Journal Article

FZJ-2021-00342

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Performance of Markov State Models and Transition Networks on Characterizing Amyloid Aggregation Pathways from MD Data

Illig, A.-M. ; Strodel, B. (Corresponding author)FZJ*

2020
Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/26829  doi:10.1021/acs.jctc.0c00727

Abstract: 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.

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Contributing Institute(s):

1. Strukturbiochemie (IBI-7)

Research Program(s):

1. 553 - Physical Basis of Diseases (POF3-553) (POF3-553)

Appears in the scientific report 2020

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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