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 Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Protein-Protein Interfaces

Protein-Protein Interfaces

Intrinsically Disordered Proteins

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

Intrinsically Disordered Proteins

Intrinsically Disordered Proteins

You might also read

Related Articles

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

Sort by

Same author

NAT10 drives ovarian cancer progression via NLRP3 mRNA ac4C modification.

Discover oncology·2026

Same author

Controllable fabrication of bioinspired pollen-like microparticles via solvent evaporation of polyvinyl alcohol (PVA)-stabilized microfluidic polymer microdroplets.

Journal of colloid and interface science·2026

Same author

DeepHEM: A novel deep domain-adversarial learning framework for identifying human essential miRNAs.

Molecular therapy : the journal of the American Society of Gene Therapy·2025

Same author

Electroacupuncture-induced reduction of myocardial ischemia-reperfusion injury via FTO-dependent m6A methylation modulation.

Open medicine (Warsaw, Poland)·2025

Same author

A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships.

BMC medical informatics and decision making·2025

Same author

Characterization of core microbiota of barley seeds from different continents for origin tracing and quarantine pathogen assessment.

Food microbiology·2024

Search research articles

Related Experiment Video

Updated: Jan 1, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Computational Models for Self-Interacting Proteins Prediction.

Jia Qu¹, Yan Zhao¹, Li Zhang¹

¹School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.

Protein and Peptide Letters

|December 28, 2019

Summary

This summary is machine-generated.

Self-interacting proteins (SIPs) are crucial for cellular functions. This review highlights computational models for identifying SIPs, offering a cost-effective alternative to experimental methods.

Keywords:

Self-interacting proteins biomedical research cellular functions computational models machine learning protein interaction.

More Related Videos

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

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: Jan 1, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

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

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:

Biochemistry and Molecular Biology
Bioinformatics
Systems Biology

Background:

Self-interacting proteins (SIPs) play vital roles in cellular processes and the evolution of protein interaction networks (PINs).
Understanding protein self-interaction is key to elucidating protein function and mechanisms.
Existing research has primarily focused on SIP structures and functions, with less emphasis on their comprehensive properties.

Purpose of the Study:

To provide a detailed review of the concept and functions of SIPs.
To introduce available SIP datasets and computational models for their prediction.
To discuss challenges and future directions in developing computational models for SIP identification.

Main Methods:

Review of existing literature on Self-Interacting Proteins (SIPs).
Introduction to data resources for SIPs.
Overview of computational models, particularly machine learning-based approaches with feature extraction techniques, for SIP prediction.

Main Results:

SIPs are integral to cellular functions and PIN evolution.
Computational models, especially machine learning methods, offer efficient alternatives to experimental SIP identification.
Various feature extraction strategies are employed in existing computational models.

Conclusions:

Accurate identification of SIPs is critical for biomedical research.
Machine learning-based computational models are promising for high-throughput SIP prediction.
Further research is needed to address the challenges in developing robust computational models for SIP identification.