Document Type

Article

Publication Date

10-7-2010

Department

Computing

School

Computing Sciences and Computer Engineering

Abstract

Background: The aggregates of a protein called, ‘Aβ’ found in brains of Alzheimer’s patients are strongly believed to be the cause for neuronal death and cognitive decline. Among the different forms of Aβ aggregates, smaller aggregates called ‘soluble oligomers’ are increasingly believed to be the primary neurotoxic species responsible for early synaptic dysfunction. Since it is well known that the Aβ aggregation is a nucleation dependant process, it is widely believed that the toxic oligomers are intermediates to fibril formation, or what we call the ‘on-pathway’ products. Modeling of Aβ aggregation has been of intense investigation during the last decade. However, precise understanding of the process, pre-nucleation events in particular, are not yet known. Most of these models are based on curve-fitting and overlook the molecular-level biophysics involved in the aggregation pathway. Hence, such models are not reusable, and fail to predict the system dynamics in the presence of other competing pathways.

Results: In this paper, we present a molecular-level simulation model for understanding the dynamics of the amyloid-β (Aβ) peptide aggregation process involved in Alzheimer’s disease (AD). The proposed chemical kinetic theory based approach is generic and can model most nucleation-dependent protein aggregation systems that cause a variety of neurodegenerative diseases. We discuss the challenges in estimating all the rate constants involved in the aggregation process towards fibril formation and propose a divide and conquer strategy by dissecting the pathway into three biophysically distinct stages: 1) pre-nucleation stage 2) post-nucleation stage and 3) protofibril elongation stage. We next focus on estimating the rate constants involved in the protofibril elongation stages for Aβ42 supported by in vitro experimental data. This elongation stage is further characterized by elongation due to oligomer additions and lateral association of protofibrils (13) and to properly validate the rate constants involved in these phases we have presented three distinct reaction models. We also present a novel scheme for mapping the fluorescence sensitivity and dynamic light scattering based in vitro experimental plots to estimates of concentration variation with time. Finally, we discuss how these rate constants will be incorporated into the overall simulation of the aggregation process to identify the parameters involved in the complete Aβ pathway in a bid to understand its dynamics.

Conclusions: We have presented an instance of the top-down modeling paradigm where the biophysical system is approximated by a set of reactions for each of the stages that have been modeled. In this paper, we have only reported the kinetic rate constants of the fibril elongation stage that were validated by in vitro biophysical analyses. The kinetic parameters reported in the paper should be at least accurate upto the first two decimal places of the estimate. We sincerely believe that our top-down models and kinetic parameters will be able to accurately model the biophysical phenomenon of Aβ protein aggregation and identify the nucleation mass and rate constants of all the stages involved in the pathway. Our model is also reusable and will serve as the basis for making computational predictions on the system dynamics with the incorporation of other competing pathways introduced by lipids and fatty acids.

Comments

Published by 'BMC Bioinformatics' at 10.1186/1471-2105-11-S6-S24.

Publication Title

BMC Bioinformatics

Volume

11

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