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

4.5K
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...
4.5K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.9K
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...
13.9K

You might also read

Related Articles

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

Sort by
Same author

Theoretical investigation of catalytic oxidation of benzyl alcohol by Au, Cu and Au-Cu nanoclusters.

Physical chemistry chemical physics : PCCP·2026
Same author

Mechanistic principles of antimicrobial peptides uncovered by charge density-based machine learning.

Chemical communications (Cambridge, England)·2026
Same author

Leveraging high-spin DFT features for prediction of spin state gaps in 3d transition metal complexes.

Physical chemistry chemical physics : PCCP·2025
Same author

Theoretical Investigation of the Stabilities and Reactivities of <math><semantics><mrow><msub><mi>Au</mi> <mi>m</mi></msub> <msub><mi>Cu</mi> <mi>n</mi></msub></mrow> <annotation>${\rm Au}_m{\rm Cu}_n$</annotation></semantics></math> Metallic Clusters (m+n = 13).

Chemistry, an Asian journal·2025
Same author

Author Correction: PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.

Scientific data·2024
Same author

Deep reinforcement learning in chemistry: A review.

Journal of computational chemistry·2024

Related Experiment Video

Updated: Sep 27, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

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

Published on: July 14, 2015

7.4K

BiRDS - Binding Residue Detection from Protein Sequences Using Deep ResNets.

Vineeth R Chelur1, U Deva Priyakumar1

  • 1Center for Computational Natural Sciences & Bioinformatics International Institute of Information Technology Hyderabad 500032, India.

Journal of Chemical Information and Modeling
|April 13, 2022
PubMed
Summary
This summary is machine-generated.

Predicting protein binding sites from amino acid sequences is crucial for drug discovery. A new deep learning model, BiRDS (Binding Residue Detection System), accurately identifies these sites using sequence data, accelerating therapeutic development.

More Related Videos

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

8.3K
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

2.0K

Related Experiment Videos

Last Updated: Sep 27, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

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

Published on: July 14, 2015

7.4K
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

8.3K
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

2.0K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Protein-drug interactions are vital for biological processes and therapeutics.
  • Predicting protein binding sites aids in discovering these interactions and designing optimized drugs.
  • Determining protein 3D structure is time-consuming and costly, while sequence determination is rapid.

Purpose of the Study:

  • To develop a method for accurately predicting protein binding sites using only amino acid sequence information.
  • To leverage Deep Learning for efficient and economical prediction of druggable protein sites.
  • To introduce a novel computational approach for accelerating drug discovery pipelines.

Main Methods:

  • Utilized a Residual Neural Network architecture named BiRDS (Binding Residue Detection System).
  • Trained the network on the SC-PDB database of annotated druggable binding sites.
  • Extracted features including Position-Specific Scoring Matrix, Secondary Structure, and Relative Solvent Accessibility from Multiple Sequence Alignments generated by DeepMSA.
  • Employed a weighted binary cross-entropy loss function to address class imbalance.

Main Results:

  • BiRDS achieved an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.87.
  • The model accurately predicted binding sites, with 25% of predicted sites' centers within 4 Å of the actual binding site centers.
  • A new benchmark dataset, SC6K, was introduced for evaluating binding-site prediction methods.

Conclusions:

  • Deep Learning, specifically the BiRDS model, offers a feasible and accurate approach for predicting protein binding sites from sequence data.
  • This sequence-based prediction method significantly reduces the time and cost associated with identifying potential drug targets.
  • BiRDS demonstrates potential for accelerating the drug discovery process by efficiently identifying key protein-drug interaction sites.