The progress towards understanding the molecular basis of Alzheimers's disease is strongly connected to elucidating the early aggregation events of the amyloid-β (Aβ) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of Aβ into oligomers with high spatial and temporal resolution. However, the results of an MD simulation can only be as good as the underlying force field. A recent study by our group showed that none of the force fields tested can distinguish between aggregation-prone and non-aggregating peptide sequences, producing the same and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as Aβ were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the Aβ 16−22 peptide and mutations of it as test case. We investigate their performance in modeling the monomeric state, the aggregation into oligomers, and the stability of the aggregation end product, i.e., the fibrillar state. A main finding is that changing the force field has a stronger effect on the simulated aggregation pathway than changing the peptide sequence. Also the new force fields are not able to reproduce the experimental aggregation propensity order of the peptides. Dissecting the various energy contributions shows that AMBER99SB-disp overestimates the interactions between the peptides and water, thereby inhibiting peptide aggregation.More promising results are obtained with CHARMM36m and especially its version with increased protein-water interactions. It is thus recommended to use this force field for peptide aggregation simulations and base future reparameterizations on it. IntroductionIntrinsically disordered proteins (IDPs) are a class of proteins which are either completely unstructured or contain large disordered regions in their native state, which comes with a high tendency towards self-assembly leading to non-toxic or toxic aggregates and fibrils that may be related to diseases. 1 One of the IDPs is the amyloid-beta peptide (Aβ), forming 2

The convergence of MD simulations is tested using varying measures for the intrinsically disordered amyloid-β peptide (Aβ). Markov state models show that 20–30 μs of MD is needed to reliably reproduce the thermodynamics and kinetics of Aβ.

Cellular membrane lipid composition is implicated in diseases and major biological functions in cells, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. Molecular dynamics (MD) simulations have been useful in understanding membrane systems, but they require significant computational resources and often suffer inaccuracies in model parameters. Applications of data-driven and machine learning methods, currently revolutionising many fields, are limited to membrane systems due to the lack of suitable training sets. Here we present the NMRlipids Databank---a community-driven, open-for-all database featuring programmatic access to quality evaluated atom-resolution MD simulations of lipid bilayers. The NMRlipids databank will benefit scientists in different disciplines by providing automatic ranking of simulations based on their quality against experiments, flexible implementations of data-driven and machine learning applications, and rapid access to simulation data via graphical user interface. To demonstrate the unlocked possibilities beyond current MD simulation studies, we analyzed how anisotropic diffusion of water and cholesterol flip-flop rates depend on membrane properties.

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The increasing recognition of the biochemical importance of glycosaminoglycans (GAGs) has in recent times made them the center of attention of recent research investigations. It became evident that subtle conformational factors play an important role in determining the relationship between the chemical composition of GAGs and their activity. Therefore, a thorough understanding of their structural flexibility is needed, which is addressed in this work by means of all-atom molecular dynamics (MD) simulations. Four major GAGs with different substitution patterns, namely hyaluronic acid as unsulphated GAG, heparan-6-sulphate, chondroitin-4-sulphate, and chondroitin-6-sulphate, were investigated to elucidate the influence of sulphation on the dynamical features of GAGs. Moreover, the effects of increasing NaCl and KCl concentrations were studied as well. Different structural parameters were determined from the MD simulations, in combination with a presentation of the free energy landscape of the GAG conformations, which allowed us to unravel the conformational fingerprints unique to each GAG. The largest effects on the GAG structures were found for sulphation at position 6, as well as binding of the metal ions in the absence of chloride ions to the carboxylate and sulphate groups, which both increase the GAG conformational flexibility.

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Protein disorder and aggregation play significant roles in the pathogenesis of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's disease. The end products of the aggregation process in these diseases are highly structured amyloid fibrils. Though in most cases small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces that drive protein aggregation is thus required if one aims for the rational design of drugs targeting the formation of amyloid oligomers. Among a multitude of biophysical and biochemical techniques that are employed for studying protein aggregation, molecular dynamics (MD) simulations at the atomic level provide the highest temporal and spatial resolution of this process, capturing key steps during the formation of amyloid oligomers. Here we provide a step-by-step guide for setting up, running, and analyzing MD simulations of aggregating peptides using GROMACS. For the analysis we provide the scripts that were developed in our lab, which allow to determine the oligomer size and inter-peptide contacts that drive the aggregation process. Moreover, we explain and provide the tools to derive Markov state models and transition networks from MD data of peptide aggregation.

It has long been recognised that liquid interfaces, such as the air-water interface (AWI) can enhance formation of protein fibrils. This makes liquid interfaces attractive templates for fibril formation but fully realising this requires knowledge of protein behaviour at interfaces, which is currently lacking. To address this molecular dynamics simulation is used to investigate fragments of amyloid beta, a model fibril forming protein, at the air-water interface. At the air-water interface the enrichment of aggregation-prone helical conformations provides a mechanism for the enhancement of fibrillation at interfaces. The conformational ensemble at the air-water interface was also considerably reduced compared to bulk solution, due to the tendency of hydrophobic side chains partitioning into the air restricting the range of conformations. Little overlap between the conformational ensembles at the AWI and in bulk solution was found, suggesting that AWI induces the formation of different set of structures compared to bulk solution. The smaller Aβ (16)(17)(18)(19)(20)(21)(22) and fragments show an increase in the propensity for ordered secondary structure at the air-water interface but with a increased propensity for turn over other motifs, illustrating the importance of intra-protein interactions for stabilising helical and extended conformations.