Folding simulations of three proteins having all α-helix, all β-strand and α/β-structures

PEGylation of Insulin and Lysozyme To Stabilize against Thermal Denaturation: A Molecular Dynamics Simulation Study

Biologic drugs or "biologics" (proteins derived from living organisms) are one of the fastest-growing classes of FDA-approved therapeutics. These compounds are often fragile and require conjugation to polymers for stabilization, with many proteins too ephemeral for therapeutic use. During storage or administration, proteins tend to unravel and lose their secondary structure due to changes in solution temperature, pH, and other external stressors. To enhance their lifetime, protein drugs currently in the market are conjugated with polyethylene glycol (PEG), owing to its ability to increase the stability, solubility, and pharmacokinetics of protein drugs. Here, we perform all-atom molecular dynamics simulations to study the unfolding process of egg-white lysozyme and insulin at elevated temperatures. We test the validity of two force fields─CHARMM36 and Amber ff99SB-ILDN─in the unfolding process. By calculating global and local properties, we capture residues that deteriorate first─these are the "weak links" in the proteins. Next, we conjugate both proteins with PEG and find that PEG preserves the native structure of the proteins at elevated temperatures by blocking water molecules from entering the hydrophobic core, thereby causing the secondary structure to stabilize.

Effect of the Force Field on Molecular Dynamics Simulations of the Multidrug Efflux Protein P-Glycoprotein

Molecular dynamics (MD) simulations have been used extensively to study P-glycoprotein (P-gp), a flexible multidrug transporter that is a key player in the development of multidrug resistance to chemotherapeutics. A substantial body of literature has grown from simulation studies that have employed various simulation conditions and parameters, including AMBER, CHARMM, OPLS, GROMOS, and coarse-grained force fields, drawing conclusions from simulations spanning hundreds of nanoseconds. Each force field is typically parametrized and validated on different data and observables, usually of small molecules and peptides; there have been few comparisons of force field performance on large protein-membrane systems. Here we compare the conformational ensembles of P-gp embedded in a POPC/cholesterol bilayer generated over 500 ns of replicate simulation with five force fields from popular biomolecular families: AMBER 99SB-ILDN, CHARMM 36, OPLS-AA/L, GROMOS 54A7, and MARTINI. We find considerable differences among the ensembles with little conformational overlap, although they correspond to similar extents to structural data obtained from electron paramagnetic resonance and cross-linking studies. Moreover, each trajectory was still sampling new conformations at a high rate after 500 ns of simulation, suggesting the need for more sampling. This work highlights the need to consider known limitations of the force field used (e.g., biases toward certain secondary structures) and the simulation itself (e.g., whether sufficient sampling has been achieved) when interpreting accumulated results of simulation studies of P-gp and other transport proteins.

Effects of All-Atom Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of Aβ16-22 Dimer

We investigated the effects of 17 widely used atomistic molecular mechanics force fields (MMFFs) on the structures and kinetics of amyloid peptide assembly. To this end, we performed large-scale all-atom molecular dynamics simulations in explicit water on the dimer of the seven-residue fragment of the Alzheimer's amyloid-β peptide, Aβ16-22, for a total time of 0.34 ms. We compared the effects of these MMFFs by analyzing various global reaction coordinates, secondary structure contents, the fibril population, the in-register and out-of-register architectures, and the fibril formation time at 310 K. While the AMBER94, AMBER99, and AMBER12SB force fields do not predict any β-sheets, the seven force fields, AMBER96, GROMOS45a3, GROMOS53a5, GROMOS53a6, GROMOS43a1, GROMOS43a2, and GROMOS54a7, form β-sheets rapidly. In contrast, the following five force fields, AMBER99-ILDN, AMBER14SB, CHARMM22*, CHARMM36, and CHARMM36m, are the best candidates for studying amyloid peptide assembly, as they provide good balances in terms of structures and kinetics. We also investigated the assembly mechanisms of dimeric Aβ16-22 and found that the fibril formation rate is predominantly controlled by the total β-strand content.

Force-Field Induced Bias in the Structure of Aβ21-30: A Comparison of OPLS, AMBER, CHARMM, and GROMOS Force Fields

In this work we examine the dynamics of an intrinsically disordered protein fragment of the amyloid β, the Aβ21-30, under seven commonly used molecular dynamics force fields (OPLS-AA, CHARMM27-CMAP, AMBER99, AMBER99SB, AMBER99SB-ILDN, AMBER03, and GROMOS53A6), and three water models (TIP3P, TIP4P, and SPC/E). We find that the tested force fields and water models have little effect on the measures of radii of gyration and solvent accessible surface area (SASA); however, secondary structure measures and intrapeptide hydrogen-bonding are significantly modified, with AMBER (99, 99SB, 99SB-ILDN, and 03) and CHARMM22/27 force-fields readily increasing helical content and the variety of intrapeptide hydrogen bonds. On the basis of a comparison between the population of helical and β structures found in experiments, our data suggest that force fields that suppress the formation of helical structure might be a better choice to model the Aβ21-30 peptide.