ArticleScientific reports2017
Machine Learning and Network Analysis of Molecular Dynamics Trajectories Reveal Two Chains of Red/Ox-specific Residue Interactions in Human Protein Disulfide Isomerase.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.Acta pharmacologica Sinica · 2025Review
- Evaluating the protonation state of the catalytic Cys25 in cruzain cysteine protease: A target for Chagas disease.Protein science : a publication of the Protein Society · 2025Article
- Conformational Dynamics and Binding Interactions of SARS-CoV-2 Spike Protein Variants: Omicron, XBB.1.9.2, and EG.5.Journal of chemical information and modeling · 2025Article
- Using Machine Learning to Analyze Molecular Dynamics Simulations of Biomolecules.The journal of physical chemistry. B · 2025Article
- AI-integrated network for RNA complex structure and dynamic prediction.Biophysics reviews · 2024Review
- Using residue interaction networks to understand protein function and evolution and to engineer new proteins.Current opinion in structural biology · 2024Review
- Structural and Dynamic Analyses of Pathogenic Variants inLife (Basel, Switzerland) · 2024Article
- Exploring the Influence of Zinc Ions on the Conformational Stability and Activity of Protein Disulfide Isomerase.International journal of molecular sciences · 2024Article
- Molecular Design of FRET Probes Based on Domain Rearrangement of Protein Disulfide Isomerase for Monitoring Intracellular Redox Status.International journal of molecular sciences · 2023Article
- Beyond Structural Bioinformatics for Genomics with Dynamics Characterization of an Expanded KRAS Mutational Landscape.bioRxiv : the preprint server for biology · 2023Article
- Beyond structural bioinformatics for genomics with dynamics characterization of an expanded KRAS mutational landscape.Computational and structural biotechnology journal · 2023Article
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks.Frontiers in bioinformatics · 2022Review
- Article
- Integrated Computational Approaches and Tools forAllosteric Drug Discovery.International journal of molecular sciences · 2020Review
- Stability and Conformational Resilience of Protein Disulfide Isomerase.Biochemistry · 2019Article
- Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives.International journal of molecular sciences · 2019Review
- Molecular basis of rutin inhibition of protein disulfide isomerase (PDI) by combinedRSC advances · 2018Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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