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-Protein Interfaces

Protein-Protein Interfaces

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

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

You might also read

Related Articles

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

Sort by

Same author

Highly Efficient Isolation and Rapid Detection of Pancreatic Cancer-Derived Small Extracellular Vesicles by Specific Aggregation from Aptamer-Functionalized DNA Long Chains.

Analytical chemistry·2026

Same author

Screening of immune adjuvants for an attenuated live vaccine derived from the Aeromonas veronii TH0426 strain with a hisJ gene deletion and evaluation of its immune protection in crucian carp.

Fish & shellfish immunology·2026

Same author

Constant-Potential Machine Learning Force Field for the Electrochemical Interface.

Journal of chemical theory and computation·2025

Same author

Combination of Slightly Acidic Electrolyzed Water and Hydrogel to Enhance Stability, Increase Antibacterial Efficacy, and Promote Infectious Wound Healing.

International journal of molecular sciences·2025

Same author

A Tyndall Effect-Based Visible Aptasensing Platform Enables Sensitive and Isolation-Free Profiling of Small Extracellular Vesicle Surface Proteins.

Nano letters·2025

Same author

Distinct trajectory patterns of neutrophil-to-albumin ratio predict clinical outcomes after endovascular therapy in large vessel occlusion stroke.

Frontiers in aging neuroscience·2025

Search research articles

Related Experiment Video

Updated: Nov 9, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction

Yang Wang¹, Zhanchao Li², Yanfei Zhang¹

¹School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.

BMC Bioinformatics

|April 13, 2021

Summary

We developed a new protein sequence encoding method, Sequence-Statistics-Content (SSC), to improve deep learning models for predicting protein-protein interactions (PPIs). This method enhances prediction accuracy and offers a valuable tool for proteomics research.

More Related Videos

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

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Related Experiment Videos

Last Updated: Nov 9, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

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

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

Bioinformatics
Computational Biology
Proteomics

Background:

Protein-protein interactions (PPIs) are crucial for cellular functions.
Experimental determination of PPIs is costly and time-consuming.
Bioinformatics approaches are needed to predict PPIs efficiently.

Purpose of the Study:

To develop an improved protein sequence encoding method for deep learning models.
To enhance the accuracy of predicting protein-protein interactions.
To provide a more efficient bioinformatics tool for proteomics research.

Main Methods:

Proposed the Sequence-Statistics-Content (SSC) protein sequence encoding format.
Encoded protein sequences into a three-channel format using statistical and bigram information.
Utilized a 2D convolutional neural network (2D CNN) with the SSC encoding for PPI prediction.

Main Results:

The SSC encoding method significantly improved the performance of the 2D CNN model compared to 1D CNN with one-hot encoding.
Independent validation using the HIPPIE database confirmed the effectiveness of the SSC method.
Molecular docking further validated the accuracy of the predicted protein-protein interactions.

Conclusions:

The SSC encoding method enhances the capability of CNN models for protein sequence-related tasks.
This method shows potential for improving other machine learning and deep learning approaches.
The SSC encoding method offers a valuable tool for analyzing protein sequences and predicting PPIs.