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

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...
Peptide Bonds02:43

Peptide Bonds

A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...

You might also read

Related Articles

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

Sort by
Same author

Electron-donor modulated built-in electric fields in Ni<sub>2</sub>P/MoS<sub>2</sub> Heterostructures for accelerated sodium storage kinetics.

Journal of colloid and interface science·2026
Same author

STAT2-IRF1-ISG15-Associated Temporal Immune Reprogramming in Macrophages during Aspergillus fumigatus Infection.

Current microbiology·2026
Same author

Exceeding 0.94% Solar-to-Chemical Energy Conversion: Asymmetrically Charge-Distributed Local Double-Charge Layers for Benzyl Alcohol Oxidation and Hydrogen Coevolution.

ACS applied materials & interfaces·2026
Same author

Mechanochemical Rh(I)-Catalyzed C-N/C-C/C-H Triple Bond Activation of Amides.

Organic letters·2026
Same author

Balancing high performance with synthesis complexity: one-step hydrothermal growth of self-supported NiCo<sub>2</sub>Se<sub>4</sub> nanoflowers on carbon cloth for advanced asymmetric supercapacitors.

RSC advances·2026
Same author

Ammonia Concentration-Directed Preferential Growth Enhancing Lithium-Ion Diffusion in Li-Rich Mn-Based Oxide Cathodes.

Chemistry (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: May 29, 2026

Overlapping Peptide Library to Map Qa-1 Epitopes in a Protein
08:04

Overlapping Peptide Library to Map Qa-1 Epitopes in a Protein

Published on: December 20, 2017

SVEEVA descriptor application to peptide QSAR.

Jianbo Tong1, Ting Che, Shuling Liu

  • 1College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi'an, P. R. China. jianbotong@yahoo.com.cn

Archiv Der Pharmazie
|September 30, 2011
PubMed
Summary
This summary is machine-generated.

A new method, SVEEVA, was developed using principal component analysis (PCA) for peptide quantitative structure-activity relationship (QSAR) studies. This approach effectively models biological activity, showing promise for future research in peptide drug discovery.

More Related Videos

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Related Experiment Videos

Last Updated: May 29, 2026

Overlapping Peptide Library to Map Qa-1 Epitopes in a Protein
08:04

Overlapping Peptide Library to Map Qa-1 Epitopes in a Protein

Published on: December 20, 2017

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Bioinformatics

Background:

  • Quantitative structure-activity relationship (QSAR) studies are crucial for drug discovery.
  • Developing novel descriptors can improve the accuracy of QSAR models.
  • Amino acid descriptors are essential for modeling peptide properties.

Purpose of the Study:

  • To introduce a new descriptor, SVEEVA (principal component scores vector of electronic eigenvalue descriptors).
  • To evaluate the utility of SVEEVA scales in peptide QSAR modeling.
  • To assess the predictive power of models built using SVEEVA.

Main Methods:

  • Principal Component Analysis (PCA) was used to derive SVEEVA from 220 electronic eigenvalue descriptors.
  • Partial Least Squares (PLS) regression was employed to build QSAR models.
  • Three distinct peptide datasets were used: angiotensin-converting enzyme inhibitors, antimicrobial polypeptides, and thromboplastin inhibitors.

Main Results:

  • SVEEVA scales were successfully applied to three peptide QSAR panels.
  • High predictive model performance was achieved, with cross-validation correlation coefficients (Q²(LOO)) of 0.839, 0.949, and 0.976.
  • The models demonstrated strong correlation coefficients (R²(cum)) of 0.894, 0.995, and 0.995, respectively.

Conclusions:

  • SVEEVA scales can systematically express biological activity information.
  • This new descriptor methodology shows significant potential for peptide QSAR studies.
  • SVEEVA offers a valuable tool for understanding structure-activity relationships in peptides.