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

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...

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...

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

You might also read

Related Articles

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

Sort by

Same author

Exploring feature limitations in antimicrobial resistance prediction: machine learning and deep learning in A. baumannii.

Scientific reports·2026

Same author

Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.

Iranian journal of medical sciences·2026

Same author

CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.

Bioinformatics advances·2026

Same author

MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.

Briefings in bioinformatics·2026

Same author

DeepDRP: Dose-response predictions of drug pairs using deep learning based on data-driven feature representation and dose-response curve characteristics.

PloS one·2026

Same author

ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Nov 4, 2025

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

Moonlighting protein prediction using physico-chemical and evolutional properties via machine learning methods.

Farshid Shirafkan¹, Sajjad Gharaghani², Karim Rahimian³

¹Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

BMC Bioinformatics

|May 25, 2021

Summary

This study introduces a computational model to identify moonlighting proteins (MPs) and potential outliers. The model effectively predicts MPs, revealing that some non-MPs may also exhibit moonlighting functions.

Keywords:

Moonlighting protein Multitasking proteins Outlier PSSM Physico-chemical properties Random forest SVM bioinformatics

More Related Videos

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

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

Published on: February 23, 2024

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: Nov 4, 2025

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

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

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

Published on: February 23, 2024

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:

* Biochemistry and Molecular Biology
* Computational Biology and Bioinformatics

Background:

* Moonlighting proteins (MPs) possess multiple distinct functions within a single polypeptide chain.
* Studying MPs is crucial for understanding cellular processes, protein evolution, and disease mechanisms.
* Experimental identification of MPs is challenging, necessitating computational prediction methods.

Purpose of the Study:

* To develop a computational model for accurately detecting moonlighting proteins (MPs) and non-MPs.
* To identify potential outlier proteins within protein datasets.
* To assess the performance of various classification methods and feature vectors for MP prediction.

Main Methods:

* Utilized 37 distinct feature vectors extracted from protein sequences.
* Employed 8 different classification methods, including Support Vector Machines (SVM).
* Implemented a rigorous tenfold cross-validation process repeated 100 times to identify outlier proteins based on misclassification frequency.

Main Results:

* The SVM method demonstrated the highest performance in predicting moonlighting proteins across all assessed feature vectors.
* Identified 57 potential outlier proteins from a dataset of 351 samples (215 MPs, 136 non-MPs).
* Discovered that some non-MPs, like P69797, were misclassified across multiple methods, suggesting potential moonlighting activity.

Conclusions:

* The developed computational approach effectively identifies novel moonlighting proteins.
* The study highlights the difficulty of experimental MP identification and the utility of computational tools.
* Findings suggest that several proteins currently classified as non-MPs may exhibit moonlighting functions.