys . A diffusion process-controlled Monte Carlo method for finding the global energy minimum of a polypeptide chain.1. Formulation and test on a hexadecapeptide. J Chem Phys. 106, 5260−2370 (1997).CrossRefGoogle Scholar
Forcellino, F.; and Derreumaux, P. Computer simulations aimed at structure prediction of supersecondary motifs in proteins. Proteins 45, 159−166 (2001).CrossRefGoogle ScholarPubMed
Barducci, A.; Bonomi, M.; and Derreumaux, P. Assessing the Quality of the OPEP Coarse-Grained Force Field. J. Chem. Theory Comput. 7, 1928−1934 (2011).CrossRefGoogle ScholarPubMed
Maupetit, J, Derreumaux, P, and Tufféry, P. A fast method for large-scale de novo peptide and miniprotein structure prediction. J Comput Chem. 31(4):726–38 (2010).Google ScholarPubMed
Thévenet, P, Shen, Y, Maupetit, J, Guyon, F, Derreumaux, P, and Tufféry, P. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res. 40(Web Server issue):W288–93 (2012).CrossRefGoogle ScholarPubMed
Maupetit, J, Derreumaux, P, and Tuffery, P. PEP-FOLD: an online resource for de novo peptide structure prediction. Nucleic Acids Res. 37(Web Server issue):W498–503 (2010).CrossRefGoogle Scholar
Santini, S.; Wei, G.; Mousseau, N.; and Derreumaux, P. Pathway complexity of Alzheimer's beta-amyloid Abeta16-22 peptide assembly. Structure 12, 1245−1255 (2004).CrossRefGoogle ScholarPubMed
Santini, S.; Mousseau, N.; and Derreumaux, P. In silico assembly of Alzheimer's Abeta16-22 peptide into beta-sheets. J. Am. Chem. Soc. 126, 11509−11516 (2004).CrossRefGoogle ScholarPubMed
Mousseau, N.; and Derreumaux, P. Exploring the early steps of amyloid peptide aggregation by computers. Acc. Chem. Res. 38, 885−891 (2005).CrossRefGoogle ScholarPubMed
Melquiond, A.; Mousseau, N.; and Derreumaux, P. Structures of soluble amyloid oligomers from computer simulations. Proteins 65, 180−191 (2006).CrossRefGoogle ScholarPubMed
Chebaro, Y, and Derreumaux, P. Targeting the early steps of Abeta16-22 protofibril disassembly by N-methylated inhibitors: a numerical study. Proteins. 75(2):442–52 (2009);CrossRefGoogle ScholarPubMed
Nasica-Labouze, J, Meli, M, Derreumaux, P, Colombo, G, and Mousseau, N. A multiscale approach to characterize the early aggregation steps of the amyloid-forming peptide GNNQQNY from the yeast prion sup-35. PLoS Comput Biol. 7(5):e1002051 (2011); Wei G, Song W, Derreumaux P, and Mousseau N. Self-assembly of amyloid-forming peptides by molecular dynamics simulations. Front Biosci.13:5681–92(2008).CrossRefGoogle ScholarPubMed
Lu, Y, Wei, G, and Structural, Derreumaux P., thermodynamical, and dynamical properties of oligomers formed by the amyloid NNQQ peptide: insights from coarse-grained simulations. J Chem Phys. 137(2):025101 (2012).CrossRefGoogle ScholarPubMed
Côté, S, Laghaei, R, Derreumaux, P, and Mousseau, N. Distinct dimerization for various alloforms of the amyloid-beta protein: Aβ(1-40), Aβ(1-42), and Aβ(1-40)(D23N). J Phys Chem B 116(13): 4043–55 (2012).CrossRefGoogle Scholar
Chebaro, Y, Jiang, P, Zang, T, Mu, Y, Nguyen, PH, Mousseau, N and Derreumaux, P. Structures of Aβ17-42 trimers in isolation and with five small-molecule drugs using a hierarchical computational procedure. J Phys Chem B. 116(29):8412–22 (2012).CrossRefGoogle ScholarPubMed
Derreumaux, P.; Wilson, K.; Vergoten, G.; and Peticolas, W. Conformational studies of neuroactive ligands. 1. Force field and vibrational spectra of crystalline acetylcholine. J. Phys. Chem. 93, 1338−1350 (1989).Google Scholar
Derreumaux, P.; Vergoten, G.; and Lagant, P. A vibrational molecular force field of compounds with biological interest. 1. Harmonic dynamics of crystalline urea at 123 K. J. Comput. Chem. 11, 560−568 (1990).CrossRefGoogle Scholar
Nguyen, P. H.; Li, M. S.; and Derreumaux, P. Effects of all-atom force fields on amyloid oligomerization: replica exchange molecular dynamics simulations of the Aβ(16-22) dimer and trimer. Phys. Chem. Chem. Phys. 2011, 13, 9778−9788 (2011).CrossRefGoogle Scholar
De Strooper, B, Vassar, R, and Golde, T. The secretases: enzymes with therapeutic potential in Alzheimer disease. Nat Rev Neurol. 6(2):99–107 (2010)CrossRefGoogle ScholarPubMed
Lesné, S, Koh, MT, Kotilinek, L, Kayed, R, Glabe, CG, Yang, A, Gallagher, M, and Ashe, KH. A specific amyloid-beta protein assembly in the brain impairs memory. Nature 440, 352–357 (2006); Xue WF, Hellewell AL, Gosal WS, Homans SW, Hewitt EW, and Radford SE. Fibril fragmentation enhances amyloid cytotoxicity. J Biol Chem.284(49):34272-82(2009).CrossRefGoogle ScholarPubMed
Irbäck, A, and Mitternacht, S. Spontaneous beta-barrel formation: an all-atom Monte Carlo study of Abeta16-22 oligomerization. Proteins 71(1):207–14 (2008).CrossRefGoogle ScholarPubMed
Laganowsky, A, Liu, C, Sawaya, MR, Whitelegge, JP, Park, J, Zhao, M, Pensalfini, A, Soriaga, AB, Landau, M, Teng, PK, Cascio, D, Glabe, C, and Eisenberg, D. Atomic view of a toxic amyloid small oligomer. Science 335(6073):1228–31 (2012).CrossRefGoogle ScholarPubMed
Wagoner, VA, Cheon, M, Chang, I and Hall, CK. Fibrillization propensity for short designed hexapeptides predicted by computer simulation. J Mol Biol. 416(4):598–609 (2012); Matthes D, Gapsys V, and de Groot BL. Driving forces and structural determinants of steric zipper peptide oligomer formation elucidated by atomistic simulations. J Mol Biol. 421(2-3):390-416(2012).CrossRefGoogle ScholarPubMed
Baftizadeh, F, Biarnes, X, Pietrucci, F, Affinito, F, and Laio, A. Multidimensional view of amyloid fibril nucleation in atomistic detail. J Am Chem Soc.134(8):3886–94 (2012).CrossRefGoogle ScholarPubMed
Scherzer-Attali, R, Pellarin, R, Convertino, M, Frydman-Marom, A, Egoz-Matia, N, Peled, S, Levy-Sakin, M, Shalev, DE, Caflisch, A, Gazit, E, and Segal, D. Complete phenotypic recovery of an Alzheimer's disease model by a quinone-tryptophan hybrid aggregation inhibitor. PLoS One 5(6):e11101 (2010).CrossRefGoogle ScholarPubMed
Ziegler, J, Viehrig, C, Geimer, S, Rösch, P, and Schwarzinger, S. Putative aggregation initiation sites in prion protein. FEBS Lett. 580(8):2033–40 (2006).CrossRefGoogle ScholarPubMed
De Simone, A, Zagari, A, and Derreumaux, P. Structural and hydration properties of the partially unfolded states of the prion protein. Biophys J. 93(4):1284–92 (2007); Miettinen MS, Knecht V, Monticelli L, and Ignatova Z. Assessing Polyglutamine Conformation in the Nucleating Event by Molecular Dynamics Simulations. J Phys Chem B. in press (2012).CrossRefGoogle ScholarPubMed