Markov state models of peptide aggregation

Markov state models have become popular in the computational biophysics community as
a technique for identifying stationary and kinetic information of protein dynamics from
molecular dynamics (MD) simulation data. We extended the applicability of automated
Markov state modeling to simulation data of molecular self-assembly by constructing
collective coordinates from molecular descriptors that are invariant to permutations of
molecular indexing. Understanding molecular self-assembly is of critical importance if we
want to deepen our understanding of neurodegenerative diseases where the aggregation
of disordered peptides or misfolded proteins is thought to be the main culprit. I will present
the Markov state models that we obtained for the self-assembly of different peptides and
demonstrate that the Markov state models clearly map out the different aggregation
pathways, something which has not been possible with standard MD analysis tools.