Exploring the step-by-step aggregation of amyloid proteins
Exploring the step-by-step aggregation of amyloid proteins lead image
Markov state models have become popular in computational biochemistry and biophysical communities as a technique for studying stationary and kinetic protein dynamics. With this technique, scientists can explore the source of irregular protein folds related to diseases such as type II diabetes, as well as Alzheimer’s, Creutzfeldt-Jakob, Parkinson’s and Huntington’s disease.
“A Markov state model is a “states and rates” model,” said Birgit Strodel, professor of computational biochemistry at Heinrich Heine University in Düsseldorf, Germany. “It tells us which state a molecular system is in and how often it might make the transition from one state to another.”
The new paper by Sengupta et al. introduced an automated Markov state model for systems that involve molecular self-assembly. As a proof of concept, they used the model to examine the different stages of aggregation of the KFFE peptide, a peptide derived from amyloid-β that plays an integral role in Alzheimer’s disease.
Their results suggest that amyloid aggregation of KFFE occurs in an orderly way. The code also produced the first Markov model highlighting the different aggregation pathways for the system.
“We plan to apply our new Markov state model building technique to larger systems of actual biological interest, such as the aggregation of the Alzheimer’s amyloid-beta peptide,” said Strodel. “We also expect that other computational chemists can use our work to study other fascinating molecular self-assembly systems.”
The team plans to use this information to guide future studies to understand protein aggregation at each step of production. Their code developed for this model is available to the public as a Jupyter notebook called TICAgg.
Source: “Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly,” by U. Sengupta, M. Carballo-Pacheco, and B. Strodel, The Journal of Chemical Physics (2019). The article can be accessed at https://doi.org/10.1063/1.5083915 .
