Phuong H. Nguyen

Monomer adds to preformed structured oligomers of Aβ-peptides by a two-stage dock–lock mechanism

Nonfibrillar soluble oligomers, which are intermediates in the transition from monomers to amyloid fibrils, may be the toxic species in Alzheimers disease. To monitor the early events that direct assembly of amyloidogenic peptides we probe the dynamics of formation of (Aβ16–22)n by adding a monomer to a preformed (Aβ16–22)n−1 (n = 4–6) oligomer in which the peptides are arranged in an antiparallel β-sheet conformation. All atom molecular dynamics simulations in water and multiple long trajectories, for a cumulative time of 6.9 μs, show that the oligomer grows by a two-stage dock–lock mechanism. The largest conformational change in the added disordered monomer occurs during the rapid (≈50 ns) first dock stage in which the β-strand content of the monomer increases substantially from a low initial value. In the second slow-lock phase, the monomer rearranges to form in register antiparallel structures. Surprisingly, the mobile structured oligomers undergo large conformational changes in order to accommodate the added monomer. The time needed to incorporate the monomer into the fluid-like oligomer grows even when n = 6, which suggests that the critical nucleus size must exceed six. Stable antiparallel structure formation exceeds hundreds of nanoseconds even though frequent interpeptide collisions occur at elevated monomer concentrations used in the simulations. The dock–lock mechanism should be a generic mechanism for growth of oligomers of amyloidogenic peptides.

Energy landscape of a small peptide revealed by dihedral angle principal component analysis

A 100 ns molecular dynamics simulation of penta‐alanine in explicit water is performed to study the reversible folding and unfolding of the peptide. Employing a standard principal component analysis (PCA) using Cartesian coordinates, the resulting free‐energy landscape is found to have a single minimum, thus suggesting a simple, relatively smooth free‐energy landscape. Introducing a novel PCA based on a transformation of the peptide dihedral angles, it is found, however, that there are numerous free energy minima of comparable energy (≲ 1 kcal/mol), which correspond to well‐defined structures with characteristic hydrogen‐bonding patterns. That is, the true free‐energy landscape is actually quite rugged and its smooth appearance in the Cartesian PCA represents an artifact of the mixing of internal and overall motion. Well‐separated minima corresponding to specific conformational structures are also found in the unfolded part of the free energy landscape, revealing that the unfolded state of penta‐alanine is structured rather than random. Performing a connectivity analysis, it is shown that neighboring states are connected by low barriers of similar height and that each state typically makes transitions to three or four neighbor states. Several principal pathways for helix nucleation are identified and discussed in some detail. Proteins 2005.

Amyloid β Protein and Alzheimer’s Disease: When Computer Simulations Complement Experimental Studies

Simulations Complement Experimental Studies Jessica Nasica-Labouze,† Phuong H. Nguyen,† Fabio Sterpone,† Olivia Berthoumieu,‡ Nicolae-Viorel Buchete, Sebastien Cote, Alfonso De Simone, Andrew J. Doig, Peter Faller,‡ Angel Garcia, Alessandro Laio, Mai Suan Li, Simone Melchionna, Normand Mousseau, Yuguang Mu, Anant Paravastu, Samuela Pasquali,† David J. Rosenman, Birgit Strodel, Bogdan Tarus,† John H. Viles, Tong Zhang,†,▲ Chunyu Wang, and Philippe Derreumaux*,†,□ †Laboratoire de Biochimie Theorique, Institut de Biologie Physico-Chimique (IBPC), UPR9080 CNRS, Universite Paris Diderot, Sorbonne Paris Cite, 13 rue Pierre et Marie Curie, 75005 Paris, France ‡LCC (Laboratoire de Chimie de Coordination), CNRS, Universite de Toulouse, Universite Paul Sabatier (UPS), Institut National Polytechnique de Toulouse (INPT), 205 route de Narbonne, BP 44099, Toulouse F-31077 Cedex 4, France School of Physics & Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland Deṕartement de Physique and Groupe de recherche sur les proteines membranaires (GEPROM), Universite de Montreal, C.P. 6128, succursale Centre-ville, Montreal, Quebec H3C 3T5, Canada Department of Life Sciences, Imperial College London, London SW7 2AZ, United Kingdom Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom Department of Physics, Applied Physics, & Astronomy, and Department of Biology, Rensselaer Polytechnic Institute, Troy, New York 12180, United States The International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland Institute for Computational Science and Technology, SBI Building, Quang Trung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City, Vietnam Instituto Processi Chimico-Fisici, CNR-IPCF, Consiglio Nazionale delle Ricerche, 00185 Roma, Italy School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, 637551 Singapore Department of Chemical and Biomedical Engineering, Florida A&M University-Florida State University (FAMU-FSU) College of Engineering, 2525 Pottsdamer Street, Tallahassee, Florida 32310, United States National High Magnetic Field Laboratory, 1800 East Paul Dirac Drive, Tallahassee, Florida 32310, United States Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Julich GmbH, 52425 Julich, Germany School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom Institut Universitaire de France, 75005 Paris, France

Dihedral angle principal component analysis of molecular dynamics simulations

It has recently been suggested by Mu et al. [Proteins 58, 45 (2005)] to use backbone dihedral angles instead of Cartesian coordinates in a principal component analysis of molecular dynamics simulations. Dihedral angles may be advantageous because internal coordinates naturally provide a correct separation of internal and overall motion, which was found to be essential for the construction and interpretation of the free energy landscape of a biomolecule undergoing large structural rearrangements. To account for the circular statistics of angular variables, a transformation from the space of dihedral angles {phi(n)} to the metric coordinate space {x(n)=cos phi(n),y(n)=sin phi(n)} was employed. To study the validity and the applicability of the approach, in this work the theoretical foundations underlying the dihedral angle principal component analysis (dPCA) are discussed. It is shown that the dPCA amounts to a one-to-one representation of the original angle distribution and that its principal components can readily be characterized by the corresponding conformational changes of the peptide. Furthermore, a complex version of the dPCA is introduced, in which N angular variables naturally lead to N eigenvalues and eigenvectors. Applying the methodology to the construction of the free energy landscape of decaalanine from a 300 ns molecular dynamics simulation, a critical comparison of the various methods is given.

Energy transport in peptide helices

We investigate energy transport through an α-aminoisobutyric acid-based 310-helix dissolved in chloroform in a combined experimental-theoretical approach. Vibrational energy is locally deposited at the N terminus of the helix by ultrafast internal conversion of a covalently attached, electronically excited, azobenzene moiety. Heat flow through the helix is detected with subpicosecond time resolution by employing vibrational probes as local thermo meters at various distances from the heat source. The experiment is supplemented by detailed nonequilibrium molecular dynamics (MD) simulations of the process, revealing good qualitative agreement with experiment: Both theory and experiment exhibit an almost instantaneous temperature jump of the reporter units next to the heater which is attributed to the direct impact of the isomerizing azobenzene moiety. After this impact event, helix and azobenzene moiety appear to be thermally decoupled. The energy deposited in the helix thermalizes on a subpicosecond timescale and propagates along the helix in a diffusive-like process, accompanied by a significant loss into the solvent. However, in terms of quantitative numbers, theory and experiment differ. In particular, the MD simulation seems to overestimate the heat diffusion constant (2 Å2 ps−1 from the experiment) by a factor of five.

Free energy landscape and folding mechanism of a β‐hairpin in explicit water: A replica exchange molecular dynamics study

The free energy landscape and the folding mechanism of the C‐terminal β‐hairpin of protein G is studied by extensive replica exchange molecular dynamics simulations (40 replicas and 340 ns total simulation time), using the GROMOS96 force field and the SPC explicit water solvent. The study reveals that the system preferentially adopts a β‐hairpin structure at biologically important temperatures, and that the helix content is low at all temperatures studied. Representing the free energy landscape as a function of several types of reaction coordinates, four local minima corresponding to the folded, partially folded, molten globule, and unfolded states are identified. The findings suggest that the folding of the β‐hairpin occurs as the sequence: collapse of hydrophobic core → formation of H‐bond → formation of the turn. Identifying the folded and molten globule states as the main conformations, the free energy landscape of the β‐hairpin is consistent with a two‐state behavior with a broad transition state. The temperature dependence of the folding–unfolding transition is investigated in some detail. The enthalpy and entropy jumps at the folding transition temperature are found to be about three times lower than the experimental estimates, indicating that the folding–unfolding transition in silico is less cooperative than its in vitro counterpart. Proteins 2005.

Construction of the free energy landscape of biomolecules via dihedral angle principal component analysis.

A systematic approach to construct a low-dimensional free energy landscape from a classical molecular dynamics (MD) simulation is presented. The approach is based on the recently proposed dihedral angle principal component analysis (dPCA), which avoids artifacts due to the mixing of internal and overall motions in Cartesian coordinates and circumvents problems associated with the circularity of angular variables. Requiring that the energy landscape reproduces the correct number, energy, and location of the systems metastable states and barriers, the dimensionality of the free energy landscape (i.e., the number of essential components) is obtained. This dimensionality can be determined from the distribution and autocorrelation of the principal components. By performing an 800 ns MD simulation of the folding of hepta-alanine in explicit water and using geometric and kinetic clustering techniques, it is shown that a five-dimensional dPCA energy landscape is a suitable and accurate representation of the full-dimensional landscape. In the second step, the dPCA energy landscape can be employed (e.g., in a Langevin simulation) to facilitate a detailed investigation of biomolecular dynamics in low dimensions. Finally, several ways to visualize the multidimensional energy landscape are discussed.

Structures of Aβ17–42 trimers in isolation and with five small-molecule drugs using a hierarchical computational procedure

The amyloid-β protein (Aβ) oligomers are believed to be the main culprits in the cytoxicity of Alzheimers disease (AD) and p3 peptides (Aβ17-42 fragments) are present in AD amyloid plaques. Many small-molecule or peptide-based inhibitors are known to slow down Aβ aggregation and reduce the toxicity in vitro, but their exact modes of action remain to be determined since there has been no atomic level of Aβ(p3)-drug oligomers. In this study, we have determined the structure of Aβ17-42 trimers both in aqueous solution and in the presence of five small-molecule inhibitors using a multiscale computational study. These inhibitors include 2002-H20, curcumin, EGCG, Nqtrp, and resveratrol. First, we used replica exchange molecular dynamics simulations coupled to the coarse-grained (CG) OPEP force field. These CG simulations reveal that the conformational ensemble of Aβ17-42 trimer can be described by 14 clusters with each peptide essentially adopting turn/random coil configurations, although the most populated cluster is characterized by one peptide with a β-hairpin at Phe19-Leu31. Second, these 14 dominant clusters and the less-frequent fibril-like state with parallel register of the peptides were subjected to atomistic Autodock simulations. Our analysis reveals that the drugs have multiple binding modes with different binding affinities for trimeric Aβ17-42 although they interact preferentially with the CHC region (residues 17-21). The compounds 2002-H20 and Nqtrp are found to be the worst and best binders, respectively, suggesting that the drugs may interfere at different stages of Aβ oligomerization. Finally, explicit solvent molecular dynamics of two predicted Nqtrp-Aβ17-42 conformations describe at atomic level some possible modes of action for Nqtrp.

Understanding amyloid fibril nucleation and aβ oligomer/drug interactions from computer simulations.

Evolution has fine-tuned proteins to accomplish a variety of tasks. Yet, with aging, some proteins assemble into harmful amyloid aggregates associated with neurodegenerative diseases, such as Alzheimers disease (AD), which presents a complex and costly challenge to our society. Thus, far, drug after drug has failed to slow the progression of AD, characterized by the self-assembly of the 39-43 amino acid β-amyloid (Aβ) protein into extracellular senile plaques that form a cross-β structure. While there is experimental evidence that the Aβ small oligomers are the primary toxic species, standard tools of biology have failed to provide structures of these transient, inhomogeneous assemblies. Despite extensive experimental studies, researchers have not successfully characterized the nucleus ensemble, the starting point for rapid fibril formation. Similarly scientists do not have atomic data to show how the compounds that reduce both fibril formation and toxicity in cells bind to Aβ42 oligomers. In this context, computer simulations are important tools for gaining insights into the self-assembly of amyloid peptides and the molecular mechanism of inhibitors. This Account reviews what analytical models and simulations at different time and length scales tell us about the dynamics, kinetics, and thermodynamics of amyloid fibril formation and, notably, the nucleation process. Though coarse-grained and mesoscopic protein models approximate atomistic details by averaging out unimportant degrees of freedom, they provide generic features of amyloid formation and insights into mechanistic details of the self-assembly process. The thermodynamics and kinetics vary from linear peptides adopting straight β-strands in fibrils to longer peptides adopting in parallel U shaped conformations in fibrils. In addition, these properties change with the balance between electrostatic and hydrophobic interactions and the intrinsic disorder of the system. However, simulations suggest that the critical nucleus size might be on the order of 20 chains under physiological conditions. The transition state might be characterized by a simultaneous change from mixed antiparallel/parallel β-strands with random side-chain packing to the final antiparallel or parallel states with the steric zipper packing of the side chains. Second, we review our current computer-based knowledge of the 3D structures of inhibitors with Aβ42 monomer and oligomers, a prerequisite for developing new drugs against AD. Recent extensive all-atom simulations of Aβ42 dimers with known inhibitors such as the green tea compound epigallocatechin-3-gallate and 1,4-naphthoquinon-2-yl-l-tryptophan provide a spectrum of initial Aβ42/inhibitor structures useful for screening and drug design. We conclude by discussing future directions that may offer opportunities to fully understand nucleation and further AD drug development.

Quantum-classical description of the amide I vibrational spectrum of trialanine

A quantum-classical description of the amide I vibrational spectrum of trialanine cation in D2O is given that combines (i) a classical molecular dynamics simulation of the conformational distribution of the system, (ii) comprehensive density functional theory calculations of the conformation-dependent and solvent-induced frequency fluctuations, and (iii) a semiclassical description of the vibrational line shapes which includes nonadiabatic transitions between vibrational eigenstates. Various assumptions that are usually employed in the calculation of condensed-phase vibrational spectra are tested, including the adiabatic, the Franck-Condon, and the second-order cumulant approximations, respectively. All three parts of the theoretical formulation are shown to have a significant impact on the simulated spectrum, suggesting that the interpretation of peptide amide I spectra may require substantial theoretical support.