Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A novel pattern recognition algorithm to classify membrane protein unfolding pathways with high-throughput

Annalisa Marsico1, Dirk Labudde, Tanuj Sapra

  • 1Biotec, TU Dresden, Germany. annalisa.marsico@biotec.tu-dresden.de

Bioinformatics (Oxford, England)
|January 24, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

RNA design across eras: from covariance models to modern generative AI.

Nature reviews. Genetics·2026
Same author

Enhancing link prediction in biomedical knowledge graphs with BioPathNet.

Nature biomedical engineering·2026
Same author

Integrative gene and isoform co-expression networks reveal regulatory rewiring in stress-related psychiatric disorders.

iScience·2025
Same author

TransFactor-prediction of pro-viral SARS-CoV-2 host factors using a protein language model.

Bioinformatics (Oxford, England)·2025
Same author

A searchable atlas of pathogen-sensitive lncRNA networks in human macrophages.

Nature communications·2025
Same author

BioPathNet: Enhancing Link Prediction in Biomedical Knowledge Graphs through Path Representation Learning.

Research square·2024

A new algorithm automates analysis of single-molecule force spectroscopy data, identifying faulty curves and distinct protein unfolding pathways. This advances high-throughput studies of membrane protein structures.

Area of Science:

  • Biophysics
  • Structural Biology
  • Computational Biology

Background:

  • Membrane protein misfolding is implicated in various human diseases, yet their structures remain largely unknown due to limited high-resolution data.
  • Single-molecule force spectroscopy (SMFS) offers insights into protein structure and forces but generates complex, high-throughput data.
  • Manual analysis of SMFS data is time-consuming, subjective, and a bottleneck for research.

Purpose of the Study:

  • To develop and validate a novel algorithm for automated analysis of single-molecule force spectroscopy (SMFS) data.
  • To distinguish between spurious and biologically relevant force curves.
  • To classify different protein unfolding pathways from SMFS data.

Main Methods:

  • A three-stage algorithm involving dimension reduction for noise reduction.

Related Experiment Videos

  • Dynamic time warping for curve alignment and pairwise distance computation.
  • Clustering techniques to group similar force curves and identify distinct unfolding pathways.

    • Main Results:

    • The algorithm achieved 81% accuracy in identifying spurious curves and 76% accuracy in classifying unfolding pathways on a dataset of 135 bacteriorhodopsin mutant P50A force curves.
    • Identified five distinct unfolding pathways for bacteriorhodopsin, including major and minor unfolding events.
    • Linked folding barriers to residue conservation, providing structural insights.

    Conclusions:

    • The developed algorithm effectively addresses the analysis bottleneck in force spectroscopy.
    • Automated analysis leads to more consistent and reproducible results for membrane protein structural studies.
    • This method enables high-throughput analysis, facilitating a deeper understanding of membrane protein structure and function.