The progress toward 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 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 Aβ were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the Aβ16–22 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.
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.
Allosteric regulation refers to the process where the effect of binding of a ligand at one site of a protein is transmitted to another, often distant, functional site. In recent years, it has been ...demonstrated that allosteric mechanisms can be understood by the conformational ensembles of a protein. Molecular dynamics (MD) simulations are often used for the study of protein allostery as they provide an atomistic view of the dynamics of a protein. However, given the wealth of detailed information hidden in MD data, one has to apply a method that allows extraction of the conformational ensembles underlying allosteric regulation from these data. Markov state models are one of the most promising methods for this purpose. We provide a short introduction to the theory of Markov state models and review their application to various examples of protein allostery studied by MD simulations. We also include a discussion of studies where Markov modelling has been employed to analyse experimental data on allosteric regulation. We conclude our review by advertising the wider application of Markov state models to elucidate allosteric mechanisms, especially since in recent years it has become straightforward to construct such models thanks to software programs like PyEMMA and MSMBuilder.
This article is part of a discussion meeting issue ‘Allostery and molecular machines’.
Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization ...of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer’s disease (AD), Parkinson’s disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.
In amyloid aggregation diseases soluble proteins coalesce into a wide array of undesirable structures, ranging through oligomers and prefibrillar assemblies to highly ordered amyloid fibrils and ...plaques. Explicit-solvent all-atom molecular dynamics (MD) simulations of amyloid aggregation have been performed for almost 20 years, revealing valuable information about this phenomenon. However, these simulations are challenged by three main problems. Firstly, current force fields modeling amyloid aggregation are insufficiently accurate. Secondly, the protein concentrations in MD simulations are usually orders of magnitude higher than those used in vitro or found in vivo, which has direct consequences on the aggregates that form. Finally, the third problem is the well-known time-scale limit of MD simulations. In this review I highlight recent approaches to overcome these three limitations.
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•The energy landscapes of protein folding are contrasted with those of IDPs and protein aggregation.•Energy landscapes of protein folding have one funnel, the so-called folding ...funnel.•Machine-learning methods like AlphaFold work great for single-funneled energy landscapes.•Energy landscapes of IDPs and protein aggregation are rugged with multiple funnels.•Current machine-learning methods fail for rugged energy landscapes; MD simulations help here.
The protein folding problem was apparently solved recently by the advent of a deep learning method for protein structure prediction called AlphaFold. However, this program is not able to make predictions about the protein folding pathways. Moreover, it only treats about half of the human proteome, as the remaining proteins are intrinsically disordered or contain disordered regions. By definition these proteins differ from natively folded proteins and do not adopt a properly folded structure in solution. However these intrinsically disordered proteins (IDPs) also systematically differ in amino acid composition and uniquely often become folded upon binding to an interaction partner. These factors preclude solving IDP structures by current machine-learning methods like AlphaFold, which also cannot solve the protein aggregation problem, since this meta-folding process can give rise to different aggregate sizes and structures. An alternative computational method is provided by molecular dynamics simulations that already successfully explored the energy landscapes of IDP conformational switching and protein aggregation in multiple cases. These energy landscapes are very different from those of ‘simple’ protein folding, where one energy funnel leads to a unique protein structure. Instead, the energy landscapes of IDP conformational switching and protein aggregation feature a number of minima for different competing low-energy structures. In this review, I discuss the characteristics of these multifunneled energy landscapes in detail, illustrated by molecular dynamics simulations that elucidated the underlying conformational transitions and aggregation processes.
We use state-of-the-art computer-aided drug design (CADD) techniques to identify prospective inhibitors of the main protease enzyme, 3CL
of the severe acute respiratory syndrome coronavirus 2 ...(SARS-CoV-2) causing COVID-19. From our screening of over one million compounds including approved drugs, investigational drugs, natural products, and organic compounds, and a rescreening protocol incorporating enzyme dynamics via ensemble docking, we have been able to identify a range of prospective 3CL
inhibitors. Importantly, some of the identified compounds had previously been reported to exhibit inhibitory activities against the 3CL
enzyme of the closely related SARS-CoV virus. The top-ranking compounds are characterized by the presence of multiple bi- and monocyclic rings, many of them being heterocycles and aromatic, which are flexibly linked allowing the ligands to adapt to the geometry of the 3CL
substrate site and involve a high amount of functional groups enabling hydrogen bond formation with surrounding amino acid residues, including the catalytic dyad residues H41 and C145. Among the top binding compounds we identified several tyrosine kinase inhibitors, which include a bioflavonoid, the group of natural products that binds best to 3CL
. Another class of compounds that decently binds to the SARS-CoV-2 main protease are steroid hormones, which thus may be endogenous inhibitors and might provide an explanation for the age-dependent severity of COVID-19. Many of the compounds identified by our work show a considerably stronger binding than found for reference compounds with in vitro demonstrated 3CL
inhibition and anticoronavirus activity. The compounds determined in this work thus represent a good starting point for the design of inhibitors of SARS-CoV-2 replication.
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.
The aggregation of amyloid-β (Aβ) peptides, particularly of Aβ1-42, has been linked to the pathogenesis of Alzheimer's disease. In this study, we focus on the conformational change of Aβ1-42 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 Aβ 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 Aβ1-42 in solution to more compact states arising from stable β-hairpin formation when Aβ1-42 is in the vicinity of a GAG molecule, and even more compact states characterized by a α-helix or β-sheet structures when Aβ1-42 interacts with a POPC lipid cluster. We show that the molecular mechanisms underlying these transitions from disorder to order are different for the Aβ1-42/GAG and Aβ1-42/POPC systems. While in the latter the hydrophobicity provided by the lipid tails facilitates the folding of Aβ1-42, in the case of GAG there are hardly any intermolecular Aβ1-42-GAG interactions. Instead, GAG removes sodium ions from the peptide, allowing stronger electrostatic interactions within the peptide that stabilize a β-hairpin. Our results contribute to the growing knowledge of the role of GAGs and lipids in the conformational preferences of the Aβ peptide, which in turn influences its aggregation into toxic oligomers and amyloid fibrils.
Free energy surface calculated for alanine dipeptide with steepest–descent pathways and structures marked.
Approximate free energy surfaces and transition rates are presented for alanine dipeptide ...for a variety of force fields and implicit solvent models. Our calculations are based upon local minima, transition states and pathways characterised for each potential energy surface using geometry optimisation. The superposition approach employing only local minima and harmonic densities of states provides a representation of low-lying regions of the free energy surfaces. However, including contributions from the transition states of the potential energy surface and selected points obtained from displacements along the corresponding reaction vectors produces surfaces that compare quite well with results from replica exchange molecular dynamics. Characterising the local minima, transition states, normal modes, pathways, rate constants and free energy surfaces for each force field within this framework typically requires between one and five minutes cpu time on a single processor.
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