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

Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Crossing Over01:34

Crossing Over

Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process called synapsis.
In order to...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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 form...
Gene Conversion02:08

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
The DNA Helix01:07

The DNA Helix

Deoxyribonucleic acid, or DNA, is the genetic material responsible for passing traits from generation to generation in all organisms and most viruses. DNA is composed of two strands of nucleotides that wind around each other to form a spring-like structure called a double helix. However, the double helix is not perfectly symmetrical. Instead, there are regularly occurring grooves in the structure. The major groove occurs where the sugar-phosphate backbones are relatively far apart. This space...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...

You might also read

Related Articles

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

Sort by
Same author

Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics.

Patterns (New York, N.Y.)·2026
Same author

Comparative Efficacy and Safety of 15-Valent and 13-Valent Pneumococcal Conjugate Vaccines Alone and in Combination with the 23-Valent Polysaccharide Vaccine: A Systematic Review and Meta-analysis.

Biological & pharmaceutical bulletin·2026
Same author

Three fluoroquinolones with different epithelial lining fluid penetration in a murine lung infection model: an experimental site-specific PK/PD study.

Antimicrobial agents and chemotherapy·2026
Same author

Continuation of belimumab in patients with systemic lupus erythematosus in a real-world setting: A single-center retrospective cohort study.

Lupus·2026
Same author

Low rate of pharmacists accessing renal function laboratory values: A cross-sectional study using electronic medical records of a Japanese community pharmacy.

Drug discoveries & therapeutics·2026
Same author

Polyarticular juvenile idiopathic arthritis: insights from genetic studies on disease risk and pathogenesis.

Current opinion in rheumatology·2026

Related Experiment Video

Updated: Jun 5, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Protein structure predictions by parallel simulated annealing molecular dynamics using genetic crossover.

Yoshitake Sakae1, Tomoyuki Hiroyasu, Mitsunori Miki

  • 1Department of Physics, Nagoya University, Nagoya Aichi, Japan.

Journal of Computational Chemistry
|January 20, 2011
PubMed
Summary

This study introduces a novel protein structure prediction method combining simulated annealing and genetic crossover. The new approach, using molecular dynamics, found lower energy conformations than traditional simulated annealing for an alpha-helical miniprotein.

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

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 5, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein folding

Background:

  • Protein structure prediction is crucial for understanding function.
  • Simulated annealing (SA) excels at local conformational search.
  • Genetic crossover (GC) is effective for global conformational space exploration.

Purpose of the Study:

  • To develop an enhanced conformational search method for protein systems.
  • To integrate the strengths of simulated annealing and genetic crossover for global minimum energy structure determination.
  • To improve upon previous methods by replacing Monte Carlo with molecular dynamics in SA.

Main Methods:

  • A hybrid approach combining simulated annealing with genetic crossover.
  • Utilizing molecular dynamics (MD) for simulated annealing, replacing the traditional Monte Carlo algorithm.
  • Employing a genetic two-point crossover for global search.

Main Results:

  • The proposed method successfully identified protein conformations with lower energy than conventional simulated annealing.
  • Demonstrated effectiveness using an alpha-helical miniprotein model system.
  • Achieved superior results compared to standard simulated annealing molecular dynamics simulations.

Conclusions:

  • The integrated SA-MD and GC method is effective for global conformational search in protein systems.
  • This approach offers an advantage in finding lower energy structures compared to conventional SA.
  • The findings contribute to more accurate protein structure prediction and analysis.