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

This title appears in the Scientific Report : 2020

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

Molecular dynamic (MD) simulations are animportant tool for studying protein aggregation processes, whichplay a central role in a number of diseases including Alzheimer’sdisease. However, MD simulations produce large amounts of data,requiring advanced methods to extract mechanistic insight into thep...

Personal Name(s):	Illig, Alexander-Maurice
	Strodel, Birgit (Corresponding author)
Contributing Institute:	Strukturbiochemie; IBI-7
Published in:	Journal of chemical theory and computation, 16 (2020) 12, S. 7825 - 7839
Imprint:	Washington, DC 2020
DOI:	10.1021/acs.jctc.0c00727
Document Type:	Journal Article
Research Program:	Physical Basis of Diseases
Link:	OpenAccess Restricted
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/26829 in citations.
Please use the identifier: http://dx.doi.org/10.1021/acs.jctc.0c00727 in citations.

Molecular dynamic (MD) simulations are animportant tool for studying protein aggregation processes, whichplay a central role in a number of diseases including Alzheimer’sdisease. However, MD simulations produce large amounts of data,requiring advanced methods to extract mechanistic insight into theprocess under study. Transition networks (TNs) provide anelegant method to identify (meta)stable states and the transitionsbetween them from MD simulations. Here, we apply two differentmethods to generate TNs for protein aggregation: Markov statemodels (MSMs), which are based on kinetic clustering the statespace, and TNs using conformational clustering. The similaritiesand differences of both methods are elucidated for the aggregationof the fragment Aβ16−22 of the Alzheimer’s amyloid-β peptide. Ingeneral, both methods perform excellently in identifying the main aggregation pathways. The strength of MSMs is that they providea 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 proteinaggregation in order to obtain a complete picture of this process. As part of this work, a Python script called ATRANET forautomated TN generation based on a correlation analysis of the descriptors used for conformational sorting is made publiclyavailable.