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Computational Methods for Biochemical Simulations Implemented in GAMESS.

Dmitri G Fedorov1, Hui Li2, Vladimir Mironov3

  • 1Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.

Methods in Molecular Biology (Clifton, N.J.)
|February 5, 2020
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Summary
This summary is machine-generated.

This review details computational methods in the GAMESS package for biochemical modeling, including quantum mechanics/molecular mechanics (QM/MM) and fragmentation approaches. It highlights QuanPol and fragment molecular orbital (FMO) capabilities for molecular simulations.

Keywords:
Fragment molecular orbital (FMO)GAMESSGUIMolecular mechanics (MM)QuanPolQuantum mechanics (QM)

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Area of Science:

  • Computational chemistry
  • Biochemistry
  • Molecular modeling

Background:

  • The GAMESS package offers various computational methods for simulating biochemical processes.
  • Accurate modeling of biochemical systems is crucial for understanding biological functions and developing new therapeutics.

Purpose of the Study:

  • To review computational methods for biochemical modeling within the GAMESS package.
  • To summarize the capabilities of specific QM/MM and fragmentation methods.
  • To describe relevant molecular modeling and visualization tools for GAMESS users.

Main Methods:

  • Review of quantum mechanics/molecular mechanics (QM/MM) approaches.
  • Summary of semi-empirical and fragmentation methods.
  • Description of the QuanPol program for QM/MM and the fragment molecular orbital (FMO) method.
  • Overview of molecular modeling and visualization software compatible with GAMESS.

Main Results:

  • Detailed capabilities of QM/MM implementation in QuanPol are presented.
  • The fragment molecular orbital (FMO) method's applicability is summarized.
  • A compilation of GAMESS features and relevant references is provided for user convenience.

Conclusions:

  • GAMESS offers a comprehensive suite of computational tools for biochemical simulations.
  • The reviewed methods, including QM/MM and FMO, are valuable for studying complex biochemical systems.
  • Integration with molecular modeling packages enhances the utility of GAMESS for researchers.