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Motivated by the biologically important and complex phenomena of Abeta peptide aggregation in Alzheimers disease, we introduce a model and simulation methodology for studying protein aggregation that includes extra-cellular aggregation, aggregation on the cell-surface assisted by a membrane bound protein, and in addition, supply, clearance, production and sequestration of peptides and proteins. The model is used to produce equilibrium and kinetic-aggregation phase diagrams for aggregation onset and of reduced stable Abeta monomer concentrations due to aggregation. The methodology we implemented permits modeling of a phenomenon involving orders of magnitude differences in time scales and concentrations which can be retained in the simulation. We demonstrate how to identify ranges of parameter values that give monomer concentration depletion upon aggregation similar to that observed in Alzheimers disease. We show how very different behavior can be obtained as reaction parameters and protein concentrations vary, and discuss the difficulty reconciling results of experiments from two vastly different concentration regimes. The latter is an important general issue in relating in-vitro and mice based experiments to humans.
Computational prediction of membrane protein (MP) structures is very challenging partially due to lack of sufficient solved structures for homology modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light on protein contact pre
We develop a theoretical approach to the protein folding problem based on out-of-equilibrium stochastic dynamics. Within this framework, the computational difficulties related to the existence of large time scale gaps in the protein folding problem a
Models of protein energetics which neglect interactions between amino acids that are not adjacent in the native state, such as the Go model, encode or underlie many influential ideas on protein folding. Implicit in this simplification is a crucial as
The phenomena of stochasticity in biochemical processes have been intriguing life scientists for the past few decades. We now know that living cells take advantage of stochasticity in some cases and counteract stochastic effects in others. The source
Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here we describe a high-throughput deep transfer learning method that first predicts MP contacts