1,721,090 research outputs found
Investigation of the interaction between Alzheimer's abeta peptide and aggregation inhibitors using molecular simulations
No full textProtein misfolding has long been known to constitute an important class of disease initiating factors. Of special significance in this group is Alzheimers disease (AD) in which the aggregation of misfolded small molecular weight amyloid ß peptides (Aß) triggers a host of biochemical anomalies that destroy brain neuronal processes. However, in spite of the enormous efforts invested into AD research over the past one century, it has remained without a cure. The available drugs only offer symptomatic relief without improving the associated neurological decline and the typically poor prognosis. The absence of a cure largely results from the peculiarities of the Aß peptide, the molecular principle commonly targeted for drug development. Aß is produced via post-translational cleavage of the transmembrane amyloid precursor protein followed by its release into the extracellular medium. Unlike most other protein drug targets however, Aß both lacks a regular three dimensional fold and possesses a significantly high aggregation propensity under physiological conditions. Aß’s extremely high aggregation tendency renders most available experimental structure determination tools, to a large extent, unable to determine its physiological conformations. Attempts to address this challenge includes the use of nonphysiological solubilising conditions, which at the same time compromises the usefulness of such models for Aß-directed drug discovery. Molecular simulations provide a veritable tool for circumventing this challenge and have been employed in this thesis. In this thesis, a number of molecular simulation techniques have been employed in studying and describing the structural dynamics of the two physiologically dominant Aß species–Aß40 and Aß42. Multiple molecular dynamics (MD) simulations on microsecond time scale were used to study Aß40 and Aß42 monomers in explicit water and under simulation conditions mimicking physiological conditions. To validate the obtained results, we employed chemical shift calculations which we compared with Nuclear Magnetic Resonance (NMR) chemical shifts, enabling us identify the force field that correctly models experimentally relevant Aß structural ensembles. We observed Aß42 monomer to form higher ß-sheet structure than Aß40 and provided an explanation for this and other specific aspects of the folding. We also employed atomistic MD simulation in studying the conformational behaviour of Aß42 monomer under four pH conditions and found the peptide net charge to be the single most important factor directing its folding. Our goal for analysing Aß’s conformation is to obtain structural ensembles closely resembling the physiological state, which can be used in investigating Aβ’s interaction with aggregation inhibitors (D-peptides) currently investigated in the group of Prof. Dr. Willbold (Institute of Complex Systems Forschungszentrum, Jülich) for their anti-amyloid activities against Aß. The inhibitors abolished Aß’s toxicity in a dose-dependent manner, but their mechanism(s) of action, to a large extent, remains unknown. In this work, we present the outcome of the molecular simulations performed to explain the possible mechanism of action of the D-peptides. Our analyses reveal the D-peptides as interacting with both Aß42 monomer and pentamer via strong electrostatic attraction and destroying its ß-sheets. We also performed exhaustive point mutations on the D-peptides’ sequences using both natural and non-natural amino acids. Our results suggest possible modifications that may be performed on the original D-peptides’ amino acid sequences that can help modify their selectivity for different Aß oligomer sizes. Based on these results we propose possible changes to the original D-peptide sequences, and their binding selectivity for different Aß oligomer will be tested in future experiments
3D-QSAR/QSPR-basierter, oberflächenabhängiger Modellierungsansatz, abgeleitet von semi-empirischen quantenmechanischen Rechnungen
Full text linkAbstract:
In this thesis, some new QSAR/QSPR models for predicting physico-chemical and biological activities of organic compounds are described.
New solvation models for calculating the solvation free energy in water, octanol and chloroform have been developed, proceeding by gas-phase geometries derived directly from AM1, AM1*, MNDO/d, and PM3 optimization through VAMP. Basically, these models were obtained by combining a pure Coulomb free energy of solvation derived from a SCRF calculation, with a local term calculated as a surface-integral of a function of local properties. Although AM1* and MNDO/d do not have local polarizability, the calculation of the solvent effect for these Hamiltonians was made possible by extending the SCRF routine, once limited to s and p-orbitals, to d-orbitals. The local properties were calculated with ParaSurf, using either the isodensity or the spherical harmonic surface. The best models, presenting better statistical performances, were performed with the isodensity surface (iso). Among the Hamiltonians, AM1 was found to be the one providing better qualities of prediction, with statistical performances of (R2 = 0.92, MUE = 0.67, RMSD = 0.87), (R2 = 0.92, MUE = 0.57, RMSD = 0.73), and (R2 = 0.91, MUE = 0.46, RMSD = 0.61) for the solvation free energy in water, octanol, and chloroform, respectively. For these solvation models, the contribution of each local property was found to be more than 30% for the molecular electrostatic potential (MEP, V), between 15% and 25% for the local ionization energy (IEL), between 15% and 20% for the local electron affinity (EAL), and between 10% and 18% for the local polarizability (αL or POL) and the hardness (ηL or HARD). This small number of variables used helped in reducing considerably the risk of generating overfitted models. The lack of αL for AM1* and MNDO/d was significant, especially for the solvation free energy in water for neutral and ionic compounds, with a difference in RMSD of 6%, compared to AM1 and PM3. The solvation models in water and octanol developed with neutral compounds were applied for calculating the octanol/water partition coefficient, logPow, for small molecules. For these compounds, the models have provided very good predictive powers, but seem to be very limited when used to calculate the logPow for large molecules. The chloroform/water partition coefficient, logPcw, for a set of small compounds was also calculated in order to validate the models, and very good statistical performances were obtained.
A new approach, based among others on the MEP, IEL, EAL, αL, ηL, the electronegativity (χL or ENEG), the field normal to the surface (FN), and their cross-products, over the surface divided into bins, is presented that is totally different from the former polynomial surface-integral model (SIM), whose principle was to integrate across a molecular surface MEP, IEL, EAL, αL, and ηL. This approach, called binned SIM, was then used with a very large logPow data set obtained from the LOGKOW database to generate models necessary in predicting accurately the logPow, for a data set consisting of large and small compounds that are mainly present in their neutral forms. Proceeding by gas-phase geometries obtained from AM1, AM1*, PM3, MNDO, MNDO/d, and PM6 optimization through VAMP, the models were generated using either the iso or the solvent-excluded surface (SES) for calculating the descriptors. These models were found to be strongly influenced by the flexibility and the rigidity of the compounds used, and compounds having a small number of rotatable bonds were those giving good predictions. Models generated with sets of descriptors calculated with the SES presented better statistical performances. As for the solvation models, AM1 was the one providing better statistical performances for the test set (R2 = 0.89, MUE = 0.43, RMSE = 0.58), and in about 25 of the 50 bagging equations, utilized a lower number of descriptors, 40 among 336 (11.90%). AM1* and MNDO/d without αL had 252 descriptors each and used 39 (15.48%) and 55 (21.83%) of them, respectively. Because of the occurrence of MEP FN in all the 50 bagging equations for AM1* and MNDO/d, FN was found to be the parameter responsible for the compensation of the lack of αL for these Hamiltonians. A close relationship was found between FN and the number of hydrogen bond donor/acceptors, confirming the strong dependence of the logPow prediction on these parameters.
The logPow models previously developed were applied to gas-phase geometries of sets of phospholipidosis-inducing compounds obtained from Pfizer Global R&D of Amboise Laboratories, France, and Sandwich Laboratories, UK. The logPow values obtained for AM1, AM1*, MNDO, MNDO/d, PM3, and PM6 were added to the standard ParaSurf descriptors calculated either with the iso or the SES to generate sets of 125 descriptors. These created sets of descriptors, used through two machine-learning (ML) algorithms (Naive Bayes and Random Forest), generated models to classify compounds according to their ability to induce phospholipidosis. These models, when evaluated on the respective test sets, provided better predictive performances for those generated with the descriptors calculated with the SES. The best model with a predictive power of 84% was obtained with PM3 through the Random Forest classifier (RF). The Naive Bayes (NB) algorithm provided surface-dependent models, but was faced with a problem of similarity in the confusion matrix. This problem was fully corrected by applying the RF classifier on sets of descriptors obtained with the SES. With the iso, the ranges of the Matthews Correlation Coefficient (MCC) were 0.24 to 0.48, with an average of 0.38, and 0.33 to 0.55, with an average of 0.47 for the NB and the RF algorithms, respectively. With the SES, the values of the MCC ranged from 0.33 to 0.57, with an average of 0.47 for NB, and from 0.50 to 0.68, with an average of 0.57 for RF, which yielded the best prediction quality. Twenty-two of the 69 compounds of the test set were found to be highly predictive by both classifiers.Zusammenfassung:
In dieser Arbeit werden einige neue QSAR/QSPR Modelle für die Vorhersage physikalisch-chemischer und biologischer Aktivitäten von organischen Verbindungen beschrieben.
Neue Modelle für die Berechnung der freien Solvatisierungsenergie in Wasser, Octanol und Chloroform wurden entwickelt, basierend auf Gasphase-Geometrien, die mittels AM1, AM1*, MNDO/d, und PM3-Optimierung durch VAMP berechnet wurden. Die neuen Modelle wurden durch eine Kombination der reinen Coulomb-Solvatisierungsenergie erhalten, abgeleitet aus einer SCRF-Berechnung, in Kombination mit einem Oberflächen-Integral als Funktion lokaler quantenmechanischer Eigenschaften auf der Oberfläche. Obwohl AM1* und MNDO/d keine lokale Polarisierbarkeit besitzen, wurde die Berechnung der Lösungsmitteleffekte für diese Hamiltonians durch eine Erweiterung der SCRF-routine auf s-und p-Orbitale, zu d-Orbitale ermöglicht. Die lokalen Eigenschaften wurden mit ParaSurf berechnet, basierend entweder auf der Isodichte-Oberfläche oder der sphärischen harmonischen Oberfläche. Die Modelle mit den statistisch besten Ergebnissen wurden mit der Isodichte-Oberfläche berechnet. Unter den Hamiltonians ergab AM1 die besten Vorhersagen mit (R2 = 0,92, MUE = 0,67, RMSD = 0,87), (R2 = 0,92, MUE = 0,57, RMSD = 0,73), (R2 = 0,91, MUE = 0,46, RMSD = 0,61), für die Solvatisierungsenergie in Wasser, Octanol und Chloroform. Für diese Solvatisierungsmodelle wurde herausgefunden, dass der Beitrag der jeweiligen lokalen Eigenschaft mehr als 30% für das molekulare elektrostatische Potential, (MEP, V), zwischen 15% und 25% für die lokale Ionisierungsenergie, (IEL), zwischen 15% und 20% für die lokale Elektronenaffinität, (EAL) und zwischen 10% und 18% für die lokale Polarisierbarkeit, (αL, POL) und die Härte, (ηL, HARD) beträgt. Diese kleine Anzahl an verwendeten Variablen half dabei das Risiko der Erzeugung von übertrainierten Modellen erheblich zu verringern. Das Fehlen der lokalen Polarisierbarkeit für AM1* und MNDO/d äußerte sich signifikant, vor allem für die freie Solvatisierungsenergie in Wasser für neutrale und ionische Verbindungen mit einem RMSD-Unterschied von 6%, verglichen mit AM1 und PM3. Die Solvatisierungsmodelle in Wasser und Octanol, die mit neutralen Verbindungen entwickelt wurden, wurden zur Vorhersage des Octanol/Wasser-Verteilungskoeffizienten, logPow für kleine Moleküle angewandt. Für diese Verbindungen wiesen die Modelle eine sehr gute Vorhersagekraft auf, scheinen aber nur sehr eingeschränkt in der Lage zu sein, den logPow für große Moleküle zu berechnen. Der Chloroform/Wasser-Verteilungskoeffizient, logPcw für eine Reihe von kleinen Verbindungen wurde ebenfalls berechnet, um die Modelle zu validieren, wobei sehr gute statistische Ergebnisse erzielt wurden.
Es wurde ein neuer mathematischer Ansatz, basierend auf klassifizierten Oberflächenabschnitten des molekularen elektrostatischen Potentials, (MEP), der lokale Ionisierungsenergie, (IEL), der lokale Elektronenaffinität, (EAL), der lokale Polarisierbarkeit, (αL), der Härte, (ηL), der Elektronegativität, (χL, ENEG) und dem Feld senkrecht zur Oberfläche, (FN) und ihrer Kreuz-Produkte entwickelt. Dieser Ansatz unterscheidet sich grundsätzlich vom vorhergehenden polynomischen surface-integral model (SIM), dessen
Prinzip es ist, über eine molekulare Oberfläche MEP, IEL, EAL, αL und ηL zu integrieren. Der neue Oberflächen-Integral-Modell-Ansatz wurde dann verwendet, um logPow Modelle für einen sehr großen Datensatz, die LOGKOW Datenbank, bestehend aus hauptsächlich neutralen, kleinen und großen Molekülen, zu erstellen. Ausgehend von den Gasphasengeometrien, die mittels AM1, AM1*, PM3, MNDO, MNDO/d und PM6 Optimierung durch VAMP erhalten wurden, wurden Modelle unter Verwendung der Isodichte-Oberfläche, beziehungsweise der vom Lösungsmittel ausgeschlossenen Oberfläche zur Berechnung der Deskriptoren, erzeugt. Es wurde herausgefunden, dass diese Modelle stark von der Flexibilität und Steifheit der Verbindungen beeinflusst werden und Verbindungen mit einer kleineren Anzahl an rotierbaren Bindungen besser vorhergesagt wurden. Modelle, die basierend auf der vom Lösungsmittel ausgeschlossenen Fläche berechnet wurden, ergaben hier die kleineren Abweichungen. Bezüglich der Solvatisierungsvorhersagen ergab AM1 die besten Ergebnisse für den Test-Datensatz (R2 = 0,89, MUE = 0,43, RMSE = 0,58) und in etwa 25 der 50 Gleichungen des bagging-Ansatzes nutzte es eine geringere Anzahl von Deskriptoren, nämlich 40 von 336 (11,90%). AM1* und MNDO/d ohne αL basieren auf jeweils 252 Deskriptoren und verwendeten hiervon 39 (15,48%) beziehungsweise 55 (21,83%) für die einzelnen Gleichungen des bagging-Ansatzes. Aufgrund des Auftretens von MEP FN in allen 50 Gleichungen für AM1* und MNDO/d, wurde FN als der Parameter identifiziert, der für die Kompensation des Mangels an αL dieser Hamiltonians verantwortlich ist. Es wurde eine enge Beziehung zwischen FN und der Anzahl der Wasserstoffbrücken-Donatoren/Akzeptoren festgestellt, welche durch die starke Abhängigkeit der logPow Vorhersage von diesen Parametern bestätigt wurde.
Die bisher entwickelten logPow-Modelle wurden zur Vorhersage von Phospholipidose angewandt. Die Daten hierfür stammen von Pfizer Global R&D, Amboise/Frankreich und Sandwich/UK. Die logPow Werte, die mit den Modellen, basierend auf AM1, AM1*, MNDO, MNDO/d, PM3 und PM6, erhalten wurden, wurden mit den Standard ParaSurf-Deskriptoren kombiniert, um Sätze von 125 Deskriptoren zu erzeugen. Diese Deskriptorensätze wurden mit zwei verschiedenen Algorithmen des maschinellen Lernens (Naive Bayes und Random Forest) ausgewertet, um Verbindungen hinsichtlich ihrer Fähigkeit Phospholipidose zu induzieren, zu klassifizieren. Die besten Testdatensatzvorhersagen wurden mit den Modellen erzeugt, in denen die Deskriptoren mit der vom Lösungsmittel ausgeschlossenen Fläche berechnet wurden. Das beste Modell mit einer Genauigkeit von 84% wurde mit PM3 mittels der Random Forest-Klassifizierung erhalten. Der Naive Bayes-Algorithmus lieferte oberflächen-abhängige Modelle, die aber ein Ähnlichkeitsproblem in der Konfusionsmatrix aufwiesen. Dieses Problem wurde vollständig gelöst durch die Anwendung der Random Forest-Klassifizierung auf Gruppen von Deskriptoren, die die vom Lösungsmittel ausgeschlossene Oberfläche enthalten. Die Isodichte-Oberfläche ergab einen Matthews Korrelationskoeffizienten (MCC) zwischen 0,24 bis 0,48 mit einem Durchschnitt von 0,38 und von 0,33 bis 0,55 mit einem Durchschnitt von 0,47 für die Naive Bayes beziehungsweise Random Forest-Modelle. Bezüglich der vom Lösungsmittel ausgeschlossenen Oberfläche reichten die Werte des MCC von 0,33 bis 0,57, mit einem Durchschnitt von 0,47 für Naive Bayes, und von 0,50 bis 0,68, mit einem Durchschnitt von 0,57 für Random Forest, welcher die beste Vorhersagequalität erzielte. Zwei und zwanzig der 69 Verbindungen der Versuchsanordnung wurden sowohl mit der Naive Bayes-, als auch mit der Random Forest-Klassifizierungsmethode sehr gut vorhergesagt
How to learn from inconsistencies:Integrating molecular simulations with experimental data
No full textMolecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modeling tool to interpret experimental measurements, and to use experimental data to refine our biophysical models. Thus, explicit integration and synergy between molecular simulations and experiments is fundamental for furthering our understanding of biological processes. This is especially true in the case where discrepancies between measured and simulated observables emerge. In this chapter, we provide an overview of some of the core ideas behind methods that were developed to improve the consistency between experimental information and numerical predictions. We distinguish between situations where experiments are used to refine our understanding and models of specific systems, and situations where experiments are used more generally to refine transferable models. We discuss different philosophies and attempt to unify them in a single framework. Until now, such integration between experiments and simulations have mostly been applied to equilibrium data, and we discuss more recent developments aimed to analyze time-dependent or time-resolved data.</p
Essays on the interplay between glycosaminoglycans and amyloid-β peptides
No full textIntrinsically disordered proteins (IDPs), which represent ~40% of the human proteome, play crucial roles in a variety of biological pathways and biomolecular assemblies. Monomeric IDPs such as amyloid-β (Aβ), can aggregate into insoluble, relatively inert, rigid structures called fibrils, but also much more toxic, soluble struc- tures of intermediate size and varying shapes, which are called oligomers. The toxic aggregates of Aβ peptide are implicated in the pathogenesis of Alzheimer’s disease (AD). In this thesis, we use in-silico approaches to model Aβ under different physiological and pathological conditions to unravel their effects on the structures and kinetics of the amyloid oligomers. We first highlight the ramifications of molecular mechanics parameters on the structural heterogeneity of Aβ and the aggregation process of various Aβ fragments. Next, we demonstrate how Aβ fragments aggregate in the presence of glycosaminoglycans. To this end, the conformational dynamics of different glycosaminoglycans was first elucidated to understand their behavior in the absence Aβ fragments. The conclusions from these investigations enabled us to identify force fields which predict Aβ structures and dynamics in agreement with experimental observations. From transition networks applied to the aggregation data we deduced the structural transitions during the early and intermediate stages of oligomer formation. Furthermore, we elucidated the intermolecular interactions between Aβ and glycosaminoglycans that transpire towards enhancing, stabilizing, or inhibition of Aβ aggregation behavior
Atomistic Simulations of Amyloidogenic Peptides and Their Aggregation
No full textProtein aggregation into highly structured amyloid fibrils is associated both with devastating diseases, including Alzheimer’s disease and type 2 diabetes, and functional roles, such as the storage of neuropeptides. Experimental evidence shows that the toxic species in amyloid diseases are small oligomers. These oligomers are transient and, hence, are hard to characterize experimentally. In this thesis, I study the aggregation of amyloidogenic peptides into oligomers using classical molecular dynamics simulations. Most of the peptides simulated are variants of the amyloid-β peptide (Aβ), which is involved in the development of Alzheimer’s disease. First, I investigate the aggregation of Aβ 25−35 and two functional amyloidogenic tachykinin peptides: kassinin and neuromedin K. The three peptides have similar primary sequences, yet, while Aβ 25−35 is toxic, tachykinin peptides are not. In my simulations, tachykinin peptides aggregate faster than Aβ 25−35 , which suggests that functional amyloids may avoid toxicity by rapidly aggregating into the non-toxic fibril phase. Furthermore, I observe that peptides that exist in extended conformations as monomers aggregate faster than those in hairpin-like conformations. Next, I compare the ability of different force fields in modeling intrinsically disordered proteins (IDPs) and protein aggregation. In recent years, new force fields have been developed to balance different secondary structures in protein folding simulations. These new force fields should perform better than older ones for IDPs or protein aggregation. In my simulations of Aβ 42 , which is an IDP, the new force fields, particularly CHARMM22*, reproduce experimental nuclear magnetic resonance data better than the older force fields under study. In the simulations of protein aggregation, none of the force fields is able to distinguish between slowly, fast and non-aggregating peptides. However, the force fields predict similar inter-peptide contacts for aggregating peptides, indicating that protein aggregation is driven by the same interactions with all force fields. Finally, I study the monomer dynamics of multiple mutants of Aβ 16−22 with different aggregation propensity. No correlation is observed between ensemble averaged properties and aggregation propensity. However, the implied time scale of the slowest process of the monomer dynamics correlates with aggregation propensity, which shows that amyloidogenic peptide aggregation is encoded in the dynamical properties of the monomer. This thesis presents an advance in the simulations of protein aggregation, providing new insight into the formation of amyloid oligomers. Only if the physico-chemical principles of this process are understood, one can rationally design therapeutic agents against amyloid diseases and create novel amyloid-based nanomaterials
Biophysical and functional characterization of amyloids forming parathyroid hormone
Full text linkParathyroid hormone (PTH) is a hormone that regulates blood calcium levels via the activation of a GPCR. PTH consists of 84 amino acids, with the C-terminus being intrinsically disordered. The N-terminal Pro-sequence of PTH, which is particularly rich in basic residues, prevents premature fibrillation. In-vitro studies confirmed the ability of PTH to form amyloid fibrils, identifying a stable amyloid core from residues 25R-37L. Shortening of the C-terminal region alters the morphology of the fibrils but increases stability and limits reversibility, indicating their importance in PTH storage. In summary, this work contributes insights into the mechanisms underlying the behaviour of PTH as a functional amyloid. It illustrates how specific regions, such as the N-terminal Pro-sequence and the C-terminal disordered region, regulate the pathway of PTH through synthesis, secretion and storage.Parathormon (PTH) ist ein Hormon, das über die Aktivierung eines GPCR den Blutkalziumspiegel reguliert. PTH besteht aus 84 Aminosäuren, wobei der C-Terminus intrinsisch ungeordnet ist. Die N-terminale Pro-Sequenz von PTH, die besonders reich an basischen Resten ist, verhindert eine vorzeitige Fibrillierung. In-vitro Studien bestätigten die Fähigkeit von PTH, Amyloidfibrillen zu bilden, wobei ein stabiler Amyloidkern aus den Resten 25R-37L identifiziert wurde. Die Verkürzung der C-terminalen Region verändert die Morphologie der Fibrillen, erhöht jedoch die Stabilität und begrenzt die Reversibilität, was auf ihre Bedeutung für die PTH-Speicherung hinweist. Zusammenfassend trägt diese Arbeit Erkenntnisse zu den Mechanismen bei, die dem Verhalten von PTH als funktionales Amyloid zugrunde liegen. Sie verdeutlicht, wie spezifische Regionen, wie die N-terminale Pro-Sequenz und die C-terminale ungeordnete Region, den Weg von PTH durch Synthese, Sekretion und Speicherung
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Energy Landscapes of Protein Aggregation and Conformation Switching in Intrinsically Disordered Proteins
No full textThe 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
- …
