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

Protein-protein Interfaces02:04

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 polypeptide...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Protein Networks02:26

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,...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conserved Binding Sites01:49

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 analyses the...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

You might also read

Related Articles

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

Sort by
Same author

Insulin receptor trafficking and interactions in muscle cells.

Journal of the Endocrine Society·2026
Same author

Predicting protein interfaces in the age of AlphaFold: Why dynamics and disorder remain a challenge.

Cell systems·2026
Same author

Next-generation predictors of protein phase behavior.

Current opinion in structural biology·2025
Same author

Challenging AlphaFold in predicting proteins with large-scale allosteric transitions.

Communications chemistry·2025
Same author

Genetic "expiry-date" circuits control lifespan of synthetic scavenger bacteria for safe bioremediation.

Nucleic acids research·2025
Same author

AR-V7 condensates drive androgen-independent transcription in castration resistant prostate cancer.

bioRxiv : the preprint server for biology·2025
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.

Nawar Malhis1, Jörg Gsponer2,3

  • 1Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. nmalhis@msl.ubc.ca.

BMC Bioinformatics
|June 13, 2026
PubMed
Summary
This summary is machine-generated.

MoRFchibi 2.0 accurately predicts Molecular Recognition Features (MoRFs) in intrinsically disordered protein regions (IDRs). This new tool surpasses existing methods, including those using AlphaFold and advanced language models, for identifying protein-binding sites.

Keywords:
Computational BiologyConvolutional Neural Network (CNN)Intrinsically disordered protein regions (IDRs)Machine learningMolecular recognition features (MoRFs)Reverse Bayes Rule

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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

Related Experiment Videos

Last Updated: Jun 16, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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

Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Molecular Recognition Features (MoRFs) are crucial segments in intrinsically disordered protein regions (IDRs) that transition to an ordered state upon partner binding.
  • Accurate identification of MoRFs is essential for understanding protein-protein interactions and cellular mechanisms but remains a significant computational challenge.

Purpose of the Study:

  • To introduce MoRFchibi 2.0, a novel computational tool for precise prediction of MoRF locations within protein sequences.
  • To evaluate the performance of MoRFchibi 2.0 against existing MoRF predictors and general protein-binding site predictors.

Main Methods:

  • Development of MoRFchibi 2.0 utilizing an ensemble of logistic regression convolutional neural network models.
  • Normalization of output scores based on training data priors for interpretability and compatibility.
  • Benchmarking against top models from Critical Assessment of protein Intrinsic Disorder (CAID) rounds 1-3, AlphaFold-based predictors, and state-of-the-art protein language models.

Main Results:

  • MoRFchibi 2.0 demonstrates superior performance over all evaluated existing MoRF and general protein-binding site predictors within IDRs.
  • The tool achieves higher success rates and improved ROC and Precision-Recall curves compared to leading methods, including those incorporating AlphaFold data and advanced language models.
  • The ensemble models are interpretable and compatible with existing scoring frameworks.

Conclusions:

  • MoRFchibi 2.0 represents a significant advancement in the computational identification of MoRFs within intrinsically disordered protein regions.
  • The tool's superior performance offers enhanced capabilities for researchers studying protein-protein interactions and the functional roles of disordered proteins.