A new transfer free energy based implicit solvation model for the description of disordered and folded proteins

Most biological events occur on time scales that are difficult to access using conventional all-atom molecular dynamics simulations in explicit solvent. Implicit solvent techniques offer a promising solution to this problem, alleviating the computational cost associated with the simulation of large systems and accelerating the sampling compared to explicit solvent models. The substitution of water molecules by a mean field, however, introduces simplifications that may penalize accuracy and impede the prediction of certain physical properties. We demonstrate that existing implicit solvent models developed using a transfer free energy approach, while satisfactory at reproducing the folding behavior of globular proteins, fare less well in characterizing the conformational properties of intrinsically disordered proteins. We develop a new implicit solvent model that maximizes the degree of accuracy for both disordered and folded proteins. We show, by comparing the simulation outputs to experimental data, that in combination with the a99SB-disp force field, the implicit solvent model can describe both disordered (a beta 40, PaaA2, and drkN SH3) and folded ((AAQAA)(3), CLN025, Trp-cage, and GTT) peptides. Our implicit solvent model permits a computationally efficient investigation of proteins containing both ordered and disordered regions, as well as the study of the transition between ordered and disordered protein states. implicit solvent + a99SB-disp force field disordered proteins: 440 PaaA2 drkN SH3 20 a) `p 15 U 10 5 0 optimized approach appl app2 app3 app4 disordere folded & BULL; total fast-folding peptides: (AAQAA)3 CLN025 Trp-cage GT

Surface-driven denaturation of proteins during freeze-drying: An insight into the role of surfactants

Protein-based therapeutics may bind to interfaces during the freeze-drying process, possibly resulting in loss of activity. Here we investigate the mechanism by which surfactant molecules can counteract surface-induced denaturation through a detailed study of the stability of the GB1 peptide at the air-water, ice-water and silica-water interfaces. Using molecular dynamics simulations coupled with metadynamics we show that the amphiphilic nature of surfactants is key to their stabilizing/destabilizing effect, with an orientation-dependent mechanism in which the protein is stabilized when the hydrophilic heads of the surfactant point toward the protein

Force Field Parameterization for the Description of the Interactions between Hydroxypropyl-β-Cyclodextrin and Proteins

Cyclodextrins are cyclic oligosaccharides, widely used as drug carriers, solubilizers, and excipients. Among cyclodextrins, the functionalized derivative known as hydroxypropyl-β-cyclodextrin (HPβCD) offers several advantages due to its unique structural features. Its optimal use in pharmaceutical and medical applications would benefit from a molecular-level understanding of its behavior, as can be offered by molecular dynamics simulations. Here, we propose a set of parameters for all-atom simulations of HPβCD, based on the ADD force field for sugars developed in our group, and compare it to the original CHARMM36 description. Using Kirkwood-Buff integrals of binary HPβCD-water mixtures as target experimental data, we show that the ADD-based description results in a considerably improved prediction of HPβCD self-association and interaction with water. We then use the new set of parameters to characterize the behavior of HPβCD toward the different amino acids. We observe pronounced interactions of HPβCD with both polar and nonpolar moieties, with a special preference for the aromatic rings of tyrosine, phenylalanine, and tryptophan. Interestingly, our simulations further highlight a preferential orientation of HPβCD's hydrophobic cavity toward the backbone atoms of amino acids, which, coupled with a favorable interaction of HPβCD with the peptide backbone, suggest a propensity for HPβCD to denature proteins

Effect of Surfactants on Surface-Induced Denaturation of Proteins: An Insight from Molecular Dynamics

Proteins are marginally stable, and when they bind to interfaces the resulting conformational changes can lead to loss of biological activity. In order to stabilize proteins in experiments where surface-induced denaturation is an issue, surfactants are commonly used. [1] However, the mechanism by which they prevent surface-induced denaturation of proteins is not completely understood. In the present work, the folding of the GB1 hairpin (shown in Fig. 1a) at the air-water, silica-water and ice-water surfaces is investigated, in the presence and absence of the surfactant Tween 80. Atomistic molecular dynamics (MD) simulations, coupled with the enhanced sampling method known as parallel bias metadynamics (PBMetaD) [2], are used for this investigation. Our simulations reveal that GB1 is destabilized at the air-water and ice-water interfaces, but stabilized at the silica surface. Tween 80 stabilizes the protein at the air-water and ice-water surfaces (Fig. 1b), but slightly destabilizes the protein at the silica interface. The surfactant molecules bind to the air and silica surface, while they cluster around the protein in the case of ice. An orientation-dependent mechanism of the surfactants is also active, in which the protein is stabilized when the hydrophilic heads of the surfactant are oriented towards the protein, and destabilized when the hydrophobic tails point towards the peptide. The latter orientation stabilizes partially unfolded states of the protein, characterized by a larger non-polar surface area. The tails-toward-the-protein configuration is favored in a hydrophilic environment, explaining the mild destabilization observed at the silica-water interface. By contrast, the ice-water surface promotes the heads-toward-the-protein arrangement, that stabilizes the protein native structure. Finally, in the case of the air-water interface, the coating of the interface by the surfactant molecules, and the resulting inhibition of protein adsorption, accounts for the observed stabilization of the protein native structure. [3

Timescales of spike-train correlation for neural oscillators with common drive

We examine the effect of the phase-resetting curve (PRC) on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy, super-threshold stimulation. We use linear response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem, and contrast the results for Type I vs. Type II models and across the different timescales over which spike correlations can be assessed. We find that, on long timescales, Type I oscillators transfer correlations much more efficiently than Type II oscillators. On short timescales this trend reverses, with the relative efficiency switching at a timescale that depends on the mean and standard deviation of input currents. This switch occurs over timescales that could be exploited by downstream circuits