Combination of Markov state models and kinetic networks for the analysis of molecular dynamics simulations of peptide folding

pH-Dependent Behavior of Ionizable Cationic Lipids in mRNA-Carrying Lipoplexes Investigated by Molecular Dynamics Simulations

Lipid-based nanoparticles and lipoplexes containing ionizable lipids are among the most successful nanocarriers for mRNA-based therapies. The molecular structure of these assemblies is still not fully understood, as well as the role played by the ionizable lipids. SAXS experiments have shown that lipoplexes including the ionizable lipid 2-dioleyloxy-N,N-dimethyl-3-aminopropane (DODMA), under specific conditions, have a lamellar structure, where lipid bilayers are separated by mRNA-rich layers, with an overall spacing between 6.5 and 8.0 nm and a complex pH-dependence. Here, the structure and dynamics of these lipoplexes are investigated at varying pH and mRNA concentration using multiscale molecular dynamics simulations. It is observed that the interaction between DODMA and RNA is slightly attractive only at low pH levels, while it becomes effectively repulsive at high and intermediate pH. This results into a pH-dependent relocation of the RNA inside the multilayers, from the lipid head groups at low pH to a more uniform distribution inside the hydrophilic slabs of the multilayers at high pH. It is also observed that at high pH, DODMA lipids shift toward the hydrophobic part of the bilayer, consequently increasing their leaflet-flipping rate, a phenomenon which may ultimately affect the fusion process of the lipoplex with the endosomal membrane.

Poly-sarcosine and Poly(Ethylene-Glycol) Interactions with Proteins Investigated Using Molecular Dynamics Simulations

Nanoparticles coated with hydrophilic polymers often show a reduction in unspecific interactions with the biological environment, which improves their biocompatibility. The molecular determinants of this reduction are not very well understood yet, and their knowledge may help improving nanoparticle design. Here we address, using molecular dynamics simulations, the interactions of human serum albumin, the most abundant serum protein, with two promising hydrophilic polymers used for the coating of therapeutic nanoparticles, poly(ethylene-glycol) and poly-sarcosine. By simulating the protein immersed in a polymer-water mixture, we show that the two polymers have a very similar affinity for the protein surface, both in terms of the amount of polymer adsorbed and also in terms of the type of amino acids mainly involved in the interactions. We further analyze the kinetics of adsorption and how it affects the polymer conformations. Minor differences between the polymers are observed in the thickness of the adsorption layer, that are related to the different degree of flexibility of the two molecules. In comparison poly-alanine, an isomer of poly-sarcosine known to self-aggregate and induce protein aggregation, shows a significantly larger affinity for the protein surface than PEG and PSar, which we show to be related not to a different patterns of interactions with the protein surface, but to the different way the polymer interacts with water.

Determining the Energetics of Small β-Sheet Peptides using Adaptive Steered Molecular Dynamics

Mechanically driven unfolding is a useful computational tool for extracting the energetics and stretching pathway of peptides. In this work, two representative β-hairpin peptides, chignolin (PDB: 1UAO ) and trpzip1 (PDB: 1LE0 ), were investigated using an adaptive variant of the original steered molecular dynamics method called adaptive steered molecular dynamics (ASMD). The ASMD method makes it possible to perform energetic calculations on increasingly complex biological systems. Although the two peptides are similar in length and have similar secondary structures, their unfolding energetics are quite different. The hydrogen bonding profile and specific residue pair interaction energies provide insight into the differing stabilities of these peptides and reveal which of the pairs provides the most significant stabilization.

Simulations and experiments in protein folding

The interplay between simulations and experiments of protein folding has largely contributed to the elucidation of many important aspects of the phenomenon. In this chapter, I briefly describe the experiments which provide information on the kinetics of the protein folding process, and help to characterize the folding transition state. Then, I show how to probe the kinetics of protein folding using molecular dynamics simulations, how to compare the simulations with the experiments and how to help and rationalize the latter, ultimately offering a molecular picture of the process. After the production of suitable molecular dynamics simulation data in the form of trajectories, the procedure involves sequentially the identification of the stable states of the protein, the identification of the transition pathways connecting the stable states, the identification of the transition state conformations, comparison with experimental results, and finally, the identification of the molecular determinants or reaction coordinates of the folding process, that is, the features that clearly help distinguishing the transition state from the stable states.

Protein structure prediction: assembly of secondary structure elements by basin-hopping

The prediction of protein tertiary structure from primary structure remains a challenging task. One possible approach to this problem is the application of basin-hopping global optimization combined with an all-atom force field. In this work, the efficiency of basin-hopping is improved by introducing an approach that derives tertiary structures from the secondary structure assignments of individual residues. This approach is termed secondary-to-tertiary basin-hopping and benchmarked for three miniproteins: trpzip, trp-cage and ER-10. For each of the three miniproteins, the secondary-to-tertiary basin-hopping approach successfully and reliably predicts their three-dimensional structure. When it is applied to larger proteins, correctly folded structures are obtained. It can be concluded that the assembly of secondary structure elements using basin-hopping is a promising tool for de novo protein structure prediction.

On the Application of Molecular-Dynamics Based Markov State Models to Functional Proteins

Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.

Protein structure prediction using global optimization by basin-hopping with NMR shift restraints

Computational methods that utilize chemical shifts to produce protein structures at atomic resolution have recently been introduced. In the current work, we exploit chemical shifts by combining the basin-hopping approach to global optimization with chemical shift restraints using a penalty function. For three peptides, we demonstrate that this approach allows us to find near-native structures from fully extended structures within 10,000 basin-hopping steps. The effect of adding chemical shift restraints is that the α and β secondary structure elements form within 1000 basin-hopping steps, after which the orientation of the secondary structure elements, which produces the tertiary contacts, is driven by the underlying protein force field. We further show that our chemical shift-restraint BH approach also works for incomplete chemical shift assignments, where the information from only one chemical shift type is considered. For the proper implementation of chemical shift restraints in the basin-hopping approach, we determined the optimal weight of the chemical shift penalty energy with respect to the CHARMM force field in conjunction with the FACTS solvation model employed in this study. In order to speed up the local energy minimization procedure, we developed a function, which continuously decreases the width of the chemical shift penalty function as the minimization progresses. We conclude that the basin-hopping approach with chemical shift restraints is a promising method for protein structure prediction.