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 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

Protein Families

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism. Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members. If these new proteins contain similar amino acids in key...

Protein Families

Protein Families

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...

Conserved Binding Sites

Conserved Binding Sites

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...

You might also read

Related Articles

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

Sort by

Same author

Novel plasmid pCM3 harboring the <i>aph(3)</i> gene confers phosphorylation-driven streptomycin resistance in <i>Clavibacter michiganensis</i>.

mLife·2026

Same author

Double Parasitism by Two Cuckoo Gentes in a Daurian Redstart Nest.

Ecology and evolution·2026

Same author

Fabrication of Dimeric Prodrug Self-Assembly for Efficient Utilization of Aldehyde-Containing Plant Essential Oils in Management of Plant Disease.

ACS applied materials & interfaces·2026

Same author

Affective and Cognitive Distortions-Aided Suicide Risk Prediction for Long-Form Speech in Psychological Support Hotlines.

Bioengineering (Basel, Switzerland)·2026

Same author

Development and Preliminary Mechanistic Evaluation of a Novel Liposomal QS-21 and CpG ODNs Adjuvant System for Enhancing Vaccine Immunogenicity.

Vaccines·2026

Same author

Feature Transformation Network based on Correlation Distribution Graph for Disease Diagnosis.

IEEE journal of biomedical and health informatics·2026

Same journal

Integrative in silico analysis identifies functionally and regulatively relevant nsSNPs in the TRIB3 gene.

Computational biology and chemistry·2026

Same journal

MARS: Multi-anchor reasoning for reliable toxicity prediction under distribution shift.

Computational biology and chemistry·2026

Same journal

Zadeh-based fuzzy analysis of carreau tri-hybrid nanofluid hemodynamics in a straight artery with irregular triangular stenosis.

Computational biology and chemistry·2026

Same journal

Exploring C<sub>6</sub>N<sub>6</sub> as an effective drug delivery carrier for anticancer drugs mercaptopurine and thiotepa: A DFT and MD approach.

Computational biology and chemistry·2026

Same journal

Role of Artificial Intelligence in bioinformatics: Revolutionizing molecular docking and DNA tokenization.

Computational biology and chemistry·2026

Same journal

An interpretable framework for cancer drug response prediction using integrated drug and multi-omics data with a hybrid Bi-LSTM-GRU network.

Computational biology and chemistry·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 6, 2025

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

DeepAdd: Protein function prediction from k-mer embedding and additional features.

Zhihua Du¹, Yufeng He¹, Jianqiang Li¹

¹Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Guangdong Province, PR China.

Computational Biology and Chemistry

|October 4, 2020

Summary

This summary is machine-generated.

This study introduces a novel deep learning method for predicting protein function from sequence data. The approach leverages natural language processing and Gene Ontology (GO) class dependencies, showing improved performance in computational function annotation.

Keywords:

Convolution neural network Natural language process Protein function prediction Protein-protein interaction network Sequence similarity profile

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

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: Dec 6, 2025

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

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

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:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput sequencing generates vast protein data, making experimental functional analysis costly and time-consuming.
Computational methods are essential for predicting protein function, especially for non-model organisms.
Protein function prediction is a complex multi-label, multi-class problem due to proteins having diverse roles.

Purpose of the Study:

To develop an accurate and efficient computational method for predicting protein function directly from amino acid sequences.
To integrate sequence-derived features with Gene Ontology (GO) class relationships for improved prediction accuracy.
To benchmark the novel method against existing algorithms using established datasets and evaluation standards.

Main Methods:

Utilized natural language processing (NLP) models to generate word embeddings from protein sequences, capturing essential features.
Employed deep learning architectures to learn from sequence embeddings and additional positional features.
Incorporated dependencies between Gene Ontology (GO) classes (Molecular Function, Biological Process, Cellular Component) into the deep learning model.

Main Results:

The developed method demonstrated noticeable improvements over established algorithms like FFPred, DeepGO, and GoFDR.
Performance was evaluated using the rigorous standards set by the Computational Assessment of Function Annotation (CAFA) challenge, specifically on CAFA3 datasets.
The integration of NLP-derived features and GO class dependencies contributed to enhanced protein function prediction accuracy.

Conclusions:

The novel deep learning approach offers a powerful tool for accurate protein function prediction from sequence data.
This method addresses the limitations of experimental analyses and expands the scope of functional annotation in bioinformatics.
The approach shows significant potential for advancing our understanding of protein roles across various biological contexts.