Advances in the Simulation of Protein Aggregation at the Atomistic Scale

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

Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly.

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

Extension of the FACTS Implicit Solvation Model to Membranes

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

On the Applicability of Force Fields To Study the Aggregation of Amyloidogenic Peptides Using Molecular Dynamics Simulations

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

Oligomer Formation of Toxic and Functional Amyloid Peptides Studied with Atomistic Simulations

Amyloids are associated with diseases, including Alzheimer’s, as well as functional roles such as storage of peptide hormones. It is still unclear what differences exist between aberrant and functional amyloids. However, it is known that soluble oligomers formed during amyloid aggregation are more toxic than the final fibrils. Here, we perform molecular dynamics simulations to study the aggregation of the amyloid-β peptide Aβ25–35, associated with Alzheimer’s disease, and two functional amyloid-forming tachykinin peptides: kassinin and neuromedin K. Although the three peptides have similar primary sequences, tachykinin peptides, in contrast to Aβ25–35, form nontoxic amyloids. Our simulations reveal that the charge of the C-terminus is essential to controlling the aggregation process. In particular, when the kassinin C-terminus is not amidated, the aggregation kinetics decreases considerably. In addition, we observe that the monomeric peptides in extended conformations aggregate faster than those in collapsed hairpin-like conformations

How accurately do force fields represent protein side chain ensembles?

Although the protein backbone is the most fundamental part of the structure, the fine-tuning of side-chain conformations is important for protein function, for example, in protein-protein and protein-ligand interactions, and also in enzyme catalysis. While several benchmarks testing the performance of protein force fields for side chain properties have already been published, they often considered only a few force fields and were not tested against the same experimental observables; hence, they are not directly comparable. In this work, we explore the ability of twelve force fields, which are different flavors of AMBER, CHARMM, OPLS, or GROMOS, to reproduce average rotamer angles and rotamer populations obtained from extensive NMR studies of the 3 J and residual dipolar coupling constants for two small proteins: ubiquitin and GB3. Based on a total of 196 μs sampling time, our results reveal that all force fields identify the correct side chain angles, while the AMBER and CHARMM force fields clearly outperform the OPLS and GROMOS force fields in estimating rotamer populations. The three best force fields for representing the protein side chain dynamics are AMBER 14SB, AMBER 99SB*-ILDN, and CHARMM36. Furthermore, we observe that the side chain ensembles of buried amino acid residues are generally more accurately represented than those of the surface exposed residues

Superhéroes y superheroínas del Santa Engracia

Convocatoria Proyectos de innovación de Extremadura 2017/2018El CEIP Santa Engracia (Badajoz) está ubicado en un barrio marginal de la ciudad, en un entorno con un nivel socioeconómico y cultural bajo, rodeado de circustancias que afectan tanto a los alumnos como a sus familias y que le lleva a ser catalogado como un Centro de Atención Preferente. Atendiendo a estas características se lleva a cabo una innovación que tiene como centro de interés a los superhéroes y superheroínas de los comics para promover los valores implícitos en estos personajes: el esfuerzo, el respeto, la justicia, la tolerancia, la solidaridad, la ayuda y la empatía. Las actividades realizadas tienen además los siguientes objetivos: motivar al alumnado y reducir el absentimo escolar partiendo de sus intereses y dotando a los contenidos del curriculum académico de un carácter más lúdico; reducir los conflictos que surgen a diario proporcionándoles las habilidades necesarias para ser buenos compañeros y futuros alumnos mediadores en los conflictos; promover la lectura; desarrollar su competencia lingüística, social y emocional y fomentar la colaboración e implicación familiar en el proceso educativo de sus hijosExtremaduraES