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

You might also read

Related Articles

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

Sort by
Same author

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata.

International journal of molecular sciences·2023
Same author

Prediction of structural alphabet protein blocks using data mining.

Biochimie·2022
Same author

Entropy-driven translocation of disordered proteins through the Gram-positive bacterial cell wall.

Nature microbiology·2021
Same author

Structural disorder of plasmid-encoded proteins in Bacteria and Archaea.

BMC bioinformatics·2018
Same author

Finding Statistically Significant Repeats in Nucleic Acids and Proteins.

Journal of computational biology : a journal of computational molecular cell biology·2017
Same author

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences.

Computational biology and chemistry·2016

Related Experiment Video

Updated: Mar 26, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

15.7K

Software tools for simultaneous data visualization and T cell epitopes and disorder prediction in proteins.

Davorka R Jandrlić1, Goran M Lazić2, Nenad S Mitić2

  • 1University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, Belgrade, Serbia.

Journal of Biomedical Informatics
|February 7, 2016
PubMed
Summary

New software tools, EpDis and MassPred, aid bioinformatics by enabling parallel prediction of T cell epitopes and protein disorder. These open-source tools streamline analysis for individual proteins or large datasets.

Keywords:
Disorder predictionMassive parallel predictionT cell epitope predictionVisualization

More Related Videos

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

1.3K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.8K

Related Experiment Videos

Last Updated: Mar 26, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

15.7K
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

1.3K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.8K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Immunoinformatics

Background:

  • Accurate prediction of T cell epitopes and protein disorder is crucial for understanding immune responses and protein function.
  • Existing bioinformatics tools often lack flexibility and ease of use for integrating multiple prediction methods.
  • There is a need for open-source software that supports parallel processing and comparative analysis of prediction results.

Purpose of the Study:

  • To develop and present EpDis and MassPred, extendable open-source software tools for bioinformatic research.
  • To enable the parallel application of diverse prediction methods for T cell epitopes, protein disorder, and hydropathy.
  • To provide a user-friendly interface for applying, visualizing, and analyzing prediction results.

Main Methods:

  • Development of EpDis and MassPred software with semi-automated installation of external predictors.
  • Implementation of an interface for applying prediction methods to individual proteins or large datasets.
  • Integration of visualization tools, consensus calculation, and experimental data import functionalities.
  • Inclusion of a graphical user interface and data storage in a relational database or flat file.

Main Results:

  • EpDis and MassPred offer a unified platform for various bioinformatic predictions, including T cell epitopes and disordered regions.
  • The tools facilitate semi-automated installation and parallel execution of multiple prediction algorithms.
  • Users can visualize results, compute consensus predictions, and compare in silico findings with experimental data.
  • The MassPred component allows for massive parallel application of integrated predictors.

Conclusions:

  • EpDis and MassPred enhance bioinformatic research by providing flexible and efficient tools for prediction and analysis.
  • These open-source software solutions support the integration and comparative assessment of multiple prediction methods.
  • The tools are valuable for researchers studying T cell epitopes, protein disorder, and related biological processes.