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,...

You might also read

Related Articles

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

Sort by

Same author

Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.

Journal of computer-aided molecular design·2026

Same author

Permanent Scaffolding of Social Expertise.

Acta biotheoretica·2026

Same author

Shenfu Injection Inhibits Cardiac Fibroblast Activation and Improves Myocardial Fibrosis by Down-regulating M1 Macrophage-Derived Exosomal miR-155-5p.

Cardiovascular drugs and therapy·2026

Same author

An Explainable Deep Learning Model for Clathrin Protein Prediction Using a DCT-Enhanced Position-Specific Scoring Matrix.

Current drug targets·2026

Same author

AMP-CapsNet: a multi-view feature fusion approach for antimicrobial peptide prediction using capsule networks.

Genomics & informatics·2026

Same author

Surfactant-Layer Engineering Enables Growth Pathway Switching and Reversal of Chiroptical Activity in Helicoid Gold Nanocrystals.

Journal of the American Chemical Society·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 29, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

DPI_CDF: druggable protein identifier using cascade deep forest.

Muhammad Arif¹, Ge Fang^2,3, Ali Ghulam⁴

¹College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

BMC Bioinformatics

|April 5, 2024

Summary

This summary is machine-generated.

This study introduces DPI_CDF, a deep learning model for identifying druggable proteins (DPs) using only protein sequences. DPI_CDF significantly improves accuracy in drug discovery compared to existing computational methods.

Keywords:

Bioinformatics Cascade deep forest Druggable proteins PSSM Physicochemical features

More Related Videos

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Related Experiment Videos

Last Updated: Jun 29, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Computational biology
Drug discovery
Bioinformatics

Background:

Identifying drug targets is crucial for drug discovery.
Wet-lab methods for target characterization are costly and time-consuming.
Existing computational methods for predicting druggable proteins (DPs) lack satisfactory performance.

Purpose of the Study:

To develop a novel deep learning model for predicting DPs solely from protein sequences.
To improve the accuracy and efficiency of identifying potential drug targets.
To provide a valuable tool for accelerating the drug discovery process.

Main Methods:

Developed DPI_CDF, a deep learning model utilizing protein sequence information.
Integrated evolutionary, physiochemical, and compositional properties of protein sequences as features.
Employed a hierarchical deep forest model to fuse these diverse feature encoding schemes.

Main Results:

DPI_CDF achieved 99.13% accuracy and 0.982 MCC on the training dataset.
The model demonstrated strong generalization with 95.01% accuracy and 0.900 MCC on an independent test dataset.
DPI_CDF outperformed state-of-the-art methods, improving accuracy by over 4% on both training and testing datasets.

Conclusions:

DPI_CDF offers a highly accurate and efficient computational approach for identifying druggable proteins.
The model's performance has the potential to significantly expedite the drug discovery pipeline.
The developed model and associated code are publicly available to support the research community.