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

You might also read

Related Articles

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

Sort by
Same author

ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2.

bioRxiv : the preprint server for biology·2026
Same author

Predictions from deep learning propose substantial protein-carbohydrate interplay.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.

Journal of chemical information and modeling·2026
Same author

Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties.

bioRxiv : the preprint server for biology·2026
Same author

Can We Extract Physics-like Energies from Generative Protein Diffusion Models?

bioRxiv : the preprint server for biology·2025
Same author

Adapting Co-Folding Models for Structure-Based Protein-Protein Docking Through Flow Matching.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Jun 20, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta.

Rituparna Samanta1,2, Ameya Harmalkar1,3, Priyamvada Prathima1,4

  • 1Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.

Biorxiv : the Preprint Server for Biology
|July 19, 2024
PubMed
Summary

Rosetta-MPDock accurately predicts membrane protein complex structures, even for flexible proteins. This computational method improves upon existing techniques for modeling protein interactions within cell membranes.

Keywords:
backbone flexibilityenergy functionstransmembrane protein docking

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

Published on: February 23, 2024

2.5K

Related Experiment Videos

Last Updated: Jun 20, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

Published on: February 23, 2024

2.5K

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Membrane proteins (MPs) are crucial for cellular functions, including signal transduction and immune response.
  • MPs are key targets for pharmaceutical drugs, but their structural determination is experimentally challenging.
  • Computational docking offers a viable approach to model MP complex structures.

Purpose of the Study:

  • To present Rosetta-MPDock, a novel computational protocol for flexible transmembrane (TM) protein docking.
  • To assess the performance of Rosetta-MPDock in predicting TM-protein complex structures, accounting for conformational changes.

Main Methods:

  • Rosetta-MPDock employs flexible monomer sampling and docking within an implicit membrane environment.
  • The method was benchmarked on 29 TM-protein complexes with varying backbone flexibility (rigid, moderately flexible, flexible).
  • Integration with AlphaFold2-multimer was explored to enhance structure prediction and refinement.

Main Results:

  • Rosetta-MPDock achieved a 67% success rate for moderately flexible and 60% for highly flexible TM-protein targets in local docking scenarios.
  • These success rates represent a significant improvement over existing membrane protein docking methods.
  • Combining AlphaFold2-multimer with Rosetta-MPDock further boosted success rates from 64% to 73%.

Conclusions:

  • Rosetta-MPDock provides an advanced computational tool for predicting membrane protein complex structures.
  • This method facilitates the study of key biological questions and functional mechanisms involving membrane proteins.
  • The benchmark dataset and code are publicly available to support further research.