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 Folding01:25

Protein Folding

8.6K
Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
8.6K
Structural Protein Function01:56

Structural Protein Function

2.8K
2.8K
Protein Organization01:24

Protein Organization

7.0K
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....
7.0K
Protein and Protein Structure02:15

Protein and Protein Structure

81.3K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
81.3K
Protein and Protein Structures02:15

Protein and Protein Structures

10.8K
10.8K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.3K
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...
11.3K

You might also read

Related Articles

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

Sort by
Same author

Ginsenoside Rd attenuates renal aging and fibrosis through inhibition of angiotensin II type 1 receptor signaling.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

PPS03 triggers cancer-selective necroptosis in hepatocellular carcinoma via macropinocytosis-dependent ROS overload.

Biomaterials·2026
Same author

Evaluation of Antigen Productivity and Inactivation Kinetics of a Recombinant Foot-and-Mouth Disease SAT1 Vaccine Strain.

Viruses·2026
Same author

Enhancing Capsid Stability of a Foot-and-Mouth Disease Virus Vaccine Strain Through VP1-Directed Chimeric Design While Preserving Antigenicity.

Vaccines·2026
Same author

Evaluation of an In-House Developed Foot-and-Mouth Disease Virus SAT 3 Vaccine Strain Based on Antigen Productivity and Inactivation Kinetics for Commercial Feasibility.

Vaccines·2026
Same author

Molecular descriptor driven QSPR modeling of Papp, TEER and Efflux Ratio from Caco-2 cells using machine learning for various phytochemicals.

Journal of the science of food and agriculture·2026
Same journal

Carboxymethyl chitosan/polyvinyl alcohol/sodium alginate hydrogels incorporating Fe<sup>3+</sup>/curcumin/tannic acid self-assembled nanocomplex with self-healing, antioxidant, and antibacterial properties.

International journal of biological macromolecules·2026
Same journal

NaCl-zwitterion enhanced chitosan-based hydrogels with super compressibility, conductivity, and stretchability as wearable strain and pressure sensors.

International journal of biological macromolecules·2026
Same journal

FES suppresses macrophage-mediated inflammation and atherosclerotic plaque formation by modulating the PU.1/Arg1 axis.

International journal of biological macromolecules·2026
Same journal

SETD5 regulates chronic stress-induced cancer stemness and M2 macrophage polarization via ferroptosis in non-small cell lung cancer.

International journal of biological macromolecules·2026
Same journal

NOX4 promotes cardiomyocyte ferroptosis-related injury in septic cardiomyopathy in associated with mitochondrial dysfunction and impaired NRF2 nuclear translocation.

International journal of biological macromolecules·2026
Same journal

Development of wound dressing using a novel multifunctional self-repairing Schiff-based N, O-carboxymethyl chitosan and oxidized tragacanth gum hydrogel: An in vitro study.

International journal of biological macromolecules·2026
See all related articles
  1. Home
  3. Enhancing Protein Structural Properties Through Model-guided Sequence Optimization.
  1. Home
  3. Enhancing Protein Structural Properties Through Model-guided Sequence Optimization.

Related Experiment Video

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

340

Enhancing protein structural properties through model-guided sequence optimization.

Young-Joon Ko1, Dohyeon Kim2, Charuvaka Muvva1

  • 1Center for Natural Product Systems Biology, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea.

International Journal of Biological Macromolecules
|August 25, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an iterative machine learning (ML) approach for protein engineering. This method efficiently optimizes protein properties like stability and binding, reducing experimental costs and accelerating discovery.

Keywords:
Glutamine-binding proteinMachine learningMulti-objective protein optimization

More Related Videos

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

17.1K
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

7.4K

Related Experiment 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

340
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

17.1K
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

7.4K

Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Optimizing proteins for multiple functions (stability, binding, expression) is complex and resource-intensive.
  • Traditional protein engineering methods face limitations due to structural complexity and high costs.
  • Machine learning (ML) offers a promising avenue for accelerating protein design and optimization.

Purpose of the Study:

  • To develop and validate an iterative ML-guided strategy for efficient protein engineering.
  • To enhance protein properties such as structural stability, ligand binding affinity, and shape complementarity.
  • To reduce the reliance on costly experimental characterization in protein design.

Main Methods:

  • An iterative ML-guided approach was employed to explore protein sequence space.
  • ML models predicted protein properties, guiding the selection of sequences for experimental validation.
  • A genetic algorithm, directed by ML models, identified optimal mutant sequences.
  • Experimental data from validated sequences were used to iteratively refine ML models.

    • Main Results:

    • The ML-guided approach successfully identified mutant sequences with improved performance compared to conventional methods.
    • Iterative model refinement led to enhanced predictive accuracy with each cycle.
    • Novel variants of glutamine binding protein (QBP) with superior stability and binding properties were discovered.
    • The method demonstrated efficient exploration of the sequence space for protein optimization.

    Conclusions:

    • The integration of ML and iterative optimization provides an efficient and scalable solution for protein engineering.
    • This approach accelerates the discovery of proteins with tailored functional properties.
    • The validated method holds significant potential for advancing various biotechnological applications.