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

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...

Protein Networks

Protein Networks

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 Complex Assembly

Protein Complex Assembly

You might also read

Related Articles

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

Sort by

Same author

A unified multimodal model for generalizable zero-shot and supervised protein function prediction.

Bioinformatics (Oxford, England)·2026

Same author

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Briefings in bioinformatics·2026

Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026

Same author

Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction.

NAR genomics and bioinformatics·2026

Same author

LAFA: A Framework for Reproducible Longitudinal Assessment of Protein Function Annotation Models.

ArXiv·2026

Same author

Improving AlphaFold3 by Engineering MSA and Template Inputs.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Feb 17, 2026

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

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

Badri Adhikari¹, Jie Hou¹, Jianlin Cheng^1,2

¹Department of Mathematics and Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA.

Bioinformatics (Oxford, England)

|December 12, 2017

Summary

This summary is machine-generated.

DNCON2 improves protein contact map prediction using a novel deep learning architecture. This method enhances accuracy in predicting residue-residue contacts, crucial for protein structure prediction.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

Related Experiment Videos

Last Updated: Feb 17, 2026

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

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

Area of Science:

Computational Biology
Bioinformatics
Structural Biology

Background:

Protein contact map prediction is vital for ab initio protein structure prediction.
Recent advancements utilize coevolution and machine learning methods.
Reliable contact map prediction is essential for further progress in the field.

Purpose of the Study:

Introduce DNCON2, an improved protein contact map predictor.
Enhance the accuracy of predicting residue-residue contacts.
Improve ab initio protein structure prediction.

Main Methods:

Developed DNCON2, a two-level deep convolutional neural network predictor.
Employed six convolutional neural networks with varying distance thresholds.
Integrated predictions from initial networks as features for the final prediction.

Main Results:

DNCON2 achieved higher mean precisions on CASP10, 11, and 12 free-modeling datasets compared to existing methods.
Demonstrated superior performance in predicting long-range contacts.
Performance attributed to novel deep learning architecture and training strategies.

Conclusions:

DNCON2 represents a significant improvement in protein contact map prediction.
The method's architecture and training approach contribute to its enhanced accuracy.
DNCON2 is a valuable tool for advancing ab initio protein structure prediction.