Machine learning and network analysis of molecular dynamics trajectories reveal two chains of red/ox-specific residue interactions in human protein disulfide isomerase

Razieh Karamzadeh, Mohammad Hossein Karimi-Jafari*, Ali Sharifi-Zarchi, Hamidreza Chitsaz, Ghasem Hosseini Salekdeh, Ali Akbar Moosavi-Movahedi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
3 Downloads (Pure)

Abstract

The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.

Original languageEnglish
Article number3666
Number of pages11
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - 16 Jun 2017
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Fingerprint Dive into the research topics of 'Machine learning and network analysis of molecular dynamics trajectories reveal two chains of red/ox-specific residue interactions in human protein disulfide isomerase'. Together they form a unique fingerprint.

Cite this