Coarse-Grained Molecular Dynamics Simulations of Peptide Aggregation on Surfaces

UNIVERSITY OF CALIFORNIA Santa Barbara Coarse-Grained Molecular Dynamics Simulations of Peptide Aggregation on Surfaces A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Physics by Herbert Alexander Morriss-Andrews Committee in charge: Professor Joan-Emma Shea, Chair Professor Everett Lipman Professor Philip Pincus September 2014 The Dissertation of Herbert Alexander Morriss-Andrews is approved. Professor Everett Lipman Professor Philip Pincus Professor Joan-Emma Shea, Committee Chair July 2014 Coarse-Grained Molecular Dynamics Simulations of Peptide Aggregation on Surfaces Copyright c 2014 by Herbert Alexander Morriss-Andrews iii Acknowledgments Foremost, I would like to thank my advisor, Joan-Emma Shea, whose guidance was essential to the development of this work at every step of the way. I would also like to thank Frank Brown for his considerable help in the success of the membrane project in Chapter 6, particularly in the ironing out of many details and subtleties. Finally, the assistence of Max Watson, Andrew Jewett, and Giovanni Bellesia in getting started with the various projects of this work is greatly appre- ciated. I acknowledge funding from an NSERC postdoctoral fellowship (PGS-D), the Los Alamos National Laboratory, and the University of California Santa Bar- bara. Computational resources for simulations were provided by the UC Thresher computer pilot project, the Extreme Science and Engineering Discovery Envi- ronment (XSEDE), and the University of California Santa Barbara Center for Scientific Computing. iv Curriculum Vitae Herbert Alexander Morriss-Andrews Education Ph.D. in Physics, University of California at Santa Barbara, 2014 (expected) M.Sc. in Physics, University of British Columbia, Vancouver, Canada, 2009 B.Sc. in Mathematical Physics, Queen’s University, Kingston, Canada, 2007 Field of Study Computational biophysics, biomolecular simulation, and protein aggregation with Joan-Emma Shea Selected Publications Morriss-Andrews, A. and Shea, J.-E. “Simulations of Protein Aggregation: Insights from Atomistic and Coarse-Grained Models”, J. Phys. Chem. Lett. 5 (11), pp 1899–1908 (2014) Morriss-Andrews, A., Brown, F.L.H. and Shea, J.-E. “A Coarse-Grained Model for Peptide Aggregation on a Membrane Surface”, J. Phys. Chem. B. In press (2014) Watson, M.C., Morriss-Andrews, A. and Brown, F.L.H. “Thermal fluctuations in shape, thickness, and molecular orientation in lipid bilayers. II. Finite surface tensions”, BioChem. Phys. 139, 084706 (2013) Morriss-Andrews, A., Bellesia, G. and Shea, J.-E. “β-sheet propensity controls the kinetic pathways and morphologies of seeded peptide aggregation”, J. Chem. Phys. 137, 145104 (2012) Morriss-Andrews, A. and Shea, J.-E. “Kinetic pathways to peptide aggregation on surfaces: the effects of β-sheet propensity and surface attraction”, J. Chem. Phys. 136, 065103 (2012) Morriss-Andrews, A., Bellesia, G. and Shea, J.-E. “Effects of surface interactions on peptide aggregate morphology”, J. Chem. Phys. 135, 085102 (2011) Morriss-Andrews, A., Rottler, J. and Plotkin, S.S. “A systematically coarse-grained model for DNA, and its predictions for persistence length, stacking, twist, and chirality”, J. Chem. Phys. 132, 035105 (2010) v Abstract Coarse-Grained Molecular Dynamics Simulations of Peptide Aggregation on Surfaces by Herbert Alexander Morriss-Andrews Protein aggregation involves self-assembly of normally soluble proteins or peptides into supramolecular structures. This process is particularly important due to its involvement in several amyloid diseases, such as Parkinson’s, Alzheimer’s, and Type II diabetes. Several fibrillization mechanisms have been proposed, in- cluding a condensation-ordering mechanism where ordered fibril structures emerge from disordered oligomers and a dock-lock mechanism where a growing fibril in- duces attached polypeptides to organize individually into fibril-compatible con- formations. We present a series of computational studies using a coarse-grained peptide aggregate model that exhibits a rich diversity of structures: amorphous/disordered aggregates, beta-barrels, multi-layered fibrils, and aggregates of mixed type. Our model has a tunable backbone stiffness that governs the propensity to form fibrils in bulk solution. In this work, we investigate how this β-sheet propensity couples with the properties of a surface template to influence the mechanism of aggregation. Here, we focus on peptide aggregation in the presence of three templates: a solid surface, the surface of a pre-existing aggregate seed, and a lipid bilayer. Aggregation on solid hydrophilic or hydrophobic surfaces frequently occurs in many experimental setups. We find that the solid surface strongly biases toward the formation of fibrillar aggregates. Peptide-peptide interactions and surface attraction couple cooperatively on a solid surface to influence the bind- vi ing/aggregation transition. Aggregation and binding occur almost simultaneously since the surface’s crystal symmetry enforces a preferred direction of bound fibril growth, thus accelerating the process. Seeding peptides with compatible aggregates removes the nucleation bar- rier for aggregation. We find that the aggregation mechanism is strongly de- pendent on the β-sheet propensity of both the seed and bulk peptides. Addi- tionally, bulk peptides that exhibit polymorphism can have multiple pathways to aggregation depending on which class of aggregate they initially form. We find that a fibrillar seed can induce amorphous-prone peptides into fibrillar structures via a condensation-ordering mechanism, thus sequestering potentially cytotoxic oligomers into a more inert form. We simulate aggregation on lipid bilayers in an effort to approximate the complexity of the cellular milieu. While aggregation in vivo would occur in the presence of membrane surfaces, few simulation studies have been conducted on this combined system due to its computational complexity. We have determined that a membrane surface, like a crystal surface, biases toward fibrillar aggregates. However, membrane undulations disturb multi-layered fibrils into non-planar β- sheet structures, such as β-barrels. The presence of fibrils on the membrane also affects its fluid properties, creating a hexagonally packed lipid ordering underneath the fibrils, locally increasing its bending modulus and aligning lipid tilt to the orientation of the peptides. Thus peptide aggregation and membrane fluidity affect each other’s structure and dynamics. The key general features of a surface that control its modulation of peptide aggregation are its structural order and fluidity. An ordered, rigid template biases more strongly toward fibrillar structures and restricts the set of aggregation pathways and morphologies. The dynamic nature of a fluid surface biases less toward vii fibrils and enhances the range of aggregation dynamics. viii Contents 1 Introduction 1 1.1 Coarse-Graining............................ 1 1.2 Proteins ................................ 4 1.3 OurPeptideModel .......................... 9 1.4 Atomistic Simulations of Monomers, Small Oligomers, and Mature Fibrils ................................. 12 1.4.1 Coarse-Grained Models of Protein Aggregation . 17 1.4.2 Aggregation in the Cellular Milieu . 23 1.5 ConcludingThoughts......................... 25 2 Methods 27 2.1 PeptideModel............................. 27 2.1.1 ModelGeometry ....................... 27 2.1.2 Potentials ........................... 28 2.1.3 NonbondedInteractions . 30 2.1.4 Peptide-SurfaceInteractions . 30 2.2 LipidModel.............................. 33 2.3 Langevin Dynamics Implicit Solvent Model . 35 2.4 Replica Exchange Molecular Dynamics . 37 ix 3 Thermodynamics of Peptide Aggregation on a Solid Surface 40 3.1 Methods................................ 43 3.1.1 OrderParameters....................... 44 3.2 Results................................. 47 3.2.1 Peptides adopt different aggregate structures in the bulk thanonsurfaces........................ 47 3.2.2 Binding transition sharpened by surface attraction and chi- ralstiffness .......................... 50 3.2.3 Isolating Interactions Highlights Aggregation/Binding Co- operativity........................... 55 3.3 SummaryandDiscussion....................... 60 4 Kinetics of Peptide Aggregation on a Solid Surface 63 4.1 Methods................................ 64 4.1.1 OrderParameters....................... 64 4.1.2 Simulations . 65 4.2 Results................................. 67 4.2.1 Formation of a Single Layer . 67 4.2.2 OtherAggregateStructures . 77 4.2.3 Order Parameter Equilibration Rates Indicate Cooperativity 79 4.2.4 Diffusion............................ 82 4.3 SummaryandDiscussion....................... 83 5 Seeded Peptide Aggregation 87 5.1 Methods................................ 90 5.1.1 Simulations . 90 5.1.2 OrderParameters....................... 90 x 5.2 Results................................. 91 5.2.1 Case 1: Homogeneous Seeding and Aggregation: Fibril Seed (Kχ = 2 kcal/mol) and Free Peptides of High β-Sheet Propen- sity (Kχ = 2 kcal/mol).................... 92 5.2.2 Case 2: Heterogeneous Seeding and Aggregation: Fibril Seed (Kχ = 2 kcal/mol) with Flexible Peptides (Kχ = 1 kcal/mol) .......................... 93 5.2.3 Case 3: Heterogeneous Seeding and Aggregation: Fibril Seed (Kχ = 2 kcal/mol and Free Peptides with Interme- diate β-Sheet Propensity (Kχ =1.5 kcal/mol) ....... 94 5.2.4 Case 4: Heterogeneous Seeding and Aggregation: Amor- phous Seed (Kχ = 1 kcal/mol) with Rigid Peptides (Kχ =

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