Description: TECHNICAL FIELD OF THE INVENTION
Anthrax is a highly contagious illness that affects both animals and people, and this bacterium is the primary cause. Anthrax spores may survive for decades and are resistant to desiccation, gamma irradiation, UV light, and disinfectants. Carbohydrate chemistry and biology are developing topics of study. Carbohydrate alterations of proteins and lipids are critical mechanisms that impact intercellular recognition in infection, cancer, and immune response. Carbohydrate antibiotics are antibacterial and antifungal microbial metabolites (mostly from Actinomyces species) that are carbohydrate-based or include a carbohydrate component.

BACKGROUND AND PRIOR ART
Drug research and development is a multidisciplinary process. Traditional pharmaceutics adapts wet-lab high-throughput screening (HTS) technologies, which are costly and time-consuming. Alternatively, use a computer. Preliminary estimates put the cost of drug development at $800 million. Hence, molecular modeling-based drug design research may be less expensive. Bioinformatics and efficient methods like combinatorial chemistry, virtual screening, de novo designing, and structure-based drug design have greatly altered drug discovery. It has been frequently utilized to research ligand-receptor interactions and to create novel medications against biological warfare agents. Here, twelve carbohydrate-containing compounds have been selected by several active in silico screening for working as probable antibiotics against Bacillus Anthracis. Methyl a-D- rhamnopyranoside analogues are commonly considered a carbohydrate glucose group and show potent antimicrobial activity.

A DETAILED DESCRIPTION OF THE INVENTION
Optimization and Ligand Preparation
Vibrational frequency data from Material Studio 08, a module of DMol3 was used to do molecular optimization with the use of DFT functionals. As an electronegative atom, oxygen was believed to be present, B3LYP and DNP+ were used to build up functions in the DMol3 code to produce exceptionally exact results. For building the HOMO and LUMO orbitals and their magnitudes, the analytical procedures were used after optimization. Molecular docking, molecular dynamics, and ADMET may all be performed on the PDB file that contains the optimized chemical compounds.

Preparation of the protein
The four proteins for Anthrax caused by Bacillus Anthracis (PDB ID: 3SC6, 3GA9, 1RZ2, 2H3G, and 3G9K) bacteria were selected from the protein data bank (PDB). These proteins were optimized and removed from the water molecules using discovery studio to get fresh protein, and stored in a table of their properties (Figure 2).

Molecular docking
Molecular docking was primarily used to study the binding affinity of biologically active drugs to pathogenic protein stains. AutoDock Vina and PyRx Virtual Screening Tools were used to accomplish the docking method. Center points were placed at X = -34.976, Y = 10.2241, Z = -88.1642, and the dimensions (Angstrom) X = 105.5369, Y = 95.9796, and Z = 79.020 for the grid dimensions. The protein's substrate-binding domain was wrapped in a grid box with the chosen box dimensions. The pathogenic protein-ligand non-covalent interaction was examined using the BIOVIA Discovery Studio Visualizer 2017.

ADMET properties
Pharmacokinetic characteristics of pharmaceutical substances are mostly studied using ADMET parameters. These online databases, http://www.swissadme.ch/ and http://biosig.unimelb.edu.au/pkcsm/, are the most reliable source for AMDET parameter predictions and was used to gather ADMET parameters for the 12 biologically active chemicals we studied (Figure 1). Focused attention has been placed on ADMET metrics such as plasma protein binding and human intestinal assimilation. CYP1A2 and CYP3A4 inhibitor has also been a focus of our research (Tables 1 and 4).
Chemical descriptors and quantum properties
The highest occupied molecular orbital indicates HOMO whereas the lowest unoccupied molecular orbital is LUMO. Assessment of atomic electrical transport properties is carried out by using the energy gaps of threshold molecular orbitals. Anatomically, the closer two systems are to one another, the better the chemical stability. Conversely, the further apart they are from one another, the less stable they are. There are a number of parameters that may be used to analyze bioactivity, including chemical potential, hardness, softness, and electrophilicity coefficients. The stability of the systems is shown by the positive chemical potential. There is a high level of biochemical stability in the molecules because of their hardness, which is higher than their softness.
Ligand RPS12 has the lowest energy gap at 6.546 eV which contributes to greater dissolvement properties than others. On the other hand, Ligand RPS6 has the second-lowest HOMO-LUMO gap at 6.959 eV. The softness of ligands is found from 0.165 to 0.305 eV. With increasing the alkyl side chain in Methyl a-D- rhamnopyranoside, the energy gap, and other parameters are changed in a regular fashion in which the highest chemical stability is obtained at RPS12 and RPS06 although other conveys almost close value (Table 2).
HOMO and LUMO are the best catalysts for determining and controlling the chemical characteristics of any chemical molecule. The chemical characteristics of developed derivatives may be expected in this context because of previous research. Since the aromatic ring and heterocyclic ring both include oxygen atoms, it is still impossible to determine which ring governs the chemical characteristics of these compounds without using the HOMO and LUMO diagram shown in figure 3.

Map of Electrostatic Potential (MEP)
Using the MEP map, researchers may see how the overall charge (positive and negative) of a molecule is distributed. Aside from that, it may identify the presence of ligands or protein-binding areas, as well as potential attack sites for an electrophile or a nucleophile. The molecular electrostatic potential MEP may be used to determine the compounds' nucleophilic and electrophilic sites listed in figure 4. All the ligands (RPS-01 to RPS-12) convey the negative charge in whole molecules so that they can attract to the protein as a nucleophilic group.

Molecular Docking
Determining binding energy is essential for gaining insight into the molecular interactions that drive biochemical mechanisms, system biology, and the links between structure and function. It is also assessed as part of the drug discovery phase in order to aid in the development of medications that adhere their targets preferentially and precisely to their respective targets. Ciprofloxacin is a standard drug for Bacillus Anthracis which is used to bind against this protein and the score is detected as -7.1 K. cal/mol (Table 3). L10 has the highest binding score of -7.3 K. cal/mol against this protein. L9-L11 has maintained the range of 7 K. cal/mol. L2-L7, L12 also maintained the average range of 6 K.cal/mol. All the ligands have maintained the standard drug affinity of -6.00 kcal/mol except ligand 1. In addition, consider more four proteins (3GA9, 1RZ2, 2H3G, and 3G9K), which are available in PDB, for molecular docking study to make a comparative study. In this case, the binding affinity is obtained good enough to say as an inhibitor against anthrax whereas the ligand, RPS09, RPS10, and RPS11 illustrate the higher binding affinity of almost all proteins (Figure 5).

Molecular dynamics simulations
Ligands RPS10 and RPS11 showed the highest top binding scores against anthrax protein. By following these, these two ligands have been selected for molecular dynamics study compared with FDA-approved antibiotic ciprofloxacin listed in Figures 6 (a), (b), and (c). The docking stability and validation are so much high because the RMSD stays below 1.2 A° up to 100 ns time frame during molecular dynamic as well as the RMSF shows a similar trend for stability.

ADMET properties and aquatic and non-aquatic toxicity
Another important factor is whether the drug compound will penetrate the BBB and CNS or not and it will provide the efficacy and toxicity parameters. Considering the logarithmic values from the table which are almost (Table 5) less than -1 for LOGBB and not greater than -2, LogPS of CNS permeability. CYP1A2 inhibitor has shown all negative whether CYP3A4 inhibitor indicated almost positive for all compounds except ligands 1 and 2. This transporter, which is involved in the distribution and clearance of drugs and endogenous chemicals, has been identified as organic cation transporter 2 (OCT2). OCT2 substrates have adverse effects where the table shows there are no Renal Oct2 substrates in these selected drugs that specified the fine clearance of the compounds from the human system. These twelve compounds have also gone through aquatic and non-aquatic toxicity screening. All the compounds have been found from non-mutagenic as negative in AMES toxicity except ligand 2, all negative in Hepatoxicity and skin sensation with the water-soluble properties. The maximum recommended tolerated dose (MRTD) of a chemical in humans provides an estimate of the lethal dosage threshold of the chemical. As a result, the maximum recommended starting dosage of medicines in phase 1 clinical studies will be more accurately guided.

Table 1. Data of Lipinski rule, pharmacokinetics, and drug-likeness
Ligand NBR HBA HBD TPSA
A02 Log p (o/w)
consensus Log Kp
(Skin permeation) cm/s Lipinski rule
Result violation MW Bioavailability
score GI
absorption
RPS01 0 5 4 79.15 -1.13 -8.75 Yes 0 182.21 0.55 High
RPS02 3 6 4 88.38 -0.03 -7.69 Yes 0 268.35 0.55 High
RPS03 7 7 4 86.61 2.13 -5.74 Yes 0 404.62 0.55 High
RPS04 18 8 3 106.84 3.93 -4.34 Yes 1 524.77 0.55 Low
RPS05 18 10 5 125.30 3.74 -5.96 Yes 1 528.72 0.55 Low
RPS06 20 8 3 106.84 5.07 -4.46 Yes 1 520.74 0.55 High
RPS07 18 10 5 136.30 3.08 -5.67 Yes 1 558.79 0.55 Low
RPS08 18 10 5 136.30 3.08 -5.67 Yes 1 558.79 0.55 Low
RPS09 18 8 3 106.84 3.93 -4.34 Yes 1 524.77 0.55 Low
RPS10 18 10 5 125.30 3.79 -5.96 Yes 1 528.72 0.55 Low
RPS11 20 8 3 106.84 5.05 -4.46 Yes 1 520.74 0.55 High
RPS012 18 10 5 136.30 3.08 -5.67 Yes 1 558.79 0.55 low

Viol: violation, NBR: Number of rotatable bonds, HBA: Hydrogen bind acceptors, HBD: Hydrogen bond donors, TPSA: Topological surface area, MW: Molecular weight.

Table 2. Data of chemical descriptors and quantum properties
Ligands A=-LUMO, eV I=-HOMO, eV E GAP= I- A, eV Chemical potential:(µ)=-(I+A)/2 Electronegetivity:x= (I+A)/2 Hardness:(h)=(I-A)/2 Softness:(s)=1/h Electrophilicity

RPS01 10.493 -1.618 12.111 -4.437 4.437 6.055 0.165 10.493
RPS02 10.997 1.197 9.800 -6.097 6.097 4.900 0.204 3.793
RPS03 10.278 2.833 7.445 -6.555 6.555 3.722 0.268 5.772
RPS04 9.986 2.949 7.037 -6.467 6.467 3.518 0.284 5.944
RPS05 9.749 2.334 7.415 -6.041 6.041 3.707 0.269 4.922
RPS06 9.401 2.442 6.959 -5.921 5.921 3.479 0.287 5.038
RPS07 9.804 2.381 7.423 -6.092 6.092 3.711 0.269 5.000
RPS08 9.803 2.865 6.938 -6.334 6.334 3.469 0.288 5.782
RPS09 9.906 2.662 7.244 -6.284 6.284 3.622 0.276 5.451
RPS10 9.405 2.421 6.984 -5.913 5.913 3.492 0.286 5.006
RPS11 9.534 2.178 7.356 -5.865 5.865 3.678 0.271 4.661
RPS012 8.981 2.435 6.546 -5.708 5.708 3.278 0.305 4.977
cid2764 8.352 1.367 6.985 -4.859 4.859 3.492 0.286 3.380

Table 3. Binding Affinity against Anthrax Protein (Protein ID: 3SC6)
Ligands Binding Affinity, kcal/mol Hydrogen Bonds Hydrophobic Bonds
RPS01 5.5 4 1
RPS02 6.1 3 8
RPS03 6.4 2 5
RPS04 6.6 3 16
RPS05 6.9 4 8
RPS06 6.8 4 6
RPS07 6.5 2 10
RPS08 7.1 4 11
RPS09 7.1 6 8
RPS10 7.3 5 6
RPS11 7.2 3 10
RPS012 6.4 4 3
Ciprofloxacin 7.1 5 2

Ligands Anthrax protein ID- 3GA9 Anthrax protein ID-1RZ2 Anthrax protein ID-2H3G Anthrax protein ID-3G9K
RPS01 -5.5 -5.5 -5.7 -5.3
RPS02 -6.1 -5.7 -7.0 -6.2
RPS03 -5.8 -7.0 -6.7 -6.6
RPS04 -6.8 -6.0 -6.3 -6.5
RPS05 -6.8 -7.4 -6.9 -6.3
RPS06 -7.3 -6.2 -6.7 -6.8
RPS07 -6.7 -6.7 -6.8 -6.5
RPS08 -7.0 -7.0 -6.0 -7.5
RPS09 -7.9 -7.3 -6.9 -6.9
RPS10 -6.9 -7.9 -6.7 -7.9
RPS11 -7.8 -7.4 -7.1 -7.7
RPS012 -7.2 -7.2 -6.1 -6.9
Ciprofloxacin -7.0 -6.9 -6.1 -7.6

Table 4. Data for ADME parameters
ligand Human intestinal absorption CaCO2 permeability Blood brain Barrier CNS
permeability Skin
permeability CYP
1A2 inhibitor CYP
2C9
inhibitor CYP
3A4
inhibitor TOTAL
Clearance Renal
OCT2
substrate
RPS01 68.904 0.396 -0.448 -3.129 -3.344 No No No 0.636 No
RPS02 78.275 0.407 -0.096 -2.894 -3.393 No No No 1.174 No
RPS03 94.075 1.584 -0.828 -2.822 -3.033 No No Yes 0.310 No
RPS04 100.00 1.147 -1.395 -2.705 -2.742 No Yes Yes 0.283 No
RPS05 87.778 0.274 -1.105 -3.122 -2.735 No No Yes 0.594 No
RPS06 100.00 1.163 -1.044 -2.729 -2.744 No Yes Yes 0.777 No
RPS07 94.089 1.158 -1.515 -3.034 -2.738 No Yes Yes 0.769 No
RPS08 94.089 1.158 -1.515 -3.034 -2.738 No Yes Yes 0.769 No
RPS09 100.00 1.175 -1.395 -2.728 -2.743 No Yes Yes -0.105 No
RPS10 95.084 0.293 -1.105 -3.144 -2.735 No No Yes 0.604 No
RPS11 100.00 1.191 -1.044 -2.752 -2.744 No Yes Yes 0.773 No
RPS12 100.00 1.138 -1.515 -3.056 -2.738 No yes Yes 0.785 No

Table 5. Aquatic and non-aquatic toxicity
ligands AMES
toxicity Hepatotoxicity Water
Solubility
Log s Skin
Sensation Maximum
Tolerated
dose Oral rat
Acute toxicity
(LD50) Oral Rat Chronic Toxicity (LOAEL) T.
Pyriformis
toxicity
(Log mg/L)
RPS01 No No -0.991 No 1.476 1.088 2.542 0.266
RPS02 Yes No -1.701 No 1.007 2.462 2.090 0.301
RPS03 No No -4.657 No 0.619 2.826 1.625 0.349
RPS04 No No -5.685 No 0.636 2.942 1.549 0.287
RPS05 No No -3.926 No -0.488 2.450 2.652 0.285
RPS06 No No -5.553 No 0.670 2.824 2.003 0.287
RPS07 No No -5.406 No 0.728 3.107 1.740 0.285
RPS08 No No -5.406 No 0.728 3.107 1.740 0.285
RPS09 No No -5.655 No 0.545 2.875 1.501 0.287
RPS10 No No -3.902 No -0.457 2.388 2.319 0.285
RPS11 No No -5.520 No 0.577 2.748 1.955 0.288
RPS12 No No -5.380 No 0.669 3.060 1.657 0.285

Claims: 1. The present invention provides a twelve new methyl a-D-rhamnopyranoside analogues against anthrax
2. A method as claimed in claim 1, wherein molecular dynamics simulations were evaluated
3. A method as claimed in claim 1, wherein ADMET properties were performed
4. A method as claimed in claim 1, wherein density functional theory was calculated as claimed in claim 1, wherein aquati
5. A method c and non-aquatic toxicity propreties were studied