(620ay) Dynamics of Aggregation of Proteins (Rapid Fire) | AIChE

(620ay) Dynamics of Aggregation of Proteins (Rapid Fire)


Zheng, S. - Presenter, University of Southern California
Shing, K. - Presenter, University of Southern California
Sahimi, M. - Presenter, University of Southern California

Dynamics of Aggregation of Proteins

Zheng, Katherine S. Shing and Muhammad Sahimi

Misfolded proteins aggregate together and form
large clusters. A normal protein typically has hydrophobic interior and shields
itself with a hydrophilic exterior. When a protein misfolds,
it exposes its inner hydrophobic portions to the outside, which may result in
an inter-molecular interaction with other misfolded proteins, leading to aggregation.
Protein aggregation is associated with a variety of human neurodegenerative
diseases, including Alzheimer's, Parkinson's and prion disease. The aggregates'
structural characteristics and molecular-level details are, however, hard to be
studied by experimental methods and, thus, the mechanism of aggregations still
remains poorly understood. But, advances in computational power and
simulations, particularly, molecular dynamics (MD) simulations, provide the
opportunity to simulate the aggregation process, hence helping us to unveil the
secrets behind the phenomena and find the way to cure the diseases.

Previous works indicated that the proteins
linked to neurodegenerative diseases, such as amyloid beta-protein to
Alzheimer's disease and PrP prion protein to prion
disease, only require a short portions of their entire chains, typically 4-10
residues in length, to begin aggregating. Such short portions contain high
hydrophobic residues that are believed to drive the aggregation
into fibrils. We have used discontinuous MD (DMD) technique to simulate the
aggregation process of multiple 10-residue peptides into ordered fibrils, as
the first step toward simulating the same in a crowded cellular environment. The
DMD simulation uses coarse-grained peptides models and discontinuous stepwise
potentials to provide dramatically faster simulation speed for big systems
comparing with the classical continuous all-atom MD simulations, without sacrificing
much of the necessary details.

We simulated
multiple systems that have 8, 16 and 32 peptides. In each system we have simulated
several cases at various temperatures, and investigated the characteristics of the
aggregated protein clusters, including the number of hydrogen bonds and of the
beta-sheets, the mean-square displacement and diffusion coefficient, as well as
the radius of gyration, and the distribution of the cluster sizes. Interesting
and physically relevant phenomena are observed, which will be reported.