Performance of Markov State Models and Transition Networks on Characterizing Amyloid Aggregation Pathways from MD Data.

Abstract

Molecular dynamic (MD) simulations are an important tool for studying protein aggregation processes, which play a central role in a number of diseases including Alzheimer's disease. However, MD simulations produce large amounts of data, requiring advanced methods to extract mechanistic insight into the process under study. Transition networks (TNs) provide an elegant method to identify (meta)stable states and the transitions between them from MD simulations. Here, we apply two different methods to generate TNs for protein aggregation: Markov state models (MSMs), which are based on kinetic clustering the state space, and TNs using conformational clustering. The similarities and differences of both methods are elucidated for the aggregation of the fragment Aβ16-22 of the Alzheimer's amyloid-β peptide. In general, both methods perform excellently in identifying the main aggregation pathways. The strength of MSMs is that they provide a rather coarse and thus simply to interpret picture of the aggregation process. Conformation-sorting TNs, on the other hand, outperform MSMs in uncovering mechanistic details. We thus recommend to apply both methods to MD data of protein aggregation in order to obtain a complete picture of this process. As part of this work, a Python script called ATRANET for automated TN generation based on a correlation analysis of the descriptors used for conformational sorting is made publicly available.