Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Türk-Alman Universit...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Current Protein and Peptide Science
Article . 2024 . Peer-reviewed
Data sources: Crossref
versions View all 3 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Current Stage and Future Perspectives for Homology Modeling, Molecular Dynamics Simulations, Machine Learning with Molecular Dynamics, and Quantum Computing for Intrinsically Disordered Proteins and Proteins with Intrinsically Disordered Regions

Authors: Orkid Coskuner-Weber; Vladimir N. Uversky;

Current Stage and Future Perspectives for Homology Modeling, Molecular Dynamics Simulations, Machine Learning with Molecular Dynamics, and Quantum Computing for Intrinsically Disordered Proteins and Proteins with Intrinsically Disordered Regions

Abstract

Abstract:: The structural ensembles of intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) cannot be easily characterized using conventional experimental techniques. Computational techniques complement experiments and provide useful insights into the structural ensembles of IDPs and proteins with IDRs. Herein, we discuss computational techniques such as homology modeling, molecular dynamics simulations, machine learning with molecular dynamics, and quantum computing that can be applied to the studies of IDPs and hybrid proteins with IDRs. We also provide useful future perspectives for computational techniques that can be applied to IDPs and hybrid proteins containing ordered domains and IDRs.

Country
Turkey
Keywords

Machine learning with molecular dynamics, Intrinsically disordered proteins, Molecular dynamics simulations, Protein Conformation, Homology modeling, Quantum computing, Molecular Dynamics Simulation, Computing Methodologies, Intrinsically Disordered Proteins, Machine Learning, Proteins with intrinsically disordered regions, Quantum Theory

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    4
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 52
    download downloads 10
  • 52
    views
    10
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
4
Top 10%
Average
Average
52
10
Green