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Related Concept Videos

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.

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Modeling an Enzyme Active Site using Molecular Visualization Freeware
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CHARMM: the biomolecular simulation program.

B R Brooks1, C L Brooks, A D Mackerell

  • 1Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. brbrooks@helix.nih.gov

Journal of Computational Chemistry
|May 16, 2009
PubMed
Summary
This summary is machine-generated.

CHARMM (Chemistry at HARvard Molecular Mechanics) is a versatile molecular simulation program for biological molecules. It offers advanced tools for conformational analysis, free energy estimation, and dynamics, supporting diverse applications in computational chemistry.

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Area of Science:

  • Computational chemistry and molecular modeling.
  • Biophysics and structural biology.
  • Materials science and condensed matter physics.

Background:

  • CHARMM (Chemistry at HARvard Molecular Mechanics) is a long-standing, widely adopted molecular simulation program.
  • Developed over three decades, it primarily targets molecules of biological interest, including proteins, nucleic acids, and lipids.
  • Its versatility extends to various environments like solutions, crystals, and membranes.

Purpose of the Study:

  • To provide a comprehensive overview of the CHARMM program as it stands today.
  • To highlight key developments and advancements in CHARMM since its initial publication in 1983.
  • To showcase the program's extensive capabilities for molecular simulation and analysis.

Main Methods:

  • Utilizes a broad suite of computational tools for conformational and path sampling.
  • Incorporates methods for free energy estimation, molecular minimization, and dynamics.
  • Supports various energy functions and models, including QM/MM, all-atom classical, and implicit solvent models.

Main Results:

  • CHARMM offers extensive capabilities for studying biological molecules and broader many-particle systems.
  • The program supports diverse simulation approaches, from explicit solvent to implicit membrane models.
  • It is available on numerous platforms, supporting both serial and parallel computing architectures.

Conclusions:

  • CHARMM remains a powerful and adaptable tool for molecular simulation across various scientific disciplines.
  • Continuous development ensures its relevance for cutting-edge research in computational chemistry and biology.
  • Its broad applicability and extensive feature set make it invaluable for complex system analysis.