Overlay databank unlocks data-driven analyses of biomolecules for all.

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Transition Networks Unveil Disorder-to-Order Transformations in Aß Caused by Glycosaminoglycans or Lipids.

The aggregation of amyloid-ß (Aß) peptides, particularly of Aß1-42, has been linked to the pathogenesis of Alzheimer's disease. In this study, we focus on the conformational change of Aß1-42 in the presence of glycosaminoglycans (GAGs) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipids using molecular dynamics simulations. We analyze the conformational changes that occur in Aß by extracting the key structural features that are then used to generate transition networks. Using the same three features per network highlights the transitions from intrinsically disordered states ubiquitous in Aß1-42 in solution to more compact states arising from stable ß-hairpin formation when Aß1-42 is in the vicinity of a GAG molecule, and even more compact states characterized by a α-helix or ß-sheet structures when Aß1-42 interacts with a POPC lipid cluster. We show that the molecular mechanisms underlying these transitions from disorder to order are different for the Aß1-42/GAG and Aß1-42/POPC systems. While in the latter the hydrophobicity provided by the lipid tails facilitates the folding of Aß1-42, in the case of GAG there are hardly any intermolecular Aß1-42-GAG interactions. Instead, GAG removes sodium ions from the peptide, allowing stronger electrostatic interactions within the peptide that stabilize a ß-hairpin. Our results contribute to the growing knowledge of the role of GAGs and lipids in the conformational preferences of the Aß peptide, which in turn influences its aggregation into toxic oligomers and amyloid fibrils.

Molecular Dynamics Simulations of Protein Aggregation: Protocols for Simulation Setup and Analysis with Markov State Models and Transition Networks.

Protein disorder and aggregation play significant roles in the pathogenesis of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. 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.

The Influences of Sulphation, Salt Type, and Salt Concentration on the Structural Heterogeneity of Glycosaminoglycans.

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.

Molecular simulations of IDPs: From ensemble generation to IDP interactions leading to disorder-to-order transitions.

Intrinsically disordered proteins (IDPs) lack a well-defined three-dimensional structure but do exhibit some dynamical and structural ordering. The structural plasticity of IDPs indicates that entropy-driven motions are crucial for their function. Many IDPs undergo function-related disorder-to-order transitions upon by their interaction with specific binding partners. Approaches that are based on both experimental and theoretical tools enable the biophysical characterization of IDPs. Molecular simulations provide insights into IDP structural ensembles and disorder-to-order transition mechanisms. However, such studies depend strongly on the chosen force field parameters and simulation techniques. In this chapter, we provide an overview of IDP characteristics, review all-atom force fields recently developed for IDPs, and present molecular dynamics-based simulation methods that allow IDP ensemble generation as well as the characterization of disorder-to-order transitions. In particular, we introduce metadynamics, replica exchange molecular dynamics simulations, and also kinetic models resulting from Markov State modeling, and provide various examples for the successful application of these simulation methods to IDPs.

The Effects of Different Glycosaminoglycans on the Structure and Aggregation of the Amyloid-ß (16-22) Peptide.

Aggregates of the amyloid-ß (Aß) peptide are implicated as a causative substance in Alzheimer's disease. Molecular dynamics simulations provide valuable contributions for elucidating the conformational transitions of monomeric and aggregated forms of Aß be it in solution or in the presence of other molecules. Here, we study the effects of four different glycosaminoglycans (GAGs), three sulfated ones and a nonsulfated one, on the aggregation of Aß16-22. From experiments, it has been suggested that GAGs, which belong to the main components of the brain's extracellular space, favor amyloid fibril formation. Our simulation results reveal that the binding of Aß16-22 to the GAGs is driven by electrostatic attraction between the negative GAG charges and the positively charged K16 of Aß16-22. While these interactions have only minor effects on the GAG and Aß16-22 conformations at the 1 Aß16-22/1 GAG ratio, at the 2:2 stoichiometry the aggregation of Aß16-22 is considerably changed. In solution, the aggregation of Aß16-22 is strongly influenced by K16-E22 attraction, leading to antiparallel ß-sheets. In the presence of GAGs, on the other hand, the interaction of K16 with the GAGs increases the importance of the hydrophobic interactions during Aß16-22 aggregation, which in turn yields parallel alignments. A templating and ordering effect of the GAGs on the Aß16-22 aggregates is observed. In summary, this study provides new insight at the atomic level on GAG-amyloid interactions, strengthening the view that sulfation of the GAGs plays a major role in this context.

Thermodynamics and kinetics of the amyloid-ß peptide revealed by Markov state models based on MD data in agreement with experiment.

The amlyoid-ß peptide (Aß) is closely linked to the development of Alzheimer's disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the atomistic level. General key aspects of MD simulations are the force field used for modeling the peptide and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-µs MD simulations acquired for Aß40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for the different MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-ß peptide. This further allows us to provide answers for pertinent questions, like: which force fields are suitable for modeling Aß? (a99SB-UCB and a99SB-ILDN/TIP4P-D); what does Aß peptide really look like? (mostly extended and disordered) and; how long does it take MD simulations of Aß to attain equilibrium? (at least 20-30 µs). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of Aß in particular, and by extension other similar disordered proteins.

Effect of the air-water interface on the conformation of amyloid beta.

It has long been recognized that liquid interfaces, such as the air-water interface (AWI), can enhance the formation of protein fibrils. This makes liquid interfaces attractive templates for fibril formation but fully realizing this requires knowledge of protein behavior 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 the bulk solution was found, suggesting that AWI induces the formation of a different set of structures compared to bulk solution. The smaller Aß(16-22) and Aß(25-35) fragments show an increase in the propensity for an 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 stabilizing helical and extended conformations.

Different Force Fields Give Rise to Different Amyloid Aggregation Pathways in Molecular Dynamics Simulations.

The progress toward understanding the molecular basis of Alzheimers's disease is strongly connected to elucidating the early aggregation events of the amyloid-ß (Aß) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of Aß 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 common force fields can distinguish between aggregation-prone and nonaggregating peptide sequences, producing a similar and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as Aß were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the Aß16-22 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.

ß-Turn mimetic synthetic peptides as amyloid-ß aggregation inhibitors.

Aggregation of amyloid peptides results in severe neurodegenerative diseases. While the fibril structures of Aß40 and Aß42 have been described recently, resolution of the aggregation pathway and evaluation of potent inhibitors still remains elusive, in particular in view of the hairpin-region of Aß40. We here report the preparation of beta-turn mimetic conjugates containing synthetic turn mimetic structures in the turn region of Aß40 and Aß16-35, replacing 2 amino acids in the turn-region G25 - K28. The structure of the turn mimic induces both, acceleration of fibrillation and the complete inhibition of fibrillation, confirming the importance of the turn region on the aggregation. Replacing position G25-S26 provided the best inhibition effect for both beta-turn mimetics, the bicyclic BTD 1 and the aromatic TAA 2, while positions N27-K28 and V24-G25 showed only weaker or no inhibitory effects. When comparing different turn mimetics at the same position (G25-S26), conjugate 1a bearing the BTD turn showed the best inhibition of Aß40 aggregation, while 5-amino-valeric acid 4a showed the weakest effect. Thus there is a pronounced impact on fibrillation with the chemical nature of the embedded beta-turn-mimic: the conformationally constrained turns 1 and 2 lead to a significantly reduced fibrillation, even inhibiting fibrillation of native Aß40 when added in amounts down to 1/10, whereas the more flexible beta-turn-mimics 4-amino-benzoic acid 3a and 5-amino-valeric acid 4a lead to enhanced fibrillation. Toxicity-testing of the most successful conjugate showed only minor toxicity in cell-viability assays using the N2a cell line. Structural downsizing lead to the short fragment BTD/peptide Aß16-35 as inhibitor of the aggregation of Aß40, opening large potential for further small peptide based inhibitors.