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

5.2K
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...
5.2K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.9K
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...
14.9K
Ligand Binding Sites02:40

Ligand Binding Sites

15.4K
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...
15.4K

You might also read

Related Articles

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

Sort by
Same author

IFNg_DeepKG: A Novel Model for Identifying Interferon-Gamma-Inducing Epitopes Using Knowledge Graph RAG in Biomedical Applications.

Journal of chemical information and modeling·2025
Same author

CFTR_TL: Transfer Learning-Enhanced Prediction of CFTR ATP Binding Sites with Multi-Window Convolutional Neural Networks.

ACS omega·2025
Same author

Optimizing blood pressure control in multimorbid hypertensive patients: insights from a real-world taiwanese cohort.

Journal of hypertension·2025
Same author

mCNN-GenEfflux: enhanced predicting Efflux protein and their super families by using generative proteins combined with multiple windows convolution neural networks.

Computational biology and chemistry·2025
Same author

RTK_RAG: Leveraging Retrieval Augmented Generation with Multi-Window Convolutional Neural Networks for Superior ATP Binding Site Prediction in Receptor Tyrosine Kinases.

Journal of chemical information and modeling·2025
Same author

DeepCR: predicting cytokine receptor proteins through pretrained language models and deep learning networks.

Journal of biomolecular structure & dynamics·2025

Related Experiment Video

Updated: Mar 2, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

2.6K

msCNN-PLM-FAD: Enhanced predicting FAD binding sites by using protein language representation combined with multiple

Dinh-Quy Nguyen1, Viet-Thanh Nguyen1, Cam-Hong Ly2

  • 1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; College of Information & Communication Technology, Can Tho University, Viet Nam.

Analytical Biochemistry
|February 28, 2026
PubMed
Summary

This study introduces msCNN-PLM-FAD, a novel computational model for predicting FAD binding sites in proteins. The model enhances disease drug development by improving prediction accuracy for FAD-related conditions.

Keywords:
FAD binding sites predictionFlavin adenine dinucleotideMultiple separable windows convolutional neural networksProtein language models

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

70.0K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.8K

Related Experiment Videos

Last Updated: Mar 2, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

2.6K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

70.0K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.8K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Genomics

Background:

  • FAD binding site classification is critical for understanding flavoprotein-associated diseases.
  • Existing methods for FAD binding site prediction have limitations, necessitating improved approaches.

Purpose of the Study:

  • To develop an accurate computational model for predicting FAD binding sites in electron transport proteins.
  • To leverage pretrained protein language models (PLMs) and convolutional neural networks (CNNs) for enhanced prediction.

Main Methods:

  • Utilized a sliding window feature extraction technique combined with embeddings from the ESM PLM.
  • Developed the msCNN-PLM-FAD model integrating CNN-based window scanning with PLM embeddings.
  • Trained and validated the model on a dataset of 12,850 samples.

Main Results:

  • The msCNN-PLM-FAD model achieved high performance, with an AUC of 0.9614 and MCC of 0.7491.
  • Demonstrated superior performance in class-balance metrics, specificity (0.9836), and accuracy (0.9795), while maintaining sensitivity (0.8704).
  • Outperformed previous FAD binding site prediction studies.

Conclusions:

  • The msCNN-PLM-FAD model significantly improves the accuracy of FAD binding site prediction.
  • This advancement can accelerate the development of drugs for FAD deficiency-related diseases.
  • Potential for targeted therapies against bacterial FAD synthesis without impacting human health.