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

Abstract

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.

Keywords: Amyloid-β; Disorder-to-order transition; Intrinsically disordered proteins; Molecular dynamics simulations; Transition networks.