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

Experimental RNAi

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...

You might also read

Related Articles

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

Sort by

Same author

Structure of a designed tetrahedral protein assembly variant engineered to have improved soluble expression.

Protein science : a publication of the Protein Society·2015

Same author

Unique double-ring structure of the peroxisomal Pex1/Pex6 ATPase complex revealed by cryo-electron microscopy.

Proceedings of the National Academy of Sciences of the United States of America·2015

Same author

Mechanistic Analysis of an Engineered Enzyme that Catalyzes the Formose Reaction.

Chembiochem : a European journal of chemical biology·2015

Same author

Design of ordered two-dimensional arrays mediated by noncovalent protein-protein interfaces.

Science (New York, N.Y.)·2015

Same author

Designing Two-Dimensional Protein Arrays through Fusion of Multimers and Interface Mutations.

Nano letters·2015

Same author

Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4.

The Journal of biological chemistry·2015

Same journal

Engineered HSP90-MP65 Bivalent Fusion Antigen: A Novel Vaccine Candidate Against Invasive Candidiasis.

Proteins·2026

Same journal

Physics-Based Energy Functions for Computational Protein Design.

Proteins·2026

Same journal

Impact of Stabilizing Osmolytes on the Conformational Dynamics of Human and Rat Islet Amyloid Polypeptides.

Proteins·2026

Same journal

Stabilization of Bone Morphogenetic Protein-2 at Physiological pH: Contrasting Roles of CHAPS and Arginine in Aggregation Inhibition.

Proteins·2026

Same journal

Structural Insights Into the Function of Leishmania major Adenylosuccinate Lyase.

Proteins·2026

Same journal

Generalizing the Gaussian Network Model: Spanning-Tree Thermodynamics Shows Entropy-Driven KRAS Activation.

Proteins·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

Substrate Generation for Endonucleases of CRISPR/Cas Systems

Substrate Generation for Endonucleases of CRISPR/Cas Systems

Published on: September 8, 2012

CASP11 refinement experiments with ROSETTA.

Hahnbeom Park^1,2, Frank DiMaio^1,2, David Baker^3,4,5

¹Department of Biochemistry, University of Washington, Seattle, Washington, 98195.

|July 25, 2015

Summary

This summary is machine-generated.

New Rosetta-based methods for protein structure refinement were tested in CASP11. A high-resolution protocol consistently improved structures, while a low-resolution approach requires further development due to model selection issues.

Keywords:

Monte Carlo simulation protein homology modeling protein loop modeling structure prediction structure refinement

More Related Videos

Using Sniper-Cas9 to Minimize Off-target Effects of CRISPR-Cas9 Without the Loss of On-target Activity Via Directed Evolution

Using Sniper-Cas9 to Minimize Off-target Effects of CRISPR-Cas9 Without the Loss of On-target Activity Via Directed Evolution

Published on: February 26, 2019

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

Related Experiment Videos

Last Updated: Jun 29, 2026

Substrate Generation for Endonucleases of CRISPR/Cas Systems

Substrate Generation for Endonucleases of CRISPR/Cas Systems

Published on: September 8, 2012

Using Sniper-Cas9 to Minimize Off-target Effects of CRISPR-Cas9 Without the Loss of On-target Activity Via Directed Evolution

Using Sniper-Cas9 to Minimize Off-target Effects of CRISPR-Cas9 Without the Loss of On-target Activity Via Directed Evolution

Published on: February 26, 2019

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

Area of Science:

Computational biology
Structural bioinformatics
Protein structure prediction

Background:

Protein structure refinement is crucial for accurate biological function prediction.
Existing methods face challenges in handling diverse starting model qualities.
The Critical Assessment of protein Structure Prediction (CASP) experiment provides a benchmark for evaluating new methods.

Purpose of the Study:

To develop and test novel Rosetta-based computational approaches for protein structure refinement.
To evaluate the performance of different refinement strategies based on initial model quality.
To identify strengths and weaknesses of automated refinement protocols in a large-scale experiment.

Main Methods:

Development of automated refinement protocols integrating sampling, parallel computation, and multiobjective optimization.
Application of two distinct strategies in CASP11: aggressive large-scale rebuilding for poor models and local rebuilding with core refinement for better models.
Adaptive refinement based on the predicted accuracy of starting structures.

Main Results:

The high-resolution refinement strategy (local rebuilding + core refinement) consistently improved initial protein models.
The low-resolution strategy (large-scale rebuilding) showed variable success, with frequent worsening of models due to selection issues.
Performance varied significantly with the quality and predicted accuracy of the starting structures.

Conclusions:

The high-resolution refinement protocol shows promise as a complementary method for protein structure refinement.
The low-resolution refinement strategy requires substantial further development to overcome model selection challenges.
Automated refinement protocols can be tailored based on initial model quality for improved performance.