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

Amyloid Fibrils03:03

Amyloid Fibrils

9.2K
Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
Amyloid deposits were observed as early as 1639 in the liver and the spleen.   In 1854, Rudolph Virchow performed iodine staining,...
9.2K
Protein Families02:47

Protein Families

15.2K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
15.2K
Leaky Scanning02:28

Leaky Scanning

5.1K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.1K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Journal of proteomics·2026
Same author

Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.

bioRxiv : the preprint server for biology·2026
Same author

Skin-cancer screening preferences and trust in clinicians among outdoor enthusiasts: preference for specialist-led checks.

PeerJ·2026
Same author

Skin cancer prevalence among outdoor activity participants from Queensland, Australia: aquatic <i>versus</i> land-based sun exposure.

PeerJ·2026
Same author

Rapid Peptide Mapping of Monoclonal Antibodies with Direct Infusion Mass Spectrometry.

bioRxiv : the preprint server for biology·2026
Same author

Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: May 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.4K

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS)

Justin McKetney1,2,3,4,5, Ian J Miller1,2, Alexandre Hutton6,7,8

  • 1Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.

Analytical Chemistry
|January 27, 2025
PubMed
Summary

Predicting peptide ion mobility using High-Field Asymmetric Waveform Ion Mobility (FAIMS) is now possible. Machine learning models, including Long-Term Short-Term Memory (LSTM) networks, accurately forecast peptide behavior in mass spectrometry, aiding proteomics research.

More Related Videos

Insights into the Interactions of Amino Acids and Peptides with Inorganic Materials Using Single-Molecule Force Spectroscopy
05:44

Insights into the Interactions of Amino Acids and Peptides with Inorganic Materials Using Single-Molecule Force Spectroscopy

Published on: March 6, 2017

8.0K
T-wave Ion Mobility-mass Spectrometry: Basic Experimental Procedures for Protein Complex Analysis
16:40

T-wave Ion Mobility-mass Spectrometry: Basic Experimental Procedures for Protein Complex Analysis

Published on: July 31, 2010

24.6K

Related Experiment Videos

Last Updated: May 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.4K
Insights into the Interactions of Amino Acids and Peptides with Inorganic Materials Using Single-Molecule Force Spectroscopy
05:44

Insights into the Interactions of Amino Acids and Peptides with Inorganic Materials Using Single-Molecule Force Spectroscopy

Published on: March 6, 2017

8.0K
T-wave Ion Mobility-mass Spectrometry: Basic Experimental Procedures for Protein Complex Analysis
16:40

T-wave Ion Mobility-mass Spectrometry: Basic Experimental Procedures for Protein Complex Analysis

Published on: July 31, 2010

24.6K

Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Computational Biology

Background:

  • Peptide ion mobility analysis enhances mass spectrometry-based proteomics.
  • Accurate prediction of peptide ion mobility aids assay development and database searching.
  • Predictive methods for drift tube ion mobility exist, but High-Field Asymmetric Waveform Ion Mobility (FAIMS) prediction is less explored.

Purpose of the Study:

  • To develop and validate models for predicting peptide ion mobility in FAIMS.
  • To explore machine learning approaches for modeling FAIMS mobility.
  • To improve the accuracy and efficiency of proteomics data analysis.

Main Methods:

  • Utilized a multi-label classification scheme to model peptide ion mobility.
  • Trained a random forest and a Long-Term Short-Term Memory (LSTM) neural network on over 100,000 human peptide precursors.
  • Ensembled predictions from both models to improve performance.

Main Results:

  • The ensemble model outperformed individual models, achieving a higher F2 score.
  • Demonstrated predictive performance with an F2 score of 0.66 and AUROC of 0.928 on a test set of nearly 40,000 E. coli peptide ions.
  • Identified specific cases where models made mistakes, providing insights for further refinement.

Conclusions:

  • Successfully modeled peptide ion mobility in FAIMS using machine learning.
  • The developed deep learning model offers a valuable tool for proteomics research.
  • The model is accessible online, facilitating broader application in assay development and data analysis.