Literature DB >> 21755090

Dispersion interactions of carbohydrates with condensate aromatic moieties: theoretical study on the CH-π interaction additive properties.

Stanislav Kozmon1, Radek Matuška, Vojtěch Spiwok, Jaroslav Koča.   

Abstract

In this article we present the first systematic study of the additive properties (i.e. degree of additivity) of the carbohydrate-aromatic moiety CH-π dispersion interaction. The additive properties were studied on the β-D-glucopyranose, β-D-mannopyranose and α-L-fucopyranose complexes with the naphthalene molecule by comparing the monodentate (single CH-π) and bidentate (two CH-π) complexes. All model complexes were optimized using the DFT-D approach, at the BP/def2-TZVPP level of theory. The interaction energies were refined using single point calculations at highly correlated ab initio methods at the CCSD(T)/CBS level, calculated as E + (E(CCSD(T))-E(MP2))(Small Basis). Bidentate complexes show very strong interactions in the range from -10.79 up to -7.15 and -8.20 up to -6.14 kcal mol(-1) for the DFT-D and CCSD(T)/CBS level, respectively. These values were compared with the sum of interaction energies of the appropriate monodentate carbohydrate-naphthalene complexes. The comparison reveals that the bidentate complex interaction energy is higher (interaction is weaker) than the sum of monodentate complex interaction energies. Bidentate complex interaction energy corresponds to 2/3 of the sum of the appropriate monodentate complex interaction energies (averaging over all modeled carbohydrate complexes). The observed interaction energies were also compared with the sum of interaction energies of the corresponding previously published carbohydrate-benzene complexes. Also in this case the interaction energy of the bidentate complex was higher (i.e. weaker interaction) than the sum of interaction energies of the corresponding benzene complexes. However, the obtained difference is lower than before, while the bidentate complex interaction energy corresponds to 4/5 of the sum of interaction energy of the benzene complexes, averaged over all structures. The mentioned comparison might aid protein engineering efforts where amino acid residues phenylalanine or tyrosine are to be replaced by a tryptophan and can help to predict the changes in the interactions. The observed results also show that DFT-D correctly describes the CH-π interaction energy and their additive properties in comparison to CCSD(T)/CBS calculated interaction energies. Thus, the DFT-D approach might be used for calculation of larger complexes of biological interest, where dispersion interaction plays an important role.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21755090     DOI: 10.1039/c1cp21071h

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  6 in total

1.  Theoretical study on host-guest interaction between pillar[4]arene and molecules or ions.

Authors:  Chao Shen; Zhenyu Gong; Lei Gao; Minglong Gu; Long Huan; Sicong Wang; Ju Xie
Journal:  J Mol Model       Date:  2018-07-09       Impact factor: 1.810

2.  DNA-protein π-interactions in nature: abundance, structure, composition and strength of contacts between aromatic amino acids and DNA nucleobases or deoxyribose sugar.

Authors:  Katie A Wilson; Jennifer L Kellie; Stacey D Wetmore
Journal:  Nucleic Acids Res       Date:  2014-04-17       Impact factor: 16.971

3.  Influence of Trp flipping on carbohydrate binding in lectins. An example on Aleuria aurantia lectin AAL.

Authors:  Josef Houser; Stanislav Kozmon; Deepti Mishra; Sushil K Mishra; Patrick R Romano; Michaela Wimmerová; Jaroslav Koča
Journal:  PLoS One       Date:  2017-12-12       Impact factor: 3.240

Review 4.  CH/π Interactions in Carbohydrate Recognition.

Authors:  Vojtěch Spiwok
Journal:  Molecules       Date:  2017-06-23       Impact factor: 4.411

5.  Bioinformatic and biochemical analysis of the key binding sites of the pheromone binding protein of Cyrtotrachelus buqueti Guerin-Meneville (Coleoptera: Curculionidea).

Authors:  Hua Yang; Yan-Lin Liu; Yuan-Yuan Tao; Wei Yang; Chun-Ping Yang; Jing Zhang; Li-Zhi Qian; Hao Liu; Zhi-Yong Wang
Journal:  PeerJ       Date:  2019-10-14       Impact factor: 2.984

Review 6.  Three-Dimensional Structures of Carbohydrates and Where to Find Them.

Authors:  Sofya I Scherbinina; Philip V Toukach
Journal:  Int J Mol Sci       Date:  2020-10-18       Impact factor: 5.923

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.