Prion versus Doppel Protein Misfolding: New Insights from Replica-Exchange Molecular Dynamics Simulations

The doppel (Dpl) and prion (PrP) proteins share a very similar fold (three helices and two short beta-strands), while they differ significantly in sequence (only 25% homologous) and in disease-related beta-rich conformations that occur for PrP only. In a previous study [Baillod, P., et al. (2012) Biochemistry 51, 9891-9899], we investigated the misfolding and rare, beta-rich folds of monomeric PrP with replica-exchange molecular dynamics (REMD) simulations. In the work presented here, we perform analogous simulations for Dpl with the aim of comparing the two systems and characterizing possible specificities of PrP for misfolding and amyloidogenesis. Our extensive simulations, which allow us to overcome high energy barriers via the REMD approach, sample several beta-rich folds, some of which are stable at room temperature, for both proteins. Per residue secondary structure propensities reveal that novel beta-sheets of Dpl and PrP are formed by amino acids belonging to the helices that are the least stable in the respective native structure, H1 for Dpl and H2 and H3 for PrP, in agreement with experimental data. Using a specific clustering method that allows discrimination against different beta-strand arrangements, seven beta-rich folds could be characterized for PrP and five for Dpl, which are clearly distinct and share only one single similar fold. A major difference between the two proteins is found in the free energy barriers leading to misfolded structures: they are approximately 3 times higher for Dpl than for PrP. This suggests that the difference in amyloidogenic behavior between PrP and Dpl might be due to kinetic reasons.

Studying membrane-protein interplay by modeling realistic conditions

Martina Audagnotto

Realistic models of cellular environments have long captured the imagination of biologists and physical scientists. More than 20 years ago, Goodshellâs inspiring rendering of cellular environments provided the foundation for the idea of capturing the full complexity of biological cells. Since then, increasingly realistic simulations of subcellular models have appeared, showing that we are rapidly moving towards comprehensive, as well as physically and biologically accurate cellular models. The major challenge in developing such models is the necessity to cover a wide range of scales. Indeed, atomistic details of molecular processes are on the length scale of 0.1 nm, while cellular dimensions range from 300 nm (for the smallest bacterial cells) up to 100 ÎŒm (for large eukaryotic cells). The biological range also encompasses the internal dynamics of individual molecules (from ns to ms) as well as entire biological processes (ms to hours). Although an atomistic representation of an entire cell is conceivable, the cost for achieving biologically significant timescales with most advanced computers is very high or nearly impossible currently. Therefore, the combination of in silico approaches with in vivo and in vitro experimental data, allows gaining further insights into the biophysics of the cell. In this thesis, a multi-scale approach was applied to investigate the interplay between proteins and their respective biological environments. In particular, two specific systems were the objects of my Ph.D. project: (i) the amyloid precursor protein (APP) and its interactions at the synaptic plasma membrane, and (ii) the human acyl-protein thioesterase I (hAPT1) and its role in protein depalmitoylation cycle across the Golgi and plasma membranes. Combining atomistic and coarse-grained molecular dynamics (MD) simulations, the stability of the dimeric conformation of APP in a realistic model of the synaptic plasma membrane was investigated. Within this approach, the stable dimeric conformation of the APP as a function of lipid membrane composition was identified, highlighting the important role of lipids unsaturation in protein stability and function. In detail, this indicated not only the preferential dimerization motif of the protein, but also the possible recruiting mode of cholesterol, which has previously been experimentally observed to correlate with Alzheimerâs disease. Furthermore, the role of the synaptic plasma membrane and its ability to form raft microdomains was explored for the interactions of APP with Î³-secretase, which specific catalytic cleavage is responsible for the production of physiological or toxic amyloid Î² peptides, responsible for the Alzheimerâs disease onset. This same multi-scale approach was applied to understand the role of S-acylation in the trafficking and function of hAPT1. In particular, the combination of experimental techniques (i.e., cell biology, biochemistry, microscopy and structural biology) with molecular modeling and simulation disclosed the substrate/enzyme interaction mode, the enzyme/membrane interface, and the role for hAPT1 for a key post-translational modification as S-palmitoylation. Based on our findings, we provided for the first time an atomistic representation of the hAPT1 enzyme mechanism, suggesting that hAPT1 is active in the monomeric form. In conclusion, these studies demonstrate once more the importance of modeling the molecular details of the biological environment at the most accu

EPFL2017

Multiscale simulations of protein dynamics

Enrico Spiga

Characteristic timescales associated with the function of biomolecules, like proteins, range from femtoseconds up to minutes, whereas their corresponding spatial extent ranges from few ̊A to μm when associating in large macromolecular complexes. Moreover, biomolecules are functional in a large variety of different physico-chemical conditions strongly dependent on pH, ionic strength, crowding agents, etc. This huge complexity is hard to be studied with an arbitrary level of resolution embracing all these spatial and temporal scales. Molecular simulation is a well established approach to gain mechanistic insights into the function of biomolecular systems, producing atomistically detailed models of in vitro and/or in vivo conditions. I present in this thesis two projects that aim at improving on the current limitations of multiscale molecular simulations, namely (i) the sampling of large systems, and (ii) the detailed representation and description of realistic physiological conditions. Addressing the first issue, I propose a new coarse-grained model for proteins to be used in molecular dynamics simulations. This coarse-grained model is based on a more accurate description of protein electrostatics, which accounts for dipolar contributions. The parameterization of this force field is based on force-matching methods and on the use of a particle swarm optimization heuristic algorithm. The obtained results are encouraging being structural and electrostatic properties accurately reproduced with the coarse-grained model for a variety of protein folds. Moreover, the parameterization procedure can be straightforwardly applied to any protein, and can be extended to a larger dataset to generate a fully transferable coarse-grained force field, to be applied to any protein and any large macromolecular assemblies for which long all-atom simulations are still a challenge. While the development and use of coarse-grained models are important to tackle the limited sampling of large systems, it is still important to use all-atom molecular dynamics simulation to investigate with high accuracy in physiological conditions protein dynamics. For this reason, I present here the results of state-of-art molecular dynamics simulations applied to study the influence of crowding agents on the internal dynamics of the protein ubiquitin. Their analysis allows to describe how ubiquitin dynamics is slaved by crowding agents. This work demonstrates that the description of protein dynamics should take into account its intrinsic multiscale nature. The development and applications of coarse-grained models permit to simulate proteins at low computational cost, the use of atomistic simulations allows to accurately describe proteins in absence and presence of crowding agents, and both of them permit to highlight the essence of protein dynamics.

EPFL2013

The static and dynamic structural heterogeneities of B-DNA: extending Calladine-Dickerson rules

John Maddocks, Marco Pasi, Daiva Petkeviciute

We present a multi-laboratory effort to describe the structural and dynamical properties of duplex B-DNA under physiological conditions. By processing a large amount of atomistic molecular dynamics simulations, we determine the sequence-dependent structural properties of DNA as expressed in the equilibrium distribution of its stochastic dynamics. Our analysis includes a study of first and second moments of the equilibrium distribution, which can be accurately captured by a harmonic model, but with nonlocal sequence-dependence. We characterize the sequence-dependent choreography of backbone and base movements modulating the non-Gaussian or anharmonic effects manifested in the higher moments of the dynamics of the duplex when sampling the equilibrium distribution. Contrary to prior assumptions, such anharmonic deformations are not rare in DNA and can play a significant role in determining DNA conformation within complexes. Polymorphisms in helical geometries are particularly prevalent for certain tetranucleotide sequence contexts and are always coupled to a complex network of coordinated changes in the backbone. The analysis of our simulations, which contain instances of all tetranucleotide sequences, allow us to extend Calladine-Dickerson rules used for decades to interpret the average geometry of DNA, leading to a set of rules with quantitative predictive power that encompass nonlocal sequence-dependence and anharmonic fluctuations.