{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Txt":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=curation_notes.txt"],"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567-biopax3.owl","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567-biopax2.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=manifest.xml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=curation_image.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=metadata.rdf","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=Morris2008.sedml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567-octave.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567.ode","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567-matlab.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=Morris2008.cps","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567.vcml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000567?filename=BIOMD0000000567.png"]},"type":"primary"},"statusCodeValue":200,"statusCode":"OK"}],"scores":null,"additional":{"submitter":["Audald Lloret i Villas"],"curationStatus":["Manually curated"],"modellingApproach":["ordinary differential equation model"],"disease":["Huntington's Disease","Alzheimer's Disease","Parkinson's Disease"],"levelVersion":["L2V4"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/BIOMD0000000567"],"publication_pubmed":["18247636"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Morris2008   Fitting protein aggregation data via F W 2 step mechanism"],"publication_year":["2008"],"submissionId":["MODEL1501160000"],"publication_authors":["Aimee M Morris, Murielle A Watzky, Jeffrey N Agar, Richard G Finke"],"first_author":["Aimee M Morris"],"publication":["18247636,\n                            The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/\"Ockham's razor\" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A --> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B --> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Saitô and co-workers. In particular, the kinetic equation for Saitô's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Saitô and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments.. 8, 47.\n                            Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, USA."],"submitter_mail":["lloret@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"publicationId":["BIOMD0000000567"],"pubmed_abstract":["With some exceptions, amyloids appear to be accidental aggregated structures whose formation was not selected for in molecular evolution. Despite this, amyloid fibrils are in many respects surprisingly well-behaved molecules. For example, Huntington's disease-related polyglutamine sequences aggregate via a relatively simple nucleated growth polymerization mechanism. In addition, the Alzheimer's plaque protein Abeta has been shown to undergo reversible amyloid fibril formation to a position of dynamic equilibrium such that reaction thermodynamics can be quantified. Studies of these well-behaved amyloid systems are allowing us to peer more deeply into the process and products of off-pathway misfolding and aggregation.","Metal ions (Zn(II)) are demonstrated as probes of amyloid structure in simple segments of the Abeta peptide, Abeta(13-21). By restricting the possible metal binding sites to His13/His14 dyad, we show that Zn2+ can specifically control the rate of self-assembly and dramatically regulate amyloid morphology via distinct coordination environments as characterized by X-ray absorption spectroscopy. The data establish that the single His13 is sufficient to coordinate Zn2+ productively for typical amyloid fiber formation, while a distinct Zn2+ coordination environment can be accessed in the presence of His13/Hi14 dyad to stabilize sheet/sheet associations and the transition to a ribbon/tube morphology.","In Huntington's Disease and related expanded CAG repeat diseases, a polyglutamine [poly(Gln)] sequence containing 36 repeats in the corresponding disease protein is benign, whereas a sequence with only 2-3 additional glutamines is associated with disease risk. Above this threshold range, longer repeat lengths are associated with earlier ages-of-onset. To investigate the biophysical basis of these effects, we studied the in vitro aggregation kinetics of a series of poly(Gln) peptides. We find that poly(Gln) peptides in solution at 37 degrees C undergo a random coil to beta-sheet transition with kinetics superimposable on their aggregation kinetics, suggesting the absence of soluble, beta-sheet-rich intermediates in the aggregation process. Details of the time course of aggregate growth confirm that poly(Gln) aggregation occurs by nucleated growth polymerization. Surprisingly, however, and in contrast to conventional models of nucleated growth polymerization of proteins, we find that the aggregation nucleus is a monomer. That is, nucleation of poly(Gln) aggregation corresponds to an unfavorable protein folding reaction. Using parameters derived from the kinetic analysis, we estimate the difference in the free energy of nucleus formation between benign and pathological length poly(Gln)s to be less than 1 kcal/mol. We also use the kinetic parameters to calculate predicted aggregation curves for very low concentrations of poly(Gln) that might obtain in the cell. The repeat-length-dependent differences in predicted aggregation lag times are in the same range as the length-dependent age-of-onset differences in Huntington's disease, suggesting that the biophysics of poly(Gln) aggregation nucleation may play a major role in determining disease onset.","Fundamental questions about the relative arrangement of the beta-sheet arrays within amyloid fibrils remain central to both its structure and the mechanism of self-assembly. Recent computational analyses suggested that sheet-to-sheet lamination was limited by the length of the strand. On the basis of this hypothesis, a short seven-residue segment of the Alzheimer's disease-related Abeta peptide, Abeta(16-22), was allowed to self-assemble under conditions that maintained the basic amphiphilic character of Abeta. Indeed, the number increased over 20-fold to 130 laminates, giving homogeneous bilayer structures that supercoil into long robust nanotubes. Small-angle neutron scattering and X-ray scattering defined the outer and inner radii of the nanotubes in solution to contain a 44-nm inner cavity with 4-nm-thick walls. Atomic force microscopy and transmission electron microscopy images further confirmed these homogeneous arrays of solvent-filled nanotubes arising from a flat rectangular bilayer, 130 nm wide x 4 nm thick, with each bilayer leaflet composed of laminated beta-sheets. The corresponding backbone H-bonds are along the long axis, and beta-sheet lamination defines the 130-nm bilayer width. This bilayer coils to give the final nanotube. Such robust and persistent self-assembling nanotubes with positively charged surfaces of very different inner and outer curvature now offer a unique, robust, and easily accessible scaffold for nanotechnology.","Studies of lysozyme have played a major role over several decades in defining the general principles underlying protein structure, folding, and stability. Following the discovery some 10 years ago that two mutational variants of lysozyme are associated with systemic amyloidosis, these studies have been extended to investigate the mechanism of amyloid fibril formation. This Account describes our present knowledge of lysozyme folding and misfolding, and how the latter can give rise to amyloid disease. It also discusses the significance of these studies for our general understanding of normal and aberrant protein folding in the context of human health and disease.","The process of amyloid formation by the amyloid beta peptide (Abeta), i.e., the misassembly of Abetapeptides into soluble quaternary structures and, ultimately, amyloid fibrils, appears to be at the center of Alzheimer's disease (AD) pathology. We have shown that abnormal oxidative metabolites, including cholesterol-derived aldehydes, modify Abeta and accelerate the early stages of amyloidogenesis (the formation of spherical aggregates). This process, which we have termed metabolite-initiated protein misfolding, could explain why hypercholesterolemia and inflammation are risk factors for sporadic AD. Herein, the mechanism by which cholesterol metabolites hasten Abeta 1-40 amyloidogenesis is explored, revealing a process that has at least two steps. In the first step, metabolites modify Abeta peptides by Schiff base formation. The Abeta-metabolite adducts form spherical aggregates by a downhill polymerization that does not require a nucleation step, dramatically accelerating Abeta aggregation. In agitated samples, a second step occurs in which fibrillar aggregates form, a step also accelerated by cholesterol metabolites. However, the metabolites do not affect the rate of fibril growth in seeded aggregation assays; their role appears to be in initiating amyloidogenesis by lowering the critical concentration for aggregation into the nanomolar range. Small molecules that block Schiff base formation inhibit the metabolite effect, demonstrating the importance of the covalent adduct. Metabolite-initiated amyloidogenesis offers an explanation for how Abeta aggregation could occur at physiological nanomolar concentrations.","Amyloid fibrils form through nucleation and growth. To clarify the mechanism involved, direct observations of both processes are important. First, seed-dependent fibril growth of beta2-microglobulin (beta2-m) and amyloid beta peptide was visualized in real time at the single fibril level using total internal reflection fluorescence microscopy combined with the binding of thioflavin T, an amyloid-specific fluorescence dye. Second, using atomic force microscopy, ultrasonication-induced formation of beta2-m fibrils was shown, indicating that ultrasonication is useful to accelerate the nucleation process. Third, with the proteolytic fragment of beta2-m, propagation and a transformation of fibril morphology was demonstrated. These direct observations indicate that template-dependent growth and structural diversity are key factors determining the structure and function of amyloid fibrils.","We present the first electrochemical detection, characterization, and kinetic study of the aggregation of Alzheimer's disease (AD) amyloid beta peptides (Abeta-40, Abeta-42) using three different voltammetric techniques at a glassy carbon electrode (GCE). This method is based on detecting changes in the oxidation signal of tyrosine (Tyr) residue. As the peptides aggregate, there are structure conformational changes, which affect the degree of exposure of Tyr to the molecular surface of the peptides. The results show significant differences in the aggregation process between the two peptides, and these correlate highly with established techniques. The method is rapid and label-free, and the principle can be universally applied to other protein aggregation studies related to diseases, such as Huntington's, Parkinson's, and Creutzfeldt Jacob (CJD). This method could also be explored in screening for the effectiveness of AD therapies.","The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/\"Ockham's razor\" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A --> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B --> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Saitô and co-workers. In particular, the kinetic equation for Saitô's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Saitô and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments.","Alpha-synuclein is a major component of several pathological lesions diagnostic of specific neurodegenerative disease such as Parkinson's disease. This study focuses on the non-amyloid beta component of Alzheimer's disease amyloid, a key region for the aggregation and fibril formation of alpha-synuclein. Several mutations were introduced in an attempt to repress beta-strand formation and hydrophobic interaction-based aggregation. Although reducing the hydrophobicity drastically decreased fibril formation, the Val70Thr and Val70Pro mutations resulted in an unstable secondary structure thereby increasing non-structural aggregation, instead of fibril formation. Therefore, the stabilization of non-structural natively unfolded status is important to prevent alpha-synuclein fibril formation. Mixing the Val70Thr/Val71Thr double mutant, which has inherently low potential, with the fibril forming alpha-synucleins, WT and Ala53Thr, greatly reduced their fibril formation and aggregation. This double mutant has great potential for further therapeutic approaches.","alpha-Synuclein is a small (14 kDa), abundant, intrinsically disordered presynaptic protein, whose aggregation is believed to be a critical step in Parkinson's disease (PD). The kinetics of alpha-synuclein fibrillation are consistent with a nucleation-dependent mechanism, in which the critical early stage of the structural transformation involves a partially folded intermediate. Although the basis for the toxic effects of aggregated alpha-synuclein are unknown, it has been proposed that transient oligomers are responsible, possibly by forming pores in membranes. In this Account, I discuss our investigations into the molecular basis for alpha-synuclein aggregation/fibrillation, including factors that either accelerate or inhibit fibrillation, effects of molecular crowding, oxidation, point mutations, and lipid membranes, as well as the variety of conformational and oligomeric states that alpha-synuclein can adopt. It is apparent that neuronal cells must have a very fine balance of factors that control the levels and potential aggregation of alpha-synuclein.","Amyloid deposits within the cerebral tissue constitute a characteristic lesion associated with Alzheimer disease. They mainly consist of the amyloid peptide Abeta and display an abnormal content in Zn(2+) ions, together with many truncated, isomerized, and racemized forms of Abeta. The region 1-16 of Abeta can be considered the minimal zinc-binding domain and contains two aspartates subject to protein aging. The influence of zinc binding and protein aging related modifications on the conformation of this region of Abeta is of importance given the potentiality of this domain to constitute a therapeutic target, especially for immunization approaches. In this study, we determined from NMR data the solution structure of the Abeta-(1-16)-Zn(2+) complex in aqueous solution at pH 6.5. The residues His(6), His(13), and His(14) and the Glu(11) carboxylate were identified as ligands that tetrahedrally coordinate the Zn(II) cation. In vitro aging experiments on Abeta-(1-16) led to the formation of truncated and isomerized species. The major isomer generated, Abeta-(1-16)-l-iso-Asp(7), displayed a local conformational change in the His(6)-Ser(8) region but kept a zinc binding propensity via a coordination mode involving l-iso-Asp(7). These results are discussed here with regard to Abeta fibrillogenesis and the potentiality of the region 1-16 of Abeta to be used as a therapeutic target."],"pubmed_title":["Modulating amyloid self-assembly and fibril morphology with Zn(II).","Kinetics and thermodynamics of amyloid fibril assembly.","Structural changes of region 1-16 of the Alzheimer disease amyloid beta-peptide upon zinc binding and in vitro aging.","Normal and aberrant biological self-assembly: Insights from studies of human lysozyme and its amyloidogenic variants.","The aggregation and fibrillation of alpha-synuclein.","Oxidative metabolites accelerate Alzheimer's amyloidogenesis by a two-step mechanism, eliminating the requirement for nucleation.","Huntington's disease age-of-onset linked to polyglutamine aggregation nucleation.","A rapid label-free electrochemical detection and kinetic study of Alzheimer's amyloid beta aggregation.","Engineered alpha-synuclein prevents wild type and familial Parkin variant fibril formation.","Direct observation of amyloid fibril growth, propagation, and adaptation.","Fitting neurological protein aggregation kinetic data via a 2-step, minimal/\"Ockham's razor\" model: the Finke-Watzky mechanism of nucleation followed by autocatalytic surface growth.","Exploiting amyloid fibril lamination for nanotube self-assembly."],"pubmed_authors":["Fink Anthony L AL","Lu Kun K, Jacob Jaby J, Thiyagarajan Pappannan P, Conticello Vincent P VP, Lynn David G DG","Bieschke Jan J, Zhang Qinghai Q, Powers Evan T ET, Lerner Richard A RA, Kelly Jeffery W JW","Morris Aimee M AM, Watzky Murielle A MA, Agar Jeffrey N JN, Finke Richard G RG","Sode Koji K, Usuzaka Eri E, Kobayashi Natsuki N, Ochiai Sayaka S","Vestergaard Mun'delanji M, Kerman Kagan K, Saito Masato M, Nagatani Naoki N, Takamura Yuzuru Y, Tamiya Eiichi E","Zirah Séverine S, Kozin Sergey A SA, Mazur Alexey K AK, Blond Alain A, Cheminant Michel M, Ségalas-Milazzo Isabelle I, Debey Pascale P, Rebuffat Sylvie S","Chen Songming S, Ferrone Frank A FA, Wetzel Ronald R","Ban Tadato T, Yamaguchi Keiichi K, Goto Yuji Y","Dumoulin Mireille M, Kumita Janet R JR, Dobson Christopher M CM","Dong Jijun J, Shokes Jacob E JE, Scott Robert A RA, Lynn David G DG","Wetzel Ronald R"],"additional_accession":[]},"is_claimable":false,"name":"Morris2008 - Fitting protein aggregation data via F-W 2-step mechanism","description":"\n      \n        Morris2008 - Fitting protein aggregation data\nvia F-W 2-step mechanism\n\n  This model is described in the article:\n  \n    Fitting neurological protein\n    aggregation kinetic data via a 2-step, minimal/\"Ockham's razor\"\n    model: the Finke-Watzky mechanism of nucleation followed by\n    autocatalytic surface growth.\n  \n  Morris AM, Watzky MA, Agar JN, Finke\n  RG.\n  Biochemistry 2008 Feb; 47(8):\n  2413-2427\n  Abstract:\n  \n    The aggregation of proteins has been hypothesized to be an\n    underlying cause of many neurological disorders including\n    Alzheimer's, Parkinson's, and Huntington's diseases; protein\n    aggregation is also important to normal life function in cases\n    such as G to F-actin, glutamate dehydrogenase, and tubulin and\n    flagella formation. For this reason, the underlying mechanism\n    of protein aggregation, and accompanying kinetic models for\n    protein nucleation and growth (growth also being called\n    elongation, polymerization, or fibrillation in the literature),\n    have been investigated for more than 50 years. As a way to\n    concisely present the key prior literature in the protein\n    aggregation area, Table 1 in the main text summarizes 23 papers\n    by 10 groups of authors that provide 5 basic classes of\n    mechanisms for protein aggregation over the period from 1959 to\n    2007. However, and despite this major prior effort, still\n    lacking are both (i) anything approaching a consensus mechanism\n    (or mechanisms), and (ii) a generally useful, and thus widely\n    used, simplest/\"Ockham's razor\" kinetic model and associated\n    equations that can be routinely employed to analyze a broader\n    range of protein aggregation kinetic data. Herein we\n    demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism\n    of slow continuous nucleation, A --> B (rate constant k1),\n    followed by typically fast, autocatalytic surface growth, A + B\n    --> 2B (rate constant k2), is able to quantitatively account\n    for the kinetic curves from all 14 representative data sets of\n    neurological protein aggregation found by a literature search\n    (the prion literature was largely excluded for the purposes of\n    this study in order provide some limit to the resultant\n    literature that was covered). The F-W model is able to\n    deconvolute the desired nucleation, k1, and growth, k2, rate\n    constants from those 14 data sets obtained by four different\n    physical methods, for three different proteins, and in nine\n    different labs. The fits are generally good, and in many cases\n    excellent, with R2 values >or=0.98 in all cases. As such,\n    this contribution is the current record of the widest set of\n    protein aggregation data best fit by what is also the simplest\n    model offered to date. Also provided is the mathematical\n    connection between the 1997 F-W 2-step mechanism and the 2000\n    3-step mechanism proposed by Saitô and co-workers. In\n    particular, the kinetic equation for Saitô's 3-step\n    mechanism is shown to be mathematically identical to the\n    earlier, 1997 2-step F-W mechanism under the 3 simplifying\n    assumptions Saitô and co-workers used to derive their\n    kinetic equation. A list of the 3 main caveats/limitations of\n    the F-W kinetic model is provided, followed by the main\n    conclusions from this study as well as some needed future\n    experiments.\n  \n\n\n  This model is hosted on \n  BioModels Database\n  and identified by: \n  BIOMD0000000567.\n  To cite BioModels Database, please use: \n  BioModels Database:\n  An enhanced, curated and annotated resource for published\n  quantitative kinetic models.\n\n\n  To the extent possible under law, all copyright and related or\n  neighbouring rights to this encoded model have been dedicated to\n  the public domain worldwide. Please refer to \n  CC0\n  Public Domain Dedication for more information.\n\n\n    ","dates":{"last_modification":"2024-08-21","publication":"2024-09-02","submission":"2015-01-16"},"accession":"BIOMD0000000567","cross_references":{"pr":["PR:P04156"],"pubmed":["18247636","16981683","16981676","12785778","16301322","16081040","16536526","12186976","16117499","16981684","16981679","15794636"],"biomodels__db":["MODEL1501160000","BIOMD0000000567"],"go":["GO:1990000"],"taxonomy":["9606"],"bto":["BTO:0000142"],"doi":["10.1021/ja9705102"]}}