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 98 articles )

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  1. Agrahari, Ashish Kumar; Kumar, Amit; R, Siva; Zayed, Hatem; C, George Priya Doss: Substitution impact of highly conserved arginine residue at position 75 in \itGJB1 gene in association with X-linked charcot-marie-tooth disease: A computational study (2018)
  2. Lone, Mohsin Yousuf; Kumar, Sivakumar Prasanth; Athar, Mohd; Jha, Prakash Chandra: Exploration of mycobacterium tuberculosis structural proteome: an in-silico approach (2018)
  3. Zhong, Yimin; Ren, Kui; Tsai, Richard: An implicit boundary integral method for computing electric potential of macromolecules in solvent (2018)
  4. Fath, L.; Hochbruck, M.; Singh, C. V.: A fast mollified impulse method for biomolecular atomistic simulations (2017)
  5. Mainini, Edoardo; Murakawa, H.; Piovano, Paolo; Stefanelli, Ulisse: Carbon-nanotube geometries as optimal configurations (2017)
  6. Michael E. Fortunato, Coray M. Colina: pysimm: A python package for simulation of molecular systems (2017)
  7. Stefanelli, Ulisse: Stable carbon configurations (2017)
  8. Chen, Duan: A new Poisson-Nernst-Planck model with ion-water interactions for charge transport in ion channels (2016)
  9. Friedrich, Manuel; Piovano, Paolo; Stefanelli, Ulisse: The geometry of $C_60$: a rigorous approach via molecular mechanics (2016)
  10. Gogolinska, Anna; Jakubowski, Rafal; Nowak, Wieslaw: Petri nets formalism facilitates analysis of complex biomolecular structural data (2016)
  11. 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)
  12. Trȩdak, Przemysław; Rudnicki, Witold R.; Majewski, Jacek A.: Efficient implementation of the many-body reactive bond order (REBO) potential on GPU (2016)
  13. Cang, Zixuan; Mu, Lin; Wu, Kedi; Opron, Kristopher; Xia, Kelin; Wei, Guo-Wei: A topological approach for protein classification (2015)
  14. Cisneros-Ake, Luis A.; Cruzeiro, Leonor; Velarde, Manuel G.: Mobile localized solutions for an electron in lattices with dispersive and non-dispersive phonons (2015)
  15. Farrell, Kathryn; Oden, J. Tinsley; Faghihi, Danial: A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems (2015)
  16. Lampariello, Francesco; Liuzzi, Giampaolo: Global optimization of protein-peptide docking by a filling function method (2015)
  17. Leimkuhler, Ben; Matthews, Charles: Molecular dynamics. With deterministic and stochastic numerical methods (2015)
  18. Chen, Duan: Modeling and computation of heterogeneous implicit solvent and its applications for biomolecules (2014)
  19. Li, Chunyu; Coons, Eric; Strachan, Alejandro: Material property prediction of thermoset polymers by molecular dynamics simulations (2014)
  20. Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Song, Chanyoung; Kim, Deok-Soo: BetaSCP2: a program for the optimal prediction of side-chains in proteins (2014)

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