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-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 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 Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Protein-Protein Interfaces

Protein-Protein Interfaces

Protein Complex Assembly

Protein Complex Assembly

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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...

You might also read

Related Articles

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

Sort by

Same author

Mandarin speech-based early detection of SCD: a feature-fusion residual network method.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Heterogeneous Network With Multiview Path Aggregation: Drug-Target Interaction Prediction Study Design.

JMIR medical informatics·2025

Same author

Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis.

JMIR AI·2025

Same author

SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction.

Journal of chemical information and modeling·2024

Same author

RDFNet: A Fast Caries Detection Method Incorporating Transformer Mechanism.

Computational and mathematical methods in medicine·2021

Same author

Enriching limited information on rare diseases from heterogeneous networks for drug repositioning.

BMC medical informatics and decision making·2021

Search research articles

Related Experiment Video

Updated: Sep 13, 2025

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

CPI-MIF: Compound-Protein Interaction Prediction with Multiview Information Fusion.

Yunuo Zhang¹, Bozhu Wen¹, Yaru Li¹

¹Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian University, Dalian 116622, China.

|July 29, 2025

Summary

A new model, CPI-MIF, improves compound-protein interaction (CPI) prediction by integrating compound structures and protein biological data. This approach enhances accuracy and stability in drug discovery research.

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: Sep 13, 2025

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:

Computational biology
Drug discovery
Bioinformatics

Background:

Compound-protein interaction (CPI) prediction is crucial for drug discovery.
Deep learning models are widely used for CPI prediction.
Existing methods often neglect protein roles and complex substructure interactions.

Purpose of the Study:

To propose a novel multiview information fusion model, CPI-MIF, for enhanced CPI prediction.
To address limitations in current models by incorporating both compound structural and protein biological information.
To capture micro-level (atom-amino acid) and macro-level (sequence-sequence) interaction details.

Main Methods:

Developed a multiview information fusion model (CPI-MIF).
Integrated compound structural features and protein biological information.
Employed a multiview interaction module for aggregating information from micro and macro views.
Focused on atom-level interactions and sequence-level relationships.

Main Results:

CPI-MIF demonstrated superior performance in CPI prediction compared to existing methods.
Achieved higher accuracy, Area Under the Curve (AUC), and Area Under the Precision-Recall Curve (AUPR).
Exhibited strong stability and performance on imbalanced datasets.

Conclusions:

CPI-MIF effectively integrates diverse data sources for accurate CPI prediction.
The model's multiview approach captures complex interaction patterns.
CPI-MIF represents a significant advancement in computational drug discovery.