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 Concept Videos

Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Conserved Binding Sites01:49

Conserved Binding Sites

4.6K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Neurochemical imaging reveals changes in dopamine dynamics with photoperiod in a seasonally social vole species.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Inflammatory reprogramming of human brain endothelial cells compromises blood-brain barrier integrity in Alzheimer's disease.

bioRxiv : the preprint server for biology·2025
Same author

Protein corona formed on lipid nanoparticles compromises delivery efficiency of mRNA cargo.

Nature communications·2025
Same author

Vascular-Perfusable Human 3D Brain-on-Chip.

bioRxiv : the preprint server for biology·2025
Same author

Absence of testes at puberty impacts functional development of nigrostriatal but not mesoaccumbal dopamine terminals in a wild-derived mouse.

bioRxiv : the preprint server for biology·2025
Same author

Optical Fibers Functionalized with Single-Walled Carbon Nanotubes for Flexible Fluorescent Catecholamine Detection.

Langmuir : the ACS journal of surfaces and colloids·2025

Related Experiment Video

Updated: Oct 7, 2025

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes
09:28

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes

Published on: January 10, 2017

8.3K

Supervised learning model predicts protein adsorption to carbon nanotubes.

Nicholas Ouassil1, Rebecca L Pinals2, Jackson Travis Del Bonis-O'Donnell1

  • 1Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.

Science Advances
|January 7, 2022
PubMed
Summary

Predicting nanoparticle-protein interactions is crucial for nanotechnology. Researchers developed a random forest classifier to accurately identify proteins that bind to nanoparticles based on protein sequence, aiding in biomolecular sensing and delivery applications.

More Related Videos

Monitoring Protein Adsorption with Solid-state Nanopores
08:51

Monitoring Protein Adsorption with Solid-state Nanopores

Published on: December 2, 2011

13.7K
Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions
12:20

Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions

Published on: July 22, 2013

18.4K

Related Experiment Videos

Last Updated: Oct 7, 2025

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes
09:28

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes

Published on: January 10, 2017

8.3K
Monitoring Protein Adsorption with Solid-state Nanopores
08:51

Monitoring Protein Adsorption with Solid-state Nanopores

Published on: December 2, 2011

13.7K
Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions
12:20

Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions

Published on: July 22, 2013

18.4K

Area of Science:

  • Biotechnology
  • Nanotechnology
  • Proteomics

Background:

  • Engineered nanoparticles offer potential in biomolecular sensing and delivery.
  • Unpredictable protein corona formation hinders nanotechnology implementation in biological systems.
  • Developing predictive models for protein-nanoparticle interactions is essential.

Purpose of the Study:

  • To develop a predictive model for identifying proteins that adsorb to nanoparticles based on protein sequence.
  • To understand the relationship between amino acid properties and protein binding affinity to single-walled carbon nanotubes (SWCNTs).

Main Methods:

  • Utilized a random forest classifier trained on mass spectrometry data.
  • Analyzed protein sequences and amino acid-based properties to predict adsorption.
  • Modeled protein corona formation on SWCNT-based nanosensors.
  • Experimentally validated predictions of high-affinity SWCNT-binding proteins.

Main Results:

  • Achieved 78% accuracy and 70% precision in identifying proteins that adsorb to nanoparticles.
  • Identified specific protein features, such as high content of solvent-exposed glycines and nonsecondary structure amino acids, associated with increased SWCNT binding.
  • Validated the classifier's predictive power through experimental testing.

Conclusions:

  • The developed random forest classifier offers a significant advancement in predicting protein-nanoparticle interactions.
  • This approach facilitates the effective implementation of nanotechnologies in biological applications by addressing the challenge of protein corona formation.
  • The study provides a computational tool to guide the design and application of nanomaterials in biotechnology.