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

Molecular Models

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.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Assembly of Cytoskeletal Filaments

Assembly of Cytoskeletal Filaments

Cytoskeletal filaments are polymeric forms of smaller protein subunits. However, individual cytoskeletal filaments may easily disassemble or associate with other similar filaments to form rigid structures. Microfilaments, made of actin monomers, rely on actin-binding proteins to form bundles and create networks of individual actin filaments. Microtubules rely on microtubule-associated proteins (MAPs) to form sturdy cylindrical structures. However, the proteins involved in forming complex...

Radical Chain-Growth Polymerization: Chain Branching

Radical Chain-Growth Polymerization: Chain Branching

The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

You might also read

Related Articles

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

Sort by

Same author

Self-Assembly of V-Shaped Polyaromatic Amphiphiles Studied by Molecular Dynamics Simulation.

The journal of physical chemistry. B·2025

Same journal

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026

Same journal

Graph-Based Generation and Reduction of Complex Chemical Reaction Networks.

Journal of chemical information and modeling·2026

Same journal

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.

Journal of chemical information and modeling·2026

Same journal

Machine Learning-Driven Discovery of Indole/Oxoindole-Piperazine Scaffolds as Dual MAO-B/Sig-1R Ligands for Neurodegenerative Disorders.

Journal of chemical information and modeling·2026

Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026

Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Algorithm for Efficient Superposition and Clustering of Molecular Assemblies Using the Branch-and-Bound Method.

Yuki Yamamoto¹

¹Department of Chemistry, Graduate School of Science, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.

Journal of Chemical Information and Modeling

|April 25, 2025

Summary

This summary is machine-generated.

We developed mobbRMSD, an exact algorithm for calculating root-mean-square deviation (RMSD) between chemical structures. This method efficiently handles large molecules and molecular dynamics, improving structural similarity analysis.

More Related Videos

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

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: May 13, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

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:

Computational chemistry
Structural bioinformatics
Cheminformatics

Background:

Root-mean-square deviation (RMSD) is crucial for comparing 3D chemical structures, impacting predictions of reactivity, properties, and bioactivity.
Determining atom mapping and spatial superposition for RMSD calculation is computationally challenging, especially for large systems.

Purpose of the Study:

To introduce mobbRMSD, a novel algorithm for exact RMSD calculation that addresses limitations of existing methods.
To enable efficient structural similarity analysis for complex chemical systems and molecular dynamics trajectories.

Main Methods:

Formulation in molecular-oriented coordinates and application of the branch-and-bound method for exact RMSD solutions.
Incorporation of chemical knowledge (atom types, bonding, chirality) for handling diverse chemical systems.
Development of a mobbRMSD-based structural clustering method for molecular dynamics data.

Main Results:

mobbRMSD extends the system size limit for exact RMSD solutions by nearly double compared to previous methods.
Successfully analyzed structural similarity in large molecular micelles, a task difficult for prior algorithms.
Achieved asymptotically average polynomial time complexity for structural clustering of molecular dynamics trajectories.

Conclusions:

mobbRMSD provides an efficient and exact solution for RMSD calculation, advancing structural similarity analysis in computational chemistry.
The algorithm enhances the study of molecular liquids, solute solvation, self-assembly, and molecular dynamics simulations.
mobbRMSD offers a significant improvement for analyzing large molecular systems and trajectory data.