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

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

8.0K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
8.0K
Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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...
5.0K

You might also read

Related Articles

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

Sort by
Same author

NeuroCL: A deep learning approach for identifying neuropeptides based on contrastive learning.

Analytical biochemistry·2025
Same author

Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.

Proteomics. Clinical applications·2025
Same author

GCNLA: Inferring Cell-Cell Interactions From Spatial Transcriptomics With Long Short-Term Memory and Graph Convolutional Networks.

IEEE journal of biomedical and health informatics·2025
Same author

Benchmarking of methods that identify alternative polyadenylation events in single-/multiple-polyadenylation site genes.

NAR genomics and bioinformatics·2025
Same author

Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.

BMC biology·2025
Same author

Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM.

PLoS computational biology·2025

Related Experiment Video

Updated: Dec 23, 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

2.4K

PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature

Yu P Zhang1,2, Quan Zou1

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.

Bioinformatics (Oxford, England)
|April 30, 2020
PubMed
Summary

This study introduces a new machine learning tool, the Physicochemical Property-based Therapeutic Peptide Predictor (PPTPP), for identifying therapeutic peptides and their key properties. PPTPP offers high-quality, simultaneous prediction and property identification, advancing peptide-based drug discovery.

More Related Videos

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.5K
Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

12.9K

Related Experiment Videos

Last Updated: Dec 23, 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

2.4K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.5K
Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

12.9K

Area of Science:

  • Biotechnology
  • Computational Biology
  • Drug Discovery

Background:

  • Peptides are versatile molecules with significant potential for therapeutic and diagnostic applications.
  • Accurate identification and characterization of therapeutic peptides are crucial for drug development.
  • Existing machine learning predictors often lack the ability to simultaneously predict therapeutic peptides and identify their informative physicochemical properties (IPPs).

Purpose of the Study:

  • To develop a novel machine learning method for high-quality, generic prediction of therapeutic peptides.
  • To enable the simultaneous identification of informative physicochemical properties (IPPs) of therapeutic peptides.
  • To address the limitations of current predictors in performing both tasks concurrently.

Main Methods:

  • Development of the Physicochemical Property-based Therapeutic Peptide Predictor (PPTPP) using a Random Forest algorithm.
  • Implementation of a novel feature encoding and learning scheme for generating and ranking physicochemical property-related features.
  • Evaluation of the predictor's performance in identifying therapeutic peptides and their IPPs.

Main Results:

  • The PPTPP demonstrates high comparability to established predictors in identifying multiple therapeutic peptides.
  • The method successfully identifies informative physicochemical properties (IPPs) of therapeutic peptides.
  • The predictor's capacity was validated, showing potential for broader applications in therapeutic peptide research.

Conclusions:

  • The PPTPP is a robust tool for therapeutic peptide identification and IPP analysis.
  • This approach advances the field by enabling simultaneous prediction and property identification.
  • The developed method holds promise for accelerating the discovery and investigation of novel therapeutic peptides.