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| CHARMM (program) | |
|---|---|
| Name | CHARMM |
| Developer | Martin Karplus, Harvey A. Scheraga, Norman Davidson et al. |
| Initial release | 1983 |
| Programming language | Fortran, C |
| Operating system | Unix, Linux, macOS, Microsoft Windows |
| Genre | Molecular dynamics, Molecular modeling |
| License | Academic, Commercial |
CHARMM (program) is a widely used molecular simulation software package for biomolecular modeling, computational chemistry, and molecular dynamics. It originated from collaborations among laboratories at Harvard University, Brandeis University, and Columbia University and has been developed in the contexts of research at institutions like Boston University and Weizmann Institute of Science. The program serves communities working on problems related to protein folding, enzyme catalysis, drug design, membrane biophysics, and materials science.
CHARMM's origins trace to research groups led by Martin Karplus, Harvey A. Scheraga, and collaborators in the late 1960s and 1970s building on earlier work at Harvard University and Brandeis University. Early advances paralleled conceptual developments from Linus Pauling-era structural chemistry and computational initiatives at Argonne National Laboratory and Los Alamos National Laboratory. The code grew through contributions from researchers affiliated with Columbia University, University of California, San Diego, Rutgers University, and University of North Carolina at Chapel Hill, integrating methods from groups led by figures such as Michael Levitt and Arieh Warshel. CHARMM development incorporated ideas from algorithmic innovations at Brookhaven National Laboratory and borrowing techniques from software like projects at D.E. Shaw Research and academic collaborations with National Institutes of Health. Over decades CHARMM evolved alongside milestones like the Protein Data Bank establishment and the awarding of the Nobel Prize in Chemistry to pioneers in computational structural biology.
CHARMM provides tools for all-atom and coarse-grained molecular dynamics simulations, energy minimization, normal mode analysis, free energy calculations, and quantum mechanics/molecular mechanics coupling. Its modules address tasks common to practitioners from University of Cambridge and Massachusetts Institute of Technology labs, including enhanced sampling methods inspired by work at ETH Zurich and Max Planck Society institutes. CHARMM supports modeling of lipid bilayers studied by groups at Columbia University and Johns Hopkins University, nucleic acids examined at University of Oxford labs, and small-molecule ligands relevant to Pfizer, Merck & Co., and GlaxoSmithKline medicinal chemistry programs. The package integrates analysis utilities consistent with workflows developed at Stanford University and Princeton University.
CHARMM implements a family of empirical force fields developed and refined by teams including researchers from Boston University, University of North Carolina at Chapel Hill, National Institutes of Health, and Weizmann Institute of Science. These force fields, refined across studies published in venues associated with American Chemical Society and Royal Society of Chemistry, cover proteins, nucleic acids, lipids, carbohydrates, and small molecules. Parameter derivation procedures follow protocols used in collaborations with computational chemists at AstraZeneca and Novartis and leverage quantum chemical data from groups at California Institute of Technology and University of Illinois at Urbana–Champaign. CHARMM supports automated parametrization tools inspired by initiatives at Schrödinger (company) and community efforts coordinated with Open Force Field partners.
CHARMM implements algorithms for long-range electrostatics such as Particle Mesh Ewald, constraint algorithms like SHAKE and RATTLE, and integrators including velocity Verlet and multiple time-stepping schemes. Performance optimizations have been driven by collaborations with high-performance computing centers like Argonne National Laboratory, Oak Ridge National Laboratory, and National Energy Research Scientific Computing Center. Parallelization exploits MPI paradigms developed in projects at Lawrence Livermore National Laboratory and GPU acceleration approaches paralleling work at NVIDIA and IBM. Benchmarking comparisons often reference studies from University of Illinois and University of Tokyo.
CHARMM uses file formats for coordinates, topology, parameter, and trajectory data interoperable with resources such as the Protein Data Bank, MMTF, and tools from UCSF labs. Interfaces connect CHARMM to visualization and analysis tools developed at University of California, San Francisco, The Scripps Research Institute, Visual Molecular Dynamics projects, and third-party packages like GROMACS, NAMD, AMBER, and LAMMPS. Plugin and scripting bridges support interoperability with environments from Python (programming language) communities at NumPy and SciPy ecosystems, and workflow managers used by researchers at Lawrence Berkeley National Laboratory.
CHARMM has been applied to protein folding problems investigated by labs at University of California, Berkeley and Columbia University, membrane protein simulations prominent in work at Rockefeller University, ligand binding studies in pharmaceutical collaborations with Eli Lilly and Company and Roche, and materials modeling in projects involving MIT spin-offs. Case studies include investigations of enzyme mechanisms associated with Howard Hughes Medical Institute researchers, nucleic acid conformational dynamics studied at Cold Spring Harbor Laboratory, and drug resistance mutations explored in collaboration with Centers for Disease Control and Prevention scientists. CHARMM-based studies have appeared in journals affiliated with Nature Publishing Group, Science Magazine, and Proceedings of the National Academy of Sciences.
CHARMM is distributed under academic and commercial licensing models governed by policies from institutions such as Boston University and consortium agreements with industry partners like Schrödinger (company) and D. E. Shaw Research. Academic licenses facilitate use in universities including Harvard University and Yale University, while commercial licensing supports biotechnology firms and pharmaceutical companies. Distribution channels reflect collaborations with software stewardship entities at National Institutes of Health and deployment on infrastructures at Amazon Web Services and national supercomputing centers.