Steckmann et al., (2012). Kinetics of peptide secondary structure conversion during amyloid β-protein fibrillogenesis.
October 2015, model of the month by Lu Li
Original models: BIOMD0000000533.
Amyloid fibrils can be found in many neurological diseases, including Alzheimer's disease (AD) the most common cause of dementia. Naturally occurring Aβ peptides start to aggregate once their secondary structures change. This is then followed by multi-step polymerization process to form amyloid fibrils and plaques. However, these end products are actually less toxic than the initial aggregates, that are the soluble oligomeric intermediates called protofibrils.
Experimental methods are limited in providing aggregation kinetic information in relation to transitions among secondary structures. Therefore, computational modelling can be helpful in estimating these rate parameters and developing therapeutic strategies in the future.
Steckmann et al. [1, BIOMD0000000533], set up a detailed kinetic state-transition model including key secondary structures of amyloid peptides: the random coil/β-turn (RCT0), the α-helix(α), the β-strand/sheet (βN), the β-aggregate/protofibril(βTX), and the mature fibril (βM), as shown in Figure 1. The authors considered that α-helical structures play an important role in the fibrillization process, as its hydrophobic residues facilitate aggregation and the formation of protofibrils. However, according to in vitro experimental data, a certain amount of random coil/β-turn peptides will retain their structure and never convert to α-helices (shown as RCT1 in Figure 1). Furthermore, the presence of β-aggregates facilitate the structure conversions from random coils/β-turns to α-helices, and from α-helices to β-strands. The authors introduced different chemical species of β-strand/sheet (βN1,βN2,βN3 …), and used those transition steps to mimic the initial delay in concentration changes, also called the lag phase in fibrillogenesis, which is believed to be caused by structural heterogeneity of β-strands.
As the duration of lag phase and concentration of secondary structures vary greatly depending on the experimental conditions, Steckmann et al. determined different parameter sets and transition steps by fitting with the corresponding experimental data. By doing so, the authors demonstrated that their model is flexible and general enough to represent different experimental observations. They also provided ranges of kinetic rate parameters that can be used in future analysis.
Figure 3Simulation results match with experimental observations from Walsh et al., 1999 (a) and Fezoui and Teplow 2002 (b-d). .
Although limited by the available experimental methods to accurately measure the concentration change of secondary structures in fibrillogenesis, this model provides a general framework that could be further developed in the future. Steckmann et al. (2012), not only demonstrated how simulation can reproduce the lag phase observed experimentally, but also the importance of α-helical structures in aggregation.However, obtaining a more accurate parameter set is desirable and will certainly deepen our understanding of fibrillogenesis.
Figure 1 A state-transition model of secondary structure conversion process involved in Aβ fibrillogenesis. In addition to the abbreviations explained above, k0,k1,k2,k3, and k4 are rate parameters and ε represents fraction of initial random coil/β-turn that are stable and do not participate in fibrillogenesis. Figure taken from .
Figure 2 Simulation results agree with experimental data from Kirkitadze et al. 2001. β-structure is the sum of all the β species in the model. .
Figure 2 and Figure 3a show that the simulation results of the model that can accurately represent different in vitro experiments [2, 3], which show the concentration changes of secondary structures against time. Corresponding to different experimental conditions, the author used several parameter sets, nevertheless the simulation results show similar trends, which are the initial lag phase in fibril formation and the drop in α-helix concentration near the end of the process.
Figure 3b-d illustrates the importance of α-helices in accelerating Aβ fibrillogenesis. With increasing amount of helices stabilizing trifluoroethanol (TFE), the fibril formation becomes faster and the lag phase becomes shorter. The simulation results match with experimental observations , however with three distinct sets of parameters.
Figure 3Absolute value of the difference between parameter values given in the paper and parameter values encoded in the model in percent. .
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