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

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-beta peptide as example. The construction of transition networks relies on molecular descriptors as input, and we show how the choice of descriptors in-fluences 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-beta, which can be easily generalized to other IDPs and even protein aggregation.

Automated summary

The structural heterogeneity and dynamics of intrinsically disordered proteins (IDPs) present a challenge for their characterization solely by experimental techniques. Molecular dynamics simulations offer a powerful complement to experimental approaches. In this study, the construction of transition networks from MD trajectories is demonstrated as a means to reveal the configurational ensemble and structural interconversions of IDPs, using amyloid-beta peptide as an example.