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ATRANET – Automated generation of transition networks for the structural characterization of intrinsically disordered proteins.

Authors :
Schäffler, Moritz
Khaled, Mohammed
Strodel, Birgit
Source :
Methods. Oct2022, Vol. 206, p18-26. 9p.
Publication Year :
2022

Abstract

• Transition networks built from MD simulations reveal the configurational ensemble and structural interconversions of IDPs in an easy-to-grasp manner. • The creation of transition networks with the open-source software ATRANET is explained. • The effects of molecular descriptors on transition networks is demonstrated. Intrinsically disordered proteins (IDPs) do not fold into a unique three-dimensional structure but sample different configurations of different probabilities that further change with the surrounding of the IDPs. The structural heterogeneity and dynamics of IDPs pose a challenge for the characterization of their structures by experimental techniques only. Molecular dynamics (MD) simulations provide a powerful complement to experimental approaches for that purpose. However, MD simulations on the micro- to millisecond timescale generate a lot of data of protein motions, necessitating advanced post-processing techniques to extract the relevant information. Here, we demonstrate how transition networks created from MD trajectories allow revealing the configurational ensemble and structural interconversions of IDPs, using the amyloid- β peptide as example. The construction of transition networks relies on molecular descriptors as input, and we show how the choice of descriptors influences the resulting transition network. The transition networks are generated with the open-source Python script ATRANET, and we explain the usage of ATRANET by providing a detailed workflow and exemplary analysis for amyloid- β , which can be easily generalized to other IDPs and even protein aggregation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10462023
Volume :
206
Database :
Academic Search Index
Journal :
Methods
Publication Type :
Academic Journal
Accession number :
159169968
Full Text :
https://doi.org/10.1016/j.ymeth.2022.07.013