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

Conserved Binding Sites01:49

Conserved Binding Sites

5.1K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
5.1K
Conserved Binding Sites01:49

Conserved Binding Sites

1.9K
1.9K
Ligand Binding Sites02:40

Ligand Binding Sites

14.9K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
14.9K
Ligand Binding Sites02:40

Ligand Binding Sites

8.7K
8.7K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

489
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
489
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

5.5K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Computational identification of human ubiquitination sites using convolutional and recurrent neural networks.

Molecular omics·2021
Same author

DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization.

Journal of molecular modeling·2020
Same author

Cyclophilin A (CyPA) induces chemotaxis independent of its peptidylprolyl cis-trans isomerase activity: direct binding between CyPA and the ectodomain of CD147.

The Journal of biological chemistry·2011
Same author

Characterization of dissolved organic matter in urban sewage using excitation emission matrix fluorescence spectroscopy and parallel factor analysis.

Journal of environmental sciences (China)·2011
Same author

Actin filament associated protein mediates c-Src related SRE/AP-1 transcriptional activation.

FEBS letters·2011
Same author

Sesquiterpenes from Vladimiria souliei and their inhibitory effects on NO production.

Fitoterapia·2011
Same journal

Metabolite-centric identification of antimetabolite drug targets across cancer and neurodegenerative diseases.

Molecular omics·2026
Same journal

Platelet proteomics on less than a drop of previously frozen, non-citrate plasma.

Molecular omics·2026
Same journal

Decoding the diabetic transition: a lipidomic approach for biomarker discovery in an Indian cohort.

Molecular omics·2026
Same journal

Placental metabolomics for assessment of pregnancy complications: a systematic review.

Molecular omics·2026
Same journal

Prostate cancer: metabolic remodelling in expressed prostatic secretions reveals cellular structural changes measured by mpMRI.

Molecular omics·2026
Same journal

A practical guide to experimental design and power analysis for metaproteomics studies.

Molecular omics·2026
See all related articles

Related Experiment Video

Updated: Jan 25, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

7.1K

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods.

Renxiang Yan1, Xiaofeng Wang2, Yarong Tian3

  • 1School of Biological Sciences and Engineering, Fuzhou University, Fuzhou 350002, China. yanrenxiang@fzu.edu.cn ljuan@fzu.edu.cn and Fujian Key Laboratory of Marine Enzyme Engineering, Fuzhou 350002, China.

Molecular Omics
|May 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method for predicting zinc-binding sites in proteins. The developed machine learning models offer a cost-effective and efficient alternative to experimental methods for identifying these crucial protein modifications.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Related Experiment Videos

Last Updated: Jan 25, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

7.1K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Area of Science:

  • Biochemistry
  • Structural Biology
  • Bioinformatics

Background:

  • Zinc (Zn2+) ions are essential cofactors in many biological processes, and their binding sites are critical post-translational modifications in proteins.
  • Understanding zinc-binding sites aids in elucidating protein folding and function, but experimental determination is costly and labor-intensive.
  • The post-genomic era necessitates efficient computational tools for identifying zinc-binding sites.

Purpose of the Study:

  • To develop a novel, accurate, and efficient computational method for predicting zinc-binding sites in proteins.
  • To create a user-friendly tool that complements experimental approaches in structural biology and proteomics.

Main Methods:

  • Compiled and prepared non-redundant datasets of zinc-binding proteins from the Protein Data Bank.
  • Extracted effective and complementary features from protein sequences and 3D structures.
  • Developed and intensively trained multiple machine learning models for zinc-binding site prediction.
  • Benchmarked the prediction performance against diverse zinc-binding sites and state-of-the-art in silico methods.

Main Results:

  • The developed method demonstrates high competitiveness compared to existing computational tools.
  • The approach effectively identifies potential zinc-binding sites, aiding in protein function prediction.
  • The prediction tool shows significant potential for high-throughput screening across proteomes.

Conclusions:

  • The novel computational method provides a valuable and cost-effective tool for identifying zinc-binding sites.
  • This method can serve as a complementary approach to experimental techniques in biological research.
  • Publicly available web server and standalone programs facilitate widespread adoption and research in the scientific community.