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

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Introduction to Enzymes01:22

Introduction to Enzymes

The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that bind the substrates and convert them into products. Many enzymes also...
Introduction To Enzymes01:22

Introduction To Enzymes

The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that bind the substrates and convert them into products. Many enzymes also...

You might also read

Related Articles

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

Sort by
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

A PLUM Job: Peptide modeLs for Understanding and engineering antiMicrobial therapeutics.

bioRxiv : the preprint server for biology·2026
Same author

Advances in Protein Function Prediction from the Fifth CAFA Challenge.

bioRxiv : the preprint server for biology·2026
Same author

EpicTope: predicting and validating non-disruptive epitope tagging sites.

Development (Cambridge, England)·2026
Same author

Limitations of current machine learning models in predicting enzymatic functions for uncharacterized proteins.

G3 (Bethesda, Md.)·2025
Same author

A longitudinal analysis of function annotations of the human proteome reveals consistently high biases.

Database : the journal of biological databases and curation·2025
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: May 14, 2026

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

How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning.

Xiang Ma1, Parnal Joshi2, Iddo Friedberg2

  • 1Computer Science, Iowa State University, 50014, IA, USA.

Biorxiv : the Preprint Server for Biology
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Enzyme function prediction from sequence is challenging. EnzPlacer uses contrastive learning to accurately place novel enzyme sequences into known functional contexts, aiding experimental characterization.

Keywords:
Contrastive learningEnzymesProtein function prediction

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Related Experiment Videos

Last Updated: May 14, 2026

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

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

  • Bioinformatics
  • Enzymology
  • Computational Biology

Background:

  • Predicting enzyme function from amino acid sequence remains a significant challenge in life sciences.
  • The rapid growth of genomic data yields numerous uncharacterized enzyme sequences.
  • Accurately contextualizing these sequences within known functional space is crucial for hypothesis generation.

Purpose of the Study:

  • To develop a novel computational method for predicting enzyme function from sequence.
  • To accurately place uncharacterized enzyme sequences into a narrowed functional context.
  • To aid experimentalists in generating falsifiable hypotheses for enzyme characterization.

Main Methods:

  • A contrastive learning algorithm named EnzPlacer was developed.
  • The algorithm predicts the top three Enzyme Commission (EC) numbers (first, second, and third) for a given protein sequence.
  • This approach is effective even when the most specific EC number (fourth) is unknown or not in the training data.

Main Results:

  • EnzPlacer accurately predicts the functional context of enzyme sequences.
  • The method successfully places proteins into functional families, even without complete functional annotation.
  • This provides a valuable tool for prioritizing experimental characterization efforts.

Conclusions:

  • EnzPlacer offers a robust solution for predicting enzyme function from sequence data.
  • The algorithm enhances the ability to place novel sequences within the known enzyme function landscape.
  • This facilitates more targeted and efficient biochemical characterization of enzymes.