CHARMM (Chemistry at HARvard Macromolecular Mechanics). CHARMM models the dynamics and mechanics of macromolecular systems using empirical and mixed empirical/quantum mechanical force fields. CHARMM is designed to investigate the structure and dynamics of large molecules. It performs free energy calculations of mutations and drug binding as well as conformational folding of peptides. It uses classical mechanical methods to investigate potential energy surfaces derived from experimental and ”ab initio” quantum chemical calculations. In addition, mixed quantum mechanical/classical systems can be defined to investigate chemical processes such as enzyme catalysis.

References in zbMATH (referenced in 90 articles )

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  1. Stefanelli, Ulisse: Stable carbon configurations (2017)
  2. Chen, Duan: A new Poisson-Nernst-Planck model with ion-water interactions for charge transport in ion channels (2016)
  3. Friedrich, Manuel; Piovano, Paolo; Stefanelli, Ulisse: The geometry of $C_60$: a rigorous approach via molecular mechanics (2016)
  4. Gogolinska, Anna; Jakubowski, Rafal; Nowak, Wieslaw: Petri nets formalism facilitates analysis of complex biomolecular structural data (2016)
  5. Mishra, Avdesh; Iqbal, Sumaiya; Hoque, Md Tamjidul: Discriminate protein decoys from native by using a scoring function based on ubiquitous phi and psi angles computed for all atom (2016)
  6. Trȩdak, Przemysław; Rudnicki, Witold R.; Majewski, Jacek A.: Efficient implementation of the many-body reactive bond order (REBO) potential on GPU (2016)
  7. Cang, Zixuan; Mu, Lin; Wu, Kedi; Opron, Kristopher; Xia, Kelin; Wei, Guo-Wei: A topological approach for protein classification (2015)
  8. Cisneros-Ake, Luis A.; Cruzeiro, Leonor; Velarde, Manuel G.: Mobile localized solutions for an electron in lattices with dispersive and non-dispersive phonons (2015)
  9. Farrell, Kathryn; Oden, J.Tinsley; Faghihi, Danial: A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems (2015)
  10. Lampariello, Francesco; Liuzzi, Giampaolo: Global optimization of protein-peptide docking by a filling function method (2015)
  11. Leimkuhler, Ben; Matthews, Charles: Molecular dynamics. With deterministic and stochastic numerical methods (2015)
  12. Chen, Duan: Modeling and computation of heterogeneous implicit solvent and its applications for biomolecules (2014)
  13. Li, Chunyu; Coons, Eric; Strachan, Alejandro: Material property prediction of thermoset polymers by molecular dynamics simulations (2014)
  14. Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Song, Chanyoung; Kim, Deok-Soo: BetaSCP2: a program for the optimal prediction of side-chains in proteins (2014)
  15. Xie, Dexuan: New solution decomposition and minimization schemes for Poisson-Boltzmann equation in calculation of biomolecular electrostatics (2014)
  16. Almeida, Fabio C.L.; Moraes, Adolfo H.; Gomes-Neto, Francisco A. M.: An overview on protein structure determination by NMR: historical and future perspectives of the use of distance geometry methods (2013)
  17. Frausto-Solís, Juan; Sánchez-Pérez, Mishael; Lińan-García, Ernesto; Sánchez-Hernández, Juan Paulo: Threshold temperature tuning simulated annealing for protein folding problem in small peptides (2013) ioport
  18. Harris, Robert C.; Mackoy, Travis; Fenley, Marcia O.: A stochastic solver of the generalized Born model (2013)
  19. Kreienkamp, Amelia B.; Liu, Lucy Y.; Minkara, Mona S.; Knepley, Matthew G.; Bardhan, Jaydeep P.; Radhakrishnan, Mala L.: Analysis of fast boundary-integral approximations for modeling electrostatic contributions of molecular binding (2013)
  20. Li, Chuan; Li, Lin; Petukh, Marharyta; Alexov, Emil: Progress in developing Poisson-Boltzmann equation solvers (2013)

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