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

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.6K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.6K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.8K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.8K
Transduction01:16

Transduction

1.1K
Among the three main modes of HGT—transformation, conjugation, and transduction—transduction is unique in that it is mediated by bacteriophages, or bacterial viruses.Transduction occurs in two ways. Generalized transduction occurs during the lytic cycle of a bacteriophage infection. In this process, bacteriophages infect bacterial cells, replicate within them, and ultimately cause cell lysis, releasing newly assembled virions. Occasionally, random fragments of the bacterial genome...
1.1K
Mismatch Repair01:20

Mismatch Repair

6.3K
Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
6.3K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.0K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.0K

You might also read

Related Articles

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

Sort by
Same author

Per- and polyfluoroalkyl substances (PFAS) in early life is associated with childhood intestinal inflammation: analyses of three birth cohorts.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

Improving hit discovery by integrating activity cliff sensitivity into active learning.

Bioinformatics (Oxford, England)·2026
Same author

Validation of a 2-Gene Blood Test for Kawasaki Disease in Febrile Children.

JAMA network open·2026
Same author

Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.

Diagnostics (Basel, Switzerland)·2026
Same author

Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.

NPJ precision oncology·2026
Same author

Intestinal Inflammation Impacts Gestational Weight Gain in Women With Inflammatory Bowel Disease and Growth in Offspring.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026

Related Experiment Video

Updated: Jan 11, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.2K

Coevolutionary signals in multiple sequence alignments improve virulence factor prediction with an MSA Transformer.

Taegyu Kim1, Changyun Cho2,3, Dohoon Lee4,5

  • 1Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826, Seoul, Republic of Korea.

Scientific Reports
|November 19, 2025
PubMed
Summary

This study introduces MSA-VF Predictor (MVP), a new deep learning method for identifying bacterial virulence factors (VFs) by analyzing protein coevolutionary information. MVP achieves high accuracy, outperforming existing models in predicting VFs.

Keywords:
CoevolutionDeep learningMSA TransformerMultiple sequence alignmentVirulence factor prediction

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.4K
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

7.4K

Related Experiment Videos

Last Updated: Jan 11, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.2K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.4K
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

7.4K

Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Virulence factors (VFs) are crucial for understanding bacterial pathogenesis and developing treatments for infectious diseases.
  • Coevolutionary information in protein sequences offers insights into the functional and structural roles of VFs.
  • Previous VF prediction methods have not fully utilized coevolutionary data.

Purpose of the Study:

  • To develop a novel deep learning method for predicting virulence factors (VFs) by incorporating coevolutionary information.
  • To enhance the accuracy and understanding of bacterial pathogenicity through advanced protein sequence analysis.

Main Methods:

  • Developed MSA-VF Predictor (MVP), a deep learning model utilizing Multiple Sequence Alignment (MSA) and MSA Transformer.
  • Extracted coevolutionary and homologous protein features from MSA data.
  • Proposed MSA-composition to represent amino acid latent vectors for prediction.

Main Results:

  • Achieved a prediction accuracy of 0.869 for virulence factors, surpassing current state-of-the-art models.
  • Demonstrated the significant contribution of coevolutionary information to MVP's predictive performance.
  • Identified crucial attention blocks in the MSA Transformer model for VF prediction.

Conclusions:

  • MSA-VF Predictor (MVP) effectively integrates coevolutionary information for accurate VF prediction.
  • The study highlights the importance of evolutionary interdependencies in understanding protein function and pathogenicity.
  • MVP provides a powerful new tool for bacterial pathogenesis research and drug development.