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

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...

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...

You might also read

Related Articles

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

Sort by

Same author

Special Topics: Developments of Theoretical and Computational Chemistry Methods in Asia.

Journal of chemical theory and computation·2026

Same author

Anomaly detection and topology identification of distribution network based on conditional variational autoencoder.

Scientific reports·2026

Same author

MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics.

bioRxiv : the preprint server for biology·2026

Same author

Lacosamide versus brivaracetam as adjunctive therapy for drug-resistant focal epilepsy: A placebo-anchored indirect comparison and network meta-analysis.

Epilepsy research·2026

Same author

Fourier spatial attention guided diffusion model for optimizing exposure inconsistencies in endoscopic images.

Scientific reports·2026

Same author

Joint optimization of task offloading and energy trading in edge-enabled smart grids using deep reinforcement learning.

PloS one·2026

Search research articles

Related Experiment Video

Updated: Sep 6, 2025

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

Interrogating RNA-Small Molecule Interactions with Structure Probing and Artificial Intelligence-Augmented Molecular

Yihang Wang¹, Shaifaly Parmar², John S Schneekloth²

¹Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States.

ACS Central Science

|June 27, 2022

Summary

This summary is machine-generated.

Artificial intelligence-augmented molecular dynamics simulations reveal RNA-ligand recognition mechanisms. This approach accurately predicts binding affinities and mutation effects, advancing RNA-based therapeutics.

More Related Videos

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Published on: January 30, 2019

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

Related Experiment Videos

Last Updated: Sep 6, 2025

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Published on: January 30, 2019

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

Area of Science:

Molecular Biology
Computational Chemistry
Biophysics

Background:

Understanding RNA-ligand interactions is crucial for developing RNA-based therapeutics.
Significant challenges exist in studying RNA dynamics and ligand binding due to vast timescale differences.

Purpose of the Study:

To investigate RNA-ligand recognition using AI-augmented molecular dynamics simulations.
To compare simulation-derived flexibility profiles with experimental data (SHAPE-MaP).
To predict and validate mutation effects on ligand binding affinity.

Main Methods:

AI-augmented molecular dynamics simulations to observe ligand dissociation.
Comparison of simulation-based flexibility profiles with in vitro SHAPE-MaP experiments.
Prediction and validation of mutations affecting ligand binding affinity.

Main Results:

Simulations accurately reproduced known relative binding affinities for cognate and synthetic ligands.
Identified distinct flexibility utilization by different ligands within the riboswitch.
Successfully predicted distal mutations altering ligand binding affinities, complementing structural analysis.

Conclusions:

AI-augmented molecular dynamics simulations provide valuable insights into RNA-ligand recognition.
This methodology complements experimental techniques and elucidates complex molecular interactions.
The approach holds promise for advancing the design of RNA-targeted therapeutics.