Protein aggregation into highly structured amyloid fibrils is associated with various diseases including Alzheimer’s disease, Parkinson’s disease, and type II diabetes. Amyloids can also have normal ...biological functions and, in the future, could be used as the basis for novel nanoscale materials. However, a full understanding of the physicochemical forces that drive protein aggregation is still lacking. Such understanding is crucial for the development of drugs that can effectively inhibit aberrant amyloid aggregation and for the directed design of functional amyloids. Atomistic simulations can help understand protein aggregation. In particular, atomistic simulations can be used to study the initial formation of toxic oligomers which are hard to characterize experimentally and to understand the difference in aggregation behavior between different amyloidogenic peptides. Here, we review the latest atomistic simulations of protein aggregation, concentrating on amyloidogenic protein fragments, and provide an outlook for the future in this field.
Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's disease or ...type II diabetes. Because of their lack of a stable three‐dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN‐NMR and CHARMM22*, in their ability to model Aβ42, an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN‐NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields.
Molecular dynamics simulations are commonly used to complement experimental studies in the understanding of intrinsically disordered proteins. The validity of these simulations depends on the accuracy of the force fields used to model the system. Here, we compare the accuracy of multiple force fields in representing A
β42, an intrinsically disordered protein associated with Alzheimer's disease.
Molecular dynamics simulations play an essential role in understanding biomolecular processes such as protein aggregation at temporal and spatial resolutions which are not attainable by experimental ...methods. For a correct modeling of protein aggregation, force fields must accurately represent molecular interactions. Here, we study the effect of five different force fields on the oligomer formation of Alzheimer’s Aβ16–22 peptide and two of its mutants: Aβ16–22(F19V,F20V), which does not form fibrils, and Aβ16–22(F19L) which forms fibrils faster than the wild type. We observe that while oligomer formation kinetics depends strongly on the force field, structural properties, such as the most relevant protein–protein contacts, are similar between them. The oligomer formation kinetics obtained with different force fields differ more from each other than the kinetics between aggregating and nonaggregating peptides simulated with a single force field. We discuss the difficulties in comparing atomistic simulations of amyloid oligomer formation with experimental observables.
Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's disease or ...type II diabetes. Because of their lack of a stable three-dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN-NMR and CHARMM22*, in their ability to model Abeta42, an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN-NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields. Molecular dynamics simulations are commonly used to complement experimental studies in the understanding of intrinsically disordered proteins. The validity of these simulations depends on the accuracy of the force fields used to model the system. Here, we compare the accuracy of multiple force fields in representing A beta 42, an intrinsically disordered protein associated with Alzheimer's disease.
Phenotypic delay-the time delay between genetic mutation and expression of the corresponding phenotype-is generally neglected in evolutionary models, yet recent work suggests that it may be more ...common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.
Markov state models have become popular in the computational biochemistry and biophysics communities as a technique for identifying stationary and kinetic information of protein dynamics from ...molecular dynamics simulation data. In this paper, we extend the applicability of automated Markov state modeling to simulation data of molecular self-assembly and aggregation by constructing collective coordinates from molecular descriptors that are invariant to permutations of molecular indexing. Understanding molecular self-assembly is of critical importance if we want to deepen our understanding of neurodegenerative diseases where the aggregation of misfolded or disordered proteins is thought to be the main culprit. As a proof of principle, we demonstrate our Markov state model technique on simulations of the KFFE peptide, a subsequence of Alzheimer's amyloid-β peptide and one of the smallest peptides known to aggregate into amyloid fibrils in vitro. We investigate the different stages of aggregation up to tetramerization and show that the Markov state models clearly map out the different aggregation pathways. Of note is that disordered and β-sheet oligomers do not interconvert, leading to separate pathways for their formation. This suggests that amyloid aggregation of KFFE occurs via ordered aggregates from the very beginning. The code developed here is freely available as a Jupyter notebook called TICAgg, which can be used for the automated analysis of any self-assembling molecular system, protein, or otherwise.
Biofouling of marine surfaces such as ship hulls is a major industrial problem. Antifouling (AF) paints delay the onset of biofouling by releasing biocidal chemicals. We present a computational model ...for microbial colonization of a biocide-releasing AF surface. Our model accounts for random arrival from the ocean of microorganisms with different biocide resistance levels, biocide-dependent proliferation or killing, and a transition to a biofilm state. Our computer simulations support a picture in which biocide-resistant microorganisms initially form a loosely attached layer that eventually transitions to a growing biofilm. Once the growing biofilm is established, immigrating microorganisms are shielded from the biocide, allowing more biocide-susceptible strains to proliferate. In our model, colonization of the AF surface is highly stochastic. The waiting time before the biofilm establishes is exponentially distributed, suggesting a Poisson process. The waiting time depends exponentially on both the concentration of biocide at the surface and the rate of arrival of resistant microorganisms from the ocean. Taken together our results suggest that biofouling of AF surfaces may be intrinsically stochastic and hence unpredictable, but immigration of more biocide-resistant species, as well as the biological transition to biofilm physiology, may be important factors controlling the time to biofilm establishment.
The generalized Born (GB) formalism can be used to model water as a dielectric continuum. Among the different implicit solvent models using the GB formalism, FACTS is one of the fastest. Here, we ...extend FACTS so that it can represent a membrane environment. This extension is accomplished by considering a position dependent dielectric constant and empirical surface tension parameter. For the calculation of the effective Born radii in different dielectric environments we present a parameter-free approximation to Kirkwood’s equation, which uses the Born radii obtained with FACTS for the water environment as input. This approximation is tested for the calculation of self-free energies, pairwise interaction energies in solution and solvation free energies of complete protein conformations. The results compare well to those from the finite difference Poisson method. The new implicit membrane model is applied to estimate free energy insertion profiles of amino acid analogues and in molecular dynamics simulations of melittin, WALP23 and KALP23, glycophorin A, bacteriorhodopsin, and a Clc channel dimer. In all cases, the results agree qualitatively with experiments and explicit solvent simulations. Moreover, the implicit membrane model is only six times slower than a vacuum simulation.
Abstract
Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's ...disease or type II diabetes. Because of their lack of a stable three‐dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN‐NMR and CHARMM22*, in their ability to model Aβ
42
, an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN‐NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields.
Molecular dynamics simulations are commonly used to complement experimental studies in the understanding of intrinsically disordered proteins. The validity of these simulations depends on the accuracy of the force fields used to model the system. Here, we compare the accuracy of multiple force fields in representing A
, an intrinsically disordered protein associated with Alzheimer's disease.
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