Krishna Govender1, Jiali Gao2, Kevin J Naidoo1. 1. Scientific Computing Research Unit, University of Cape Town, Rondebosch 7701, South Africa ; Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa. 2. State Key Laboratory of Theoretical and Computational Chemistry, Jilin University, Changchun, Jilin Province 130012, China ; Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States.
Abstract
A semiempirical method based on the AM1/d Hamiltonian is introduced to model chemical glycobiological systems. We included in the parameter training set glycans and the chemical environment often found about them in glycoenzymes. Starting with RM1 and AM1/d-PhoT models we optimized H, C, N, O, and P atomic parameters targeting the best performing molecular properties that contribute to enzyme catalyzed glycan reaction mechanisms. The training set comprising glycans, amino acids, phosphates and small organic model systems was used to derive parameters that reproduce experimental data or high-level density functional results for carbohydrate, phosphate and amino acid heats of formation, amino acid proton affinities, amino acid and monosaccharide dipole moments, amino acid ionization potentials, water-phosphate interaction energies, and carbohydrate ring pucker relaxation times. The result is the AM1/d-Chemical Biology 1 or AM1/d-CB1 model that is considerably more accurate than existing NDDO methods modeling carbohydrates and the amino acids often present in the catalytic domains of glycoenzymes as well as the binding sites of lectins. Moreover, AM1/d-CB1 computed proton affinities, dipole moments, ionization potentials and heats of formation for transition state puckered carbohydrate ring conformations, observed along glycoenzyme catalyzed reaction paths, are close to values computed using DFT M06-2X. AM1/d-CB1 provides a platform from which to accurately model reactions important in chemical glycobiology.
A semiempirical method span class="Chemical">pan class="Chemical">basespan>d on the paspan>n class="Species">AM1/d Hamiltonian is introduced to model chemical glycobiological systems. We included in the parameter training set pan class="Chemical">glycans and the chemical environment often found about them in glycoenzymes. Starting with RM1 and AM1/d-PhoT models we optimized H, C, N, O, and P atomic parameters targeting the best performing molecular properties that contribute to enzyme catalyzed glycan reaction mechanisms. The training set comprising glycans, amino acids, phosphates and small organic model systems was used to derive parameters that reproduce experimental data or high-level density functional results for carbohydrate, phosphate and amino acid heats of formation, amino acid proton affinities, amino acid and monosaccharidedipole moments, amino acid ionization potentials, water-phosphate interaction energies, and carbohydrate ring pucker relaxation times. The result is the AM1/d-Chemical Biology 1 or AM1/d-CB1 model that is considerably more accurate than existing NDDO methods modeling carbohydrates and the amino acids often present in the catalytic domains of glycoenzymes as well as the binding sites of lectins. Moreover, AM1/d-CB1 computed proton affinities, dipole moments, ionization potentials and heats of formation for transition state puckered carbohydrate ring conformations, observed along glycoenzyme catalyzed reaction paths, are close to values computed using DFT M06-2X. AM1/d-CB1 provides a platform from which to accurately model reactions important in chemical glycobiology.
Authors: B R Brooks; C L Brooks; A D Mackerell; L Nilsson; R J Petrella; B Roux; Y Won; G Archontis; C Bartels; S Boresch; A Caflisch; L Caves; Q Cui; A R Dinner; M Feig; S Fischer; J Gao; M Hodoscek; W Im; K Kuczera; T Lazaridis; J Ma; V Ovchinnikov; E Paci; R W Pastor; C B Post; J Z Pu; M Schaefer; B Tidor; R M Venable; H L Woodcock; X Wu; W Yang; D M York; M Karplus Journal: J Comput Chem Date: 2009-07-30 Impact factor: 3.376