Journal of Computational Chemistry & Molecular Modeling

ISSN: 2473-6260

Impact Factor: 0.562

VOLUME: 4 ISSUE: 4

Page No: 452-462

Thermochemical, DFT, Molecular Docking and Pharmacokinetic Studies of Methyl β-D-galactopyranoside Esters


Corresponding Author

Sarkar M A Kawsar

Tel +88 01762717081; Fax +88 031 2606014

E-mail: akawsarabe@yahoo.com

Affiliation

Sarkar M. A. Kawsar1*, Mohammed A. Hosen1, Yuki Fujii2, Yasuhiro Ozeki3

1Laboratory of Carbohydrate and Nucleoside Chemistry (LCNC), Department of Chemistry, Faculty of Science, University of Chittagong, Chittagong-4331, Bangladesh.

2Laboratory of Functional Morphology, Graduate School of Pharmaceutical Sciences, Nagasaki International University, 2825-7 Huis Ten Bosch, Sasebo, Nagasaki 859-3298, Japan.

3Laboratory of Glycobiology and Marine Biochemistry, Department of Life and Environmental System Science, Graduate School of Nano Biosciences, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama 236-0027, Japan.

 

Citation

Kawsar SMA, M.A.Hosen, Y.Fujii, Y.Ozeki, Thermochemical, DFT, Molecular Docking and Pharmacokinetic Studies of Methyl β-D-galactopyranoside Esters (2020) Journal of Computational Chemistry & Molecular Modeling 4(4) pp:452-462

Abstract

Monosaccharide esters have great importance in the field of carbohydrate chemistry because of its effectiveness in the synthesis of biologically active products. As a consequence, the chemistry and biochemistry of carbohydrate derivatives are an essential part of biochemical and medicinal research. Modification of the hydroxyl (-OH) group of monosaccharides by acylation increases their biological activity. In this study, we have optimized a monosaccharide molecule, methyl β-D-galactopyranoside (MGP, 1), and some of its acylated esters employing density functional theory (DFT). All designed esters are optimized at the B3LYP/3-21G level of theory. Electronic energies, enthalpies, Gibbs free energies, heat capacity, entropy, molar refractivity, polarizability dipole moments, HOMO-LUMO gaps, density of states (DOS) and molecular electrostatic potential (MEP) of these modified compounds are also investigated in the subsequent analysis. Then compound (8), which showed a positive test on EAC cell, was subjected to molecular docking analysis with human poly [ADP-ribose] polymerase1 protein 4ZZZ to investigate the molecular interactions widely. Finally, ADMET analysis hints that modified derivatives of MGP (1) are less toxic and have improved pharmacokinetic properties than those of the parent drug.

Keywords:  galactopyranoside, esters, EAC, DFT calculation, pharmacokinetic

Introduction

Carbohydrates (named also sugars or saccharides) are the most abundant biomolecules in nature and they are often conjugated with proteins (glycoproteins) or lipids (glycolipids). These molecules play different roles in the cells, and for this reason, recent studies are focused on carbohydrates as a selective mediator in the molecular recognition processes. They are involved in physiological and pathological events and a variety of intra- and extracellular events. In most of the cases, the initial contacts of saccharides with cells take place at cell-cell and cell-matrix level [1, 2]. However, modifications to the regular cell glycosylation pattern are often observed during pathological conditions, for example in inflammation and cancer development. Therefore, glycans have become a topic of major interest to develop diagnostic tools or therapeutic drugs [3, 4]. Several carbohydrate-based drugs are currently being used to treat several diseases; the most illustrative examples are antibiotics and antivirals [5]. For decades, aminoglycosides, inhibitors of bacterial protein synthesis, have been used as antibiotics especially for infections by aerobic Gram-negative bacteria [6]. Glycans are also related to metabolic drugs e.g., diabetes mellitus type I [7] and type II diabetes [8].

To date, several sugar-based drugs have already been developed and currently employed as therapeutics, diagnostics, vaccines, and many more are expected to come in the next few years. Glycan diversity depends on the monosaccharides (basic unit of saccharide) which are made by, on the linkages that connect monosaccharides (glycosidic linkage), and on other factors such as the anomeric effects, the orientation of all the torsional angles [9, 10], and the intramolecular hydrogen bonding between adjacent OH groups [11]. Therefore, it is of paramount importance to predict and characterize their conformational and structural properties in a systematic manner. Computational techniques have proven to be essential in this aspect [12].

In this study, we have modified the hydroxyl (-OH) group of methyl β-D-galactopyranoside (MGP, 1) (Fig. 1) by some acyl substituents (including aromatic and heteroaromatic group) [13] and these modified esters are optimized to realize their thermal, electrical stability and biochemical behavior based on quantum mechanical methods. The free energy, enthalpy, entropy, heat capacity dipole- moment, HOMO-LUMO gap, DOS plot, polarizability, molar refractivity, and molecular electrostatic potential have been calculated to compare their thermal and chemical characteristic. Compound (8), which showed a positive result on test with EAC cell i.e. anti-cancer activity [13] was subjected to molecular docking to understand the nonbonding interactions, binding affinity, and binding mode with the receptor protein (4ZZZ). Finally, pharmacokinetic enumeration has been performed to compare their absorption, metabolism, and toxicity. The prime intention of our investigation was to understand the thermodynamic, molecular orbital, binding mode, molecular electrostatic potential, physicochemical and, ADMET properties (MGP, 1) and its esters.

https://www.siftdesk.org/articles/images/10663/1.png

Fig. 1 Chemical structure of the methyl β-D-galactopyranoside (MGP, 1).

Materials & Methods

2.1. Designing and Optimization of MGP Esters

In computational chemistry, quantum mechanical methods are widely used to calculate thermal, molecular orbital and molecular electrostatic properties [14]. Geometry optimization and further modification of all MGP (1) esters are carried out using the Gaussian 09 program [14]. Density functional theory (DFT) with Beck’s (B) [15] three-parameter hybrid model, Lee, Yang, and Parr’s (LYP) [16] correlation functional under 3-21G basis set has been employed to optimize and predict their thermochemical and molecular orbital properties. Dipole moment, enthalpy, free energy, entropy, heat capacity, molar refractivity, and polarizability were calculated for all the compounds. Besides this, electrostatic potential calculated for MGP (1) and its four esters. Frontier molecular orbital features HOMO (highest occupied molecular orbital), LUMO (lowest unoccupied molecular orbital) were counted at the same level of theory. For each of the MGP (1) esters, HOMO-LUMO energy gap, hardness (η), and softness (S) were calculated from the energies of frontier HOMO and LUMO as reported considering Parr and Pearson interpretation of  DFT and Koopmans theorem [17] on the correlation of ionization potential (I) and electron affinities (E) with HOMO and LUMO energy (ε). The following equations are used to calculate hardness (η), softness (S).

https://www.siftdesk.org/articles/images/10663/e1.png

2.2. Preparation of Protein and Molecular Docking

The three-dimensional crystal structure of human PARP1 (PDB ID: 4ZZZ) was retrieved in pdb format from the protein data bank [18]. All hetero atoms and water molecules were removed using PyMol (version 1.3) software packages [19]. Swiss-Pdb viewer software (version 4.1.0) was employed for energy minimization of the protein [20]. Then optimized drugs were subjected for molecular docking study against human PARP1 protein (4ZZZ). In computer-aided drug design, molecular docking simulation can predict the binding affinity and mode(s) of ligand with target protein [21]. In fine, molecular docking simulation was rendered by PyRx software (version 0.8) [22] considering the protein as macromolecule and the drug as ligand. AutodockVina was employed for docking analysis, and AutoDock Tools (ADT) of the MGL software package was used to convert pdb into a pdbqt format to input protein and ligands. The size of the grid box in AutoDockVina was kept at 52.1251, 58.4212, and 61.7939 Å  for X, Y, Z directions respectively. After the completion docking, both the macromolecule and ligand structures were saved in. pdbqt format needed by Accelrys Discovery Studio (version 4.1) to explore and visualize the docking result and search the nonbonding interactions between ligands and amino acid residues of receptor protein [23].

2.3. Pharmacokinetic Parameters Analysis

To check the pharmacokinetic parameters and toxicity of the modified MGP (1) esters and parent compound, the admetSAR server was utilized [24]. All the latest and most extensive manually curated data predicted by admetSAR with help of structure uniformity search methods for various chemicals linked with known ADMET schemes. For ADMET analysis, the admetSAR program was used in which 96000 matchless compounds with 45 types of ADMET-associated parameters, proteins, and organisms have been cautiously curated from a massive number of diverse literature. Although it is quite difficult to verify all of these compounds and to know whether this program included thymidine-based drugs or not, well known Pt-based cisplatin and carboplatin as well as metal-based drugs approved in the FDA and in clinical trials as test candidates to verify our MGP (1) esters. Moreover, some physicochemical properties (molar refractivity, molecular weight) are also calculated from the swissADME server.  

2.4. Strategies and Optimization of Designed MGP (1) Esters

 The newly modified esters of MGP (1) used in this study were designated according to the reactions scheme. MGP (1) and its esters (2-10) were designated and optimized in the quantum mechanical method. In the Fig. 2 represented the optimized structure of MGP (1) esters.

https://www.siftdesk.org/articles/images/10663/2.png

Fig. 2 Optimized structure of MGP (1) and its esters (2-10). Optimized with DFT-B3LYP/3-21g.

Results

3.1. Characterization

We have modified a group of MGP (1) esters (Fig. 3) and attempt to performed computational study to realize the mode of their different properties. Gaussian 09 is a way of molecular modeling program which consents to develop and analyze diverse molecular structures and determine their thermophysical, thermo-chemical, frontier orbital, and molecular electrostatic potential. To perform this work, core i7 computer and Gaussian 09 software were employed and the optimized structures are provided in Fig. 2. To setup reactivity computing based on Gaussian 09, molecular symmetry is a very potent tool. Molecular optimization revealed that all modified derivatives containing more than one symmetry axis. The molecules belong to the class asymmetry, and non-planar and they have two or more elements of symmetry and the plane of the molecule.

https://www.siftdesk.org/articles/images/10663/3.png

Fig. 3 Chemical structures of the MGP (1) esters 2-10.

 

3.2. Thermochemical Analysis

Usual alterations of the molecular structure significantly influence the structural properties including thermal and molecular orbital parameters. Spontaneously of a reaction and stability of a product can be elucidated from the free energy and enthalpy values [25]. Highly negative values are more suitable for thermal stability. In computational molecular modeling, the dipole moment influences non-bonded interactions hydrogen bond formation smoothly. Binding property can also be improved by the increasing of dipole moment [26]. Table 1 represents the thermochemical properties of methyl β-D-galactopyranoside and it’s all synthesized esters. The highest free energy is (-3655.5316 Hartree) observed for ester 10 which also showed the highest enthalpy (-3655.3896 Hartree), highest electronic energy (-3655.3906 Hartree) and the highest dipole moment (15.7081Debye).

Table 1. Molecular formula, Molecular weight (g/mol), Electronic energy, enthalpy, Gibbs free energy (Hartree) & dipole moment (Debye) of MGP (1) esters

Compounds no.

Molecular formula

Molecular weight

Electronic energy

Enthalpy

Gibbs free energy

Dipole moment

1

C7H14O6

194.18

-722.2093

-722.2084

-722.2608

4.7712

2

C12H22O7

278.30

-991.2505

-991.2495

-991.3208

3.8025

3

C18H28O10

404.41

-1446.5981

-1446.5972

-1446.6928

4.0591

4

C21H34O10

446.49

-1563.7944

-1563.7934

-1563.8927

4.1739

5

C24H40O10

488.57

-1681.0208

-1681.0198

-1681.1321

3.1126

6

C39H70O10

698.97

-2267.0782

-2267.0773

-2267.2356

2.5455

7

C54H100O10

909.37

-2853.1492

-2853.1482

-2853.3563

3.3707

8

C60H112O10

993.53

-3249.3087

-3249.3078

-3249.4455

5.3101

9

C33H31Cl3O10

693.95

-3390.6553

-3390.6543

-3390.7839

5.0127

10

C30H34O13S3

698.78

-3655.3906

-3655.3896

-3655.5316

15.7081

Table 2. Molar refractivity (Å3), Polarizability (a.u.), Heat capacity (cal/mol-kelvin), Entropy (cal/mol-kelvin) and Total energy (Hartree)

Compounds

Molar Refractivity

Polarizability

Heat capacity

Entropy

Total energy

1

40.47

85.3296

49.303

110.240

-722.4470

2

64.36

138.3413

77.785

149.967

-991.6197

3

93.58

201.4366

113.232

201.255

-1447.0918

4

108.00

232.8350

123.704

208.931

-1564.3807

5

122.42

263.6293

140.956

236.339

-1681.6970

6

194.52

415.4296

212.121

333.346

-2268.2077

7

266.63

570.1433

283.525

437.868

-2854.7312

8

295.47

631.8790

314.425

469.859

-3250.0814

9

168.31

383.7566

166.283

272.731

-3391.2924

10

161.98

424.9966

191.798

298.880

-3656.0891

In Table 2 represents some other thermophysical properties where the compound (8) showed the highest value for molar refractivity, polarizability, heat capacity, and entropy. Presence of bulky acylating groups also suggesting the possible improvement of polarizability. In Table 1 and Table 2, the values of all the properties increase with the increase of molecular weight from compound (2) to (8) and these esters contain long-chain acyl substituents. This analysis may reveal that with the increased molecular weight of MGP (1) esters, their stability may increase. But in the case of compound (9) and (10), which heteroaromatic ring showed the highest value for energies than the long-chain acyl substituents. In a word, all the esters showed better characteristics than the parent molecule (1).

3.3. Molecular Orbitals Analysis

The frontier molecular orbitals are the most important orbitals in a molecule and they are considered to characterize the chemical reactivity and kinetic stability (Table 3). These frontier molecular orbitals are known as the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) (Fig. 4).

Table 3. Energy (eV) of HOMO, LUMO, energy gap, hardness and softness of esters

Compounds

εHOMO

εLUMO

Gap

Hardness (η)

Softness (S)

1

-6.1918

1.3761

7.5679

3.7839

0.2643

2

-6.5189

-0.0474

6.4715

3.2357

0.3091

3

-7.0076

-0.1346

6.8730

3.4365

0.2909

4

-6.7812

-0.1806

6.6006

3.3003

0.3031

5

-6.8892

-0.1690

6.7202

3.3601

0.2976

6

-6.9978

0.1268

6.8710

3.4355

0.2911

7

6.8974

-0.4283

6.4691

3.2345

0.3092

8

-6.4016

-1.8344

4.5672

2.2836

0.4379

9

-6.2307

-1.8755

4.3552

2.1776

0.4593

10

6.7488

-1.7865

4.9623

2.4812

0.4031

The electronic absorption relates to the transition from the ground to the first excited state and mainly described by one electron excitation from HOMO to LUMO [27]. Kinetic stability increases with the increase of the HOMO-LUMO gap. As a result, the removal of electrons from ground state HOMO to excited state LUMO requires more energy. Table 3 represents the values of orbital energies, along with the two global chemical descriptors, hardness, and softness, which are also calculated for all compounds. The highest softness was observed for the compound (9). Compound (9) also showed the lowest HOMO-LUMO gap (4.3552 eV) and hardness (2.1776eV), indicating the molecule is more reactive than other compounds, according to Hoque et al [28, 29]. On the other hand, compound (3) showed the highest HOMO-LUMO gap (6.8730 eV) which less than our parent compound MGP (1) (7.5679 eV) which indicates that ester (3) closely stable as parent compound (1). Fig. 5 represents the density of states (DOS) plot for the highest and lowest energy gap of the modified derivatives.

https://www.siftdesk.org/articles/images/10663/4.png

Fig. 4 Molecular orbital distribution diagram of HOMO & LUMO of (1) and ester (3).

 

In Fig. 4, the LUMO diagram of the compound (3) showed that the electron was localized at the modified acylating group regions only, while the HOMO diagram showed that the electron was localized on the upper part of both the monosaccharide ring and acyl substituents.

https://www.siftdesk.org/articles/images/10663/5.png

Fig. 5 DOS plot and HOMO-LUMO energy gap of the compounds (3) and (9).

3.4. Molecular Electrostatic Potential (MEP) Analysis

Molecular electrostatic potential (MEP) is globally preferred as a map of reactivity that exhibits the most probable region for organic molecules to performing electrophilic and nucleophilic reactions of charged points like reagents [30]. It helps to interpret the biological recognition process and hydrogen bonding interaction [31]. MEP counter map provides a simple way to predict how different geometry could interact. The MEP of the title compound is obtained based on the B3LYP with basis set 3-21G optimized result and shown in Figure 5. The prime significant of MEP is that it simultaneously displays positive, negative, and neutral electrostatic potential regions as well as molecular size, shape by color grading, and very helpful in the study of molecular structure with physicochemical features relationship [32]. Molecular electrostatic potential (MEP) was calculated to forecast the reactive sites for electrophilic and nucleophilic attack of the optimized structure of MGP (1) esters 3, 6, 9, and 10 (Fig. 6). The different colors of electrostatic potential indicate different values. Potentiality of attacking zone decreases in the sequence of blue ˃ green ˃ yellow ˃ orange ˃ red. The maximum negative area is displayed by red color where electrophiles can easily attack and the maximum positive area indicated by blue color which is suitable for nucleophilic attack. Moreover, the green color showed the zero potential zones.

https://www.siftdesk.org/articles/images/10663/6.png

Fig. 6 Molecular electrostatic potential map of MGP (1) esters of 3, 6, 9, and 10.

Table 4. Binding affinity (kcal/mol) and nonbonding interactions of MGP (1) and ester (8)

Compounds

Protein

Binding affinity

Residues in contact

Interaction types

Distance  (Å)

 

 

1

 

 

4ZZZ

 

 

-4.6

GLY888

Conventional H-Bond

2.11116

TYR907

Conventional H-Bond

2.94769

MET890

Conventional H-Bond

    2.3132

MET890

Conventional H-Bond

2.14398

 

 

8

 

 

4ZZZ

 

 

-6.9

GL N759

Conventional H-Bond

2.37494

GLN759

C-H Bond

3.38979

MET890

Alkyl

4.52133

LYS903

Alkyl

4.88986

LEU985

Alkyl

4.55185

TYR907

Pi-Alkyl

4.92231

3.5. Molecular Docking Analysis

As compound (8) showed anti-cancer activity on EAC cell [13], that’s why it subjected to molecular docking against receptor protein 4ZZZ to check its binding mode. Molecular docking analysis reveal that, compound (8) showed higher binding affinity (-6.9 kcal/mol) than the parent compound MGP (1). It has been seen from the structural contrast compound (8) has four additional acyl substituents in the monosaccharide ring, providing a high density of electron in the molecule leading to the highest binding affinity (Fig. 7). To predict the shape and behavior of molecules nonbonding interactions are oftentimes used (Fig. 8). Among all sorts of nonbonding interactions such as CH/O, CH/N, and CH/π, the CH/O is the most noted interaction found in the drug-protein interactions. Parent MGP (1) molecule showed interactions with glycine moiety of the protein including one intensive interaction within shorter distance 2.11116Å. Besides, methionine and tyrosine interactions are observed where methionine exhibited a closed distance (2.14398 Å) interaction due to the interaction of branched alkyl chain with monosaccharide ring. In contrast with parent molecule (1), compound (8) exhibited mostly hydrophobic interactions such as alkyl and Pi-Alkyl. Again, compound (8) showed most interactions in the case of glutamine with a closed distance 2.37494 Å. Moreover, hydrogen and hydrophobic both types of interaction observed in the case of derivative (8). From molecular docking analysis, the major and common residue of 4ZZZ active site is Met890 and Tyr907 from diverse nonbonding interactions with the ligand in Table 4.

https://www.siftdesk.org/articles/images/10663/7.png

Fig. 7 Docked conformation of ester 8 at inhibition bounding site of 4ZZZ.

https://www.siftdesk.org/articles/images/10663/8.png

Fig. 8 Non-bonding interactions of MGP (1) and ester (8) with the amino acid residues of 4ZZZ generated by Discovery Studio.

Table 5. Pharmacokinetic properties of MGP (1) esters

Compound  No.

BBB

Human intestinal absorption

P-glycoprotein inhibitor

hERG

Carcinogen

Acute oral toxicity

1

+(0.8193)

+(0.9782)

NI(0.9612)

WI(0.5371)

NC(0.9857)

III

2

+(0.8193)

+(0.9782)

NI(0.9612)

WI(0.5371)

NC(0.9857)

III

3

+(0.9479)

+(0.6389)

NI(0.5963)

WI(0.5051)

NC(0.9000)

III

4

+(0.9532)

+ (0.4042)

NI(0.7355)

WI(0.4495)

NC(0.8714)

III

5

+(0.9634)

+ (0.4042)

NI(0.7641)

WI(0.4627)

NC(0.8714)

III

6

+(0.9667)

+ (0.4042)

NI(0.7949)

WI(0.4091)

NC(0.8714)

III

7

+(0.9693)

+(0.8102)

NI(0.7553)

WI(0.3916)

NC(0.9143)

III

8

+(0.9667)

+ (0.4042)

NI(0.7641)

WI(0.6682)

NC(0.8714)

III

9

+(0.9606)

+(0.9208)

NI(0.8798)

WI(0.9007)

NC(0.8857)

III

10

+(0.9732)

+(0.8513)

NI(0.8510)

WI(0.8055)

NC(0.8429)

III

+ = Positive, I = Inhibitor, NI = Non-Inhibitor, WI = Weak Inhibitor, NC = Non-Carcinogenic, III = Category III includes compounds with LD50 greater than 500 mg/kg but less than 5000 mg/kg.

3.6. ADMET Analysis

Pharmacokinetic properties have been enumerated to compare the absorption, metabolism, and toxicity of all modified MGP (1) esters. AdmetSAR calculation (Table 5) predicts these monosaccharide derivatives are non-carcinogenic and possess category III oral toxicity, so MGP (1) esters can be suggested to be relatively harmless for oral administration. All drugs are P-glycoprotein non-inhibitor where P-glycoprotein inhibitor can obstruct the absorption, permeability, and retention of the drugs. All drugs exhibit favorable positivity allowing for the blood-brain barrier. However, all the MGP (1) esters have shown weak inhibitory characteristics for human ether –a go-go-Related Gene (hERG). Inhibitory characteristic of hERG can lead to long QT syndrome [33], that’s why further investigation is required on this aspect.

 

Conclusion

In this study, the intrinsic stability, thermochemical features and biochemical interactions of monosaccharide MGP (1) and its modified esters are studied. The most important properties for biological chemistry, thermophysical properties, chemical reactivity, and molecular orbital study like HOMO, LUMO, HOMO-LUMO gap, and molecular electrostatic potential in molecule were optimized which indicates that it may a potential drug molecules. Most of the modified monosaccharide derivatives have the HOMO-LUMO gap closer to MGP (1) and have exalted pharmacokinetic properties than the parent molecule (1). That’s why it may be said that some modified compounds were may more reactive than thymidine, as the showed lower HOMO-LUMO gaps and some may stable like MGP (1). The electrostatic potential map showed probable electrophilic and nucleophilic attacking zone of some derivatives. Besides that, the docked complex of ester (8) and 4ZZZ shows a better binding affinity with significant nonbonding interactions than the parent structure. ADMET analysis suggested that the modified esters were less toxic and have improved pharmacokinetic scheme than the parent drug. Finally, this study may be useful to realize the thermochemical, physicochemical, biological, and pharmacological properties of MGP (1) esters.

Acknowledgement

The authors are very much thankful to the Ministry of Science and Technology (MOST), Government of Bangladesh (Ref.: 39.00.0000.09.06.79.2017/Phy’s-437, 2017-2018) for providing financial support to carry out this research project. Also, this study was supported by JSPS KAKENHI under grant No. JP19K06239 (Y.O. and Y.F.), and also by the Research Promotion Fund of Yokohama City University, and Nagasaki International University.

Abbreviations

MGP: Methyl β-D-galactopyranoside (1); EAC: Ehrlich Ascites Carcinoma; PARP1: poly [ADP-ribose] polymerase1; DFT: Density Functional Theory; HOMO: Highest Occupied Molecular Orbital; LUMO: Lowest Unoccupied Molecular Orbital; QM: Quantum Mechanical; LYP: Lee, Yang and Parr’s; DOS: Density of states; MEP:  Molecular electrostatic potential; hERG: Human Ether-A-Go-Go-Related Gene; BBB: Blood-Brain Barrier.

References

  1. Gabius HJ, Siebert HC, André S, Jiménez-Barbero J, Rudiger H (2004) Chemical biology of the sugar code. Chem Bio Chem 5:740-764. PMid:15174156

    View Article      PubMed/NCBI     
  2. Fraser-Reid BO, Tatsuta K, Thiem J (Eds.) Glycoscience: Chemistry and Chemical Biology, Springer-Verlag Berlin Heidelberg, 2008, 2nd Edition. PMid:18989931

    View Article      PubMed/NCBI     
  3. Varki A, Cummings RD, Esko JD, Freeze HH, Stanley P, Bertozzi CR, Hart GW, Etzler ME, Essential of Glycobiology. Cold Spring Harbor Laboratory Press, 2009, 2nd Edition.

    View Article           
  4. Gabius HJ, André S, Kaltner H, Siebert HC (2002) The sugar code: functional lectinomics. Biochim Biophys Acta 1572:165-177. 00306-9

    View Article           
  5. Fernandez-Tejada A, Canada FJ, Jiménez-Barbero J (2015) Glycans in medicinal chemistry: An underexploited resource. Chem Med Chem 10:1291-1295. PMid:25974358

    View Article      PubMed/NCBI     
  6. Mingeot-Leclercq MP, Glupczynski Y, Tulkens PM (1999) Aminoglycosides: activity and resistance. Antimicrob Agents Chemother 43:727-737. PMid:10103173

    View Article      PubMed/NCBI     
  7. Campo VL, Aragao-Leoneti V, Carvalho I (2013) Glycosidases and diabetes: metabolic changes, mode of action and therapeutic perspectives. Carbohydr Chem 39:181-203.

    View Article           
  8. Dabhi AS, Bhatt NR, Shah MJ (2013) Voglibose: an alpha glucosidase inhibitor. J Clin Diagn Res 7:3023-3027.

  9. Cummings RD (2009) The repertoire of glycan determinants in the human glycome. Mol Biosyst 5:1087-1104. PMid:19756298

    View Article      PubMed/NCBI     
  10. Gabius HJ, André S, Jiménez-Barbero J, Romero A, Solís D (2011) From lectin structure to functional glycomics: principles of the sugar code. Trends Biochem Sci 36: 298-313. PMid:21458998

    View Article      PubMed/NCBI     
  11. Mallajosyula SS, MacKerell AD (2011) Influence of solvent and intramolecular hydrogen bonding on the conformational properties of O-linked glycopeptides. J Phys Chem B 115:11215-11229. PMid:21823626

    View Article      PubMed/NCBI     
  12. Fadda E, Woods RJ (2010) Molecular simulations of carbohydrates and proteincarbohydrate interactions: motivation, issues and prospects. Drug Discov Today 15:596-609. PMid:20594934

    View Article      PubMed/NCBI     
  13. Misbah MH, Ferdous J, Bulbul MZH, Chowdury TS, Kawsar SMA (2020) An efficient, easy route for the synthesis of methyl β-D-galactopyranoside derivatives. J Bang Chem Soc 32: In press.

  14. Gaussian09 RA, Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA et al, Gaussian, Inc, Wallingford CT., 2009.

    View Article           
  15. Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behaviour. Physical Review A 38:3098-3100. PMid:9900728

    View Article      PubMed/NCBI     
  16. Lee C, Yang W, Parr RG (1988) Development of the colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B 37:785-789. PMid:9944570

    View Article      PubMed/NCBI     
  17. Pearson RG (1986) Absolute electronegativity and hardness correlated with molecular orbital theory. Proc Natl Acad Sci 83:8440-8441. PMid:16578791

    View Article      PubMed/NCBI     
  18. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H (2000) The Protein Data Bank. Nucleic Acids Res 28:235-242. PMid:10592235

    View Article      PubMed/NCBI     
  19. Delano wl, The PyMOL Molecular Graphics System. De-Lano Scientific, San Carlos, CA, USA, 2002.

    View Article           
  20. Guex N, Peitsch MC (1997) Swiss-model and the Swiss-Pdb Viewer: An environment for comparative protein modeling. Electrophoresis 18:2714-2723. PMid:9504803

    View Article      PubMed/NCBI     
  21. Seeliger D, De Groot BL (2010) Conformational transitions upon ligand binding: holo-structure prediction from apo conformations. PLoS Comput Biol 6:e1000634. PMid:20066034

    View Article      PubMed/NCBI     
  22. Dallakyan A, Olson J Small-Molecule Library Screening by Docking with Py Rx. In: Hempel JE, Williams CH, Hong CC (Eds.). Chemical Biology: Methods and Protocols. Springer New York, USA, (2015) 243-250. PMid:25618350

    View Article      PubMed/NCBI     
  23. Version ADS 4.0, Accelrys, San Diego, USA, 2017.

  24. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al (2012) admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 52:3099-3105. PMid:23092397

    View Article      PubMed/NCBI     
  25. Cohen N, Benson SW (1993) Estimation of heats of formation of organic compounds by additivity methods. Chem Rev 93:2419-2438.

    View Article           
  26. Lien EJ, Guo ZR, Li RL, Su CT (1982) Use of dipole moment as a parameter in drug-receptor interaction and quantitative structure-activity relationship studies. J Pharm Sci 71:641-655. PMid:7097526

    View Article      PubMed/NCBI     
  27. Saravanan S, Balachandran V (2014) Quantum chemical studies, natural bond orbital analysis and thermodynamic function of 2, 5-dichlorophenylisocyanate. Spectrochim Acta Part A Mol Biomol Spectrosc 120:351-364. PMid:24200649

    View Article      PubMed/NCBI     
  28. Hoque MM, Halim MA, Sarwar MG, Khan M (2015) Palladium‐catalyzed cyclization of 2-alkynyl‐N‐ethanoyl anilines to indoles: synthesis, structural, spectroscopic, and mechanistic study. J Phys Org Chem 28:732-742.

    View Article           
  29. Parr RG, Zhou Z (1993) Absolute hardness: unifying concept for identifying shells and subshells in nuclei, atoms, molecules, and metallic clusters. Acc Chem Res 26:256-258.

    View Article           
  30. Amin ML (2013) P-glycoprotein inhibition for optimal drug delivery. Drug Target Insights 2013:27-34. PMid:24023511

    View Article      PubMed/NCBI     
  31. Politzer P, Murray JS (1991) Molecular electrostatic potentials and chemical reactivity. Rev Comput Chem 2:273-312.

    View Article           
  32. Politzer P, Truhlar DG (Eds.), Chemical applications of atomic and molecular electrostatic potentials, Plenum Press, NY, 1981.

    View Article           
  33. Sanguinetti MC, Firouz MT (2006) hERG potassium channels and cardiac arrhythmia. Nature 440:463-469. PMid:16554806

    View Article      PubMed/NCBI     

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