Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Organization

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.

Protein Organization

Protein Organization

Protein Organization

Protein Organization

Protein Organization

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.

Protein Folding

Protein Folding

Protein Folding

Protein Folding

You might also read

Related Articles

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

Sort by

Same author

Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness.

Healthcare (Basel, Switzerland)·2026

Same author

GeoFusion-3D: Multi-Scale Geomorphic Feature Fusion for Landslide Scar Detection Using UAV-Mounted LiDAR.

Sensors (Basel, Switzerland)·2026

Same author

Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.

Frontiers in public health·2025

Same author

A Cross-Sectional Social Network Analysis of Decision-Making About Recruiting a Living Donor for Kidney Transplantation.

Kidney medicine·2025

Same author

Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.

Child psychiatry and human development·2025

Same author

Leveraging multi-modal data for early prediction of severity in forced transmission outages with hierarchical spatiotemporal multiplex networks.

PloS one·2025

Same journal

DiffGRN: differential gene regulatory network analysis.

International journal of data mining and bioinformatics·2019

Same journal

Integration of multi-omics data for integrative gene regulatory network inference.

International journal of data mining and bioinformatics·2018

Same journal

The development of non-coding RNA ontology.

International journal of data mining and bioinformatics·2016

Same journal

Learning multiple distributed prototypes of semantic categories for named entity recognition.

International journal of data mining and bioinformatics·2015

Same journal

Weighted fusion regularisation and predicting microbial interactions with vector autoregressive model.

International journal of data mining and bioinformatics·2015

Same journal

Application of consensus string matching in the diagnosis of allelic heterogeneity involving transposition mutation.

International journal of data mining and bioinformatics·2015

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 6, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Exploring alternative knowledge representations for protein secondary-structure prediction.

Uros Midic¹, A Keith Dunker, Zoran Obradovic

¹Center for Information Science and Technology, Temple University, 1805 N. Broad St., 303 Wachman Hall, Philadelphia, PA 19129, USA. uros@ist.temple.edu

International Journal of Data Mining and Bioinformatics

|April 11, 2008

Summary

This summary is machine-generated.

This study enhances protein secondary structure prediction accuracy, particularly for beta-sheet residues. New methods reduce input data while improving prediction performance.

More Related Videos

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

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

Published on: July 25, 2013

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Related Experiment Videos

Last Updated: Jul 6, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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

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

Published on: July 25, 2013

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Computational biology
Bioinformatics
Protein structure prediction

Background:

Current 3-class protein secondary structure prediction methods approach maximal accuracy.
Prediction accuracy for beta-sheet residues remains a significant challenge compared to other classes.

Purpose of the Study:

To analyze the relevance of input attributes in sequence-profile based secondary structure prediction.
To develop improved prediction methods by reducing input data and incorporating novel features.

Main Methods:

Analyzed the relevance of 315 input attributes for a standard prediction framework.
Developed two novel knowledge representations using smaller, optimized attribute sets.
Investigated using predictions of connected beta-sheet residue pairs and residue contact maps.

Main Results:

Identified key input attributes for secondary structure prediction.
Proposed reduced attribute sets that maintain or improve prediction accuracy.
Demonstrated potential accuracy gains by integrating contact map and beta-sheet pair predictions.

Conclusions:

Optimized attribute selection can improve protein secondary structure prediction efficiency.
Integrating residue contact information offers a promising avenue for enhancing beta-sheet prediction accuracy.
Further research into novel feature integration can advance predictive modeling in bioinformatics.