Insight into the Binding Interaction Mechanism of the ligand M1069 with Human Serum Albumin and A2A Adenosine Receptor – A Biophysical Approach

doi:10.21203/rs.3.rs-3241930/v1

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Insight into the Binding Interaction Mechanism of the ligand M1069 with Human Serum Albumin and A2A Adenosine Receptor – A Biophysical Approach

https://doi.org/10.21203/rs.3.rs-3241930/v1

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Adenosine, a nucleotide essential for human energy metabolism, can also increase the development and metastasis of cancer. Through its interaction to G protein-coupled adenosine receptors (GPCRs), adenosine can stimulate the growth and proliferation of cancer cells. Recent research suggests that drug M1069, which is developed as a treatment for alpha-1 antitrypsin deficiency (AATD), a hereditary condition that can cause lung and liver damage, can also be useful in cancer treatment as an antagonist. It prevents adenosine from binding to the protein A2A adenosine receptor on G-coupled receptors. This study examines the fundamentals of the drug M1069 and its interactions with the A2AAR (target) and HSA (transport) proteins. In this investigation, we are endeavoring to determine the electronic characteristics of the M1069 when it interacts with the HSA and A2AAR protein. Molecular Docking simulation is carried out in order to gain an understanding of the mechanism underlying the binding interaction. Molecular dynamics simulations were applied to the optimal docked pose determined from docking investigations. In addition, the optimization of the ligand and single point energy calculations were performed using density functional theory (DFT) before and after docking to gain insight into the intermolecular interaction and investigate the electronic characteristics of the docked molecules.

A2AAR

M1069

Molecular Dynamics

DFT

ADMET

Cancer is a disease characterized by the uncontrolled multiplication and dissemination of abnormal cells throughout the body [1, 2]. These cells are capable of forming tumors, invading adjacent tissues and organs, and spreading throughout the body via the circulation or lymphatic system [3]. There are numerous forms of cancer, such as breast, lung, prostate, colon, and skin cancer [4]. It is not completely understood what causes cancer, but it is believed to be a combination of genetic and environmental factors. Tobacco use, alcohol consumption, inadequate diet and lack of physical activity, exposure to certain substances and radiation, and certain genetic mutations are common cancer risk factors [5]. Surgery, radiation therapy, chemotherapy, immunotherapy, or a combination of these methods may be used to treat cancer. [6].

Adenosine is a molecule that plays an essential function in the energy metabolism of the body, but it can also promote the growth and spread of cancer cells [7–10] by binding to G protein-coupled adenosine receptors (GPCRs) on the surface of cancer cells [11]. When adenosine binds to GPCRs on cancer cells, it stimulates signaling pathways that promote cell proliferation, prevent apoptosis (programmed cell death), and increase blood vessel development by increasing angiogenic factor production [12]. These actions can boost cancer cell growth and spread, as well as increasing resistance to chemotherapy and radiation therapy [13].

The A2AAR plays a unique function in cancer growth and development compared to other adenosine receptors. A2AAR activation could have a greater effect on malignancy than other adenosine receptors, because it is selectively expressed in wide range of tumor cells as well as tissue, including breast, lung, colon, and prostate cancers [14, 15] It has a high affinity for adenosine, allowing even modest concentrations of adenosine to activate the receptors and promote cancer-related signaling pathways [16]. It has been demonstrated that A2AAR activation suppresses the immune response, allowing cancer cells to evade immune surveillance and promoting tumor growth. It is believed that this effect is more pronounced for A2AAR than for other adenosine receptors [17]. This receptor has a distinct structural conformation compared to other adenosine receptors, which may contribute to its unique signaling and functional properties. These characteristics of A2AAR may make it an especially potential target for cancer research and therapy [8].

Multiple studies have demonstrated that inhibiting adenosine receptors can inhibit the development and spread of certain types of cancer [18]. Caffeine and theophylline are two medicines that target adenosine receptors, have been shown in laboratory experiments and animal models to decrease cancer cell proliferation [9].

M1069, an orally active medication, is one of the antagonists that stops adenosine from interacting with G-protein coupled receptor subtypes such as A2A/A2B [19]. It is not being developed primarily to treat cancer. It is being developed instead as a treatment for alpha-1 antitrypsin deficiency (AATD), a hereditary condition that can cause lung and liver damage [20]. Nonetheless, several studies have looked at utilizing AAT, the protein from which M1069 is formed, as a cancer treatment. AAT has been shown to have anti-inflammatory and anti-protease activities, as well as some indications that it may have anticancer characteristics[21].

This medication molecule circulates throughout the body and binds to the A2AAR on the surface of cancer cells, initiating a signaling cascade that can result in cell death or cell proliferation inhibition. The selectivity of M1069 for A2AARs receptors minimizes off-target effects and increases the likelihood that the drug will reach its intended target [22]. In this work, we use an computational method to investigate the interaction of the antagonist drug molecule M1069 with the target A2AAR protein. As previously stated, the drug2 molecule M1069 is an A2AAR inhibitor. So, by analyzing the interaction, we can learn how efficiently the drug molecule is connecting with the target protein, and this will help to reduce therapeutic failure and boost treatment efficiency.

2.1 Ligand and Protein structure preparation

Using Chemsketch, the chemical structure of the desired drug M1069 (5S)-N-[7-(3,6-dihydro-2H-pyran-4-yl)-4-methoxy-[1, 3]thiazolo[4,5-c]pyridin-2-yl]-7-oxa-2-azaspiro[4.5]decane-2-carboxamide) is drawn. Then by using Avogadro, the structure is protonated, gasteiger charges are added, and the geometrical optimization also performed. The crystal structure of the Human serum albumin (PDB ID – 5Z0B) and A2AAR (PDB ID – 2YDO 2) is extracted from the RCSB Protein Data Bank (https://www.rcsb.org) [23]. The extra chains in the protein structure, crystal water molecules and hetero atom are removed, after that the structure is protonated in PyMol software. Energy minimization of the protein is carried out in Yasara energy minimization server (http://www.yasara.org/minimizationserver.htm) [24].

2.2 Molecular Docking

Molecular Docking is a computational method, to predict the preferred orientation, when two molecules (small molecule and macro-molecule) are interacting with each other to form a stable complex. The binding free energy, strength and stability of the complex are predicted from this best orientation [25]. In this work, we employed molecular docking simulation to predict the favored orientation of both A2AAR- M1069 drug complex and HSA- M1069 complex. The main idea of choosing M1069 drug molecule and A2AAR protein, M1069 is one of the antagonists that inhibit adenosine from interacting with A2AAR. The aim of molecular interaction study, is to understand how effectively the M1069 drug molecule is binding with the A2AAR protein. The molecular docking is carried out using Autodock4 & Autodock tools package [26] and lamarckian genetic algorithm. In docking simulation, the protein is kept rigid and the drug molecule is made to orient. Like this, the simulation is carried out for 100 confirmations. After docking, the interaction between the drug molecule with both A2AAR protein and HSA protein are visualized and analyzed using PyMOL and Discovery studio.

2.3 Molecular Dynamics

The best confirmation of M1069- A2AAR & HSA complex system obtained from molecular docking is subjected to 100ns molecular dynamics simulation. The molecular dynamics simulation is carried out by using Gromacs 2020.6[27]. The topology for free protein is generated by using CHARMM 27 [28] force filed, while the topology of complex protein and its inputs are generated by using CHARMGUI online server [29], the force filed used is CHARMM 36 [30]. To prepare the initial system for simulation, free A2AAR and HSA protein, as well as A2AAR and HSA- M1069 complex was protonated and solvated using TIP3P water box with a dimension of 10 Å³ and buffer ions are added appropriately to make the total charge zero. The first phase of energy minimization was carried with 1000 ps. The isothermal-isobaric (NPT) ensemble was applied to this system with a time step of 2fs at a temperature of 300K and a pressure of 1 bar. For 1ns, the canonical ensemble NVT, i.e., moles (N), volume (V), and temperature (T), was simulated. Furthermore, the simulated conformers resulted in equilibration over 100ns of time.

2.4 Principal Component Analysis

Principal component analysis (PCA) was used to determine the essential dynamics of HSA, A2AAR receptor and its M1069 drug complex. PCA is a multivariate statistical technique for extracting the important features from a covariance matrix by linearly transforming the data[31]. To determine the eigenvector and corresponding eigenvalues, a cartesian coordinate covariance matrix is computed[32]. To calculate PCA for the molecular dynamics trajectory, the python mdtraj package was used. Translational and rotational motions were eliminated by fitting the trajectory of the C-alpha atom coordinates to the mean structure.

2.5 Density Function theory

Density Functional Theory studies were carried out for the drug molecule M1060 before docking (as full structural optimization calculation) and after docking (as single-point energy calculation) to find the intermolecular interaction between the drug molecule and protein (HSA and A2AAR). The structural optimization was carried out using Gaussian 09- and single-point energy calculations were conducted by Jaguar package [33] from Schrodinger. As the drug molecule consists of elemental atoms, 6-311 G + (p, d) was used and B3LYP method [34] was incorporated in both packages. The properties such as frontier molecular orbital, molecular electrostatic potential, mulliken charges were studied.

2.6 ADMET

ADMET is important to evaluating a compound's pharmacodynamics effects, since it contains the pharmacokinetic profile of a therapeutic molecule[35]. Using ADMET, features such as bioavailability, brain penetration, oral absorption, carcinogenicity, and other human intestinal absorption properties of the active compounds were determined.

The SMILES of the compound were uploaded to the Swiss ADME web server (http://www.swissadme.ch/) [36–38] in order to predict the Insilco ADMET models for Absorption, Distribution, Metabolism, Excretion and Toxicity. Using derived values of lipophilicity (WlogP) and polarity (TPSA), the statistical graph technique of Brain or Intestinal Estimated Permeation (BOILED-Egg) was used to determine absorption in the Human Intestine (HIA), access to the central nervous system (CNS), and P-glycoprotein (Pgp) substrates. The phase I metabolism and excretion pathway of the substances was predicted by a similarity test of chemicals from the possibly inhibitory cytochrome P450 (CYP450) isoenzyme inhibitors database 2D6, 1A2, 3A4,2C19 and 2C9. The compound SDF files were uploaded to the ProTox-II web server (https://tox-new.charite.de/protoxII/index.php?site=home) [39] for a virtual comparison of potentially harmful molecular fragments.

3.1 Molecular Docking

In this investigation, we did molecular docking study for both A2AAR protein complex and HSA complex. After docking, the best pose of these complex analyzed by using discovery studio. Here the molecular docking study is carried out for 100 confirmations, based on the lowest binding energy the best confirmation is selected. Figure. 2 (a) & (b) is a 3D representation of the best confirmation of A2AAR- M1069 and HSA- M1069 complex respectively. In Table. 1 the lowest binding energy of the protein- drug complex is given. The binding energy of A2AAR - drug complex is – 7. 02 kcal/ mol, while that of HSA- drug complex is given as – 6.53 kcal/ mol. The energy values shows that the drug molecule is binding very well with both the proteins. In Figure. 3 (a) & (b), 2D plot of interaction between M1069 with A2AAR and HSA respectively. it shows different variety of interaction of M1069 drug molecule with the amino acid residue of A2AAR and HSA protein

Table 1

; Illustrating the protein- drug complex, and the lowest binding energy respectively
Protein	Binding energy (kcal/ mol)
A2AAR (target)	-7.02
HSA (transport)	-6.53

The non covalent interactions such as hydrogen, pi- alkyl, pi- sulfur and alkyl bonds are having a potential application in drug delivery system. These bonds are weak bonds and they not altering the chemical structure and property of the drug molecule. The drug molecule forms two conventional hydrogen bonds in A2AAR protein complex, with TYR A: 138 and ARG A: 134. There is pi- alkyl bond formation with ARG A: 186, LEU A: 115, LEU A: 185, PHE A: 134, PRO A: 118, ALA A: 126, and LEU A: 185 is having a carbon hydrogen bond with M1069 drug molecule. The amino acid residue LEU A: 115 is forming a pi- sigma bond with the drug molecule. Similarly, in HSA- M1069 complex one hydrogen bond is present, and here more pi alkyl bonds are formed. The amino acid residues forming pi alkyl bond with the dug molecules are VAL A: 31, VAL A: 46, VAL A: 25, TYR A: 43, CYS A:28, TRP A: 29, TRP A: 32. The docking study of M1069 with both the A2AAR and HSA protein giving hope full result for future studies.

3.2 Molecular Dynamics

The best confirmation of M1069- A2AAR & HSA complex system obtained from molecular docking is subjected to 100ns molecular dynamics simulation. The molecular dynamics simulation is carried out by using Gromacs 2020.6. In the initial stage, molecular dynamics simulation is carried out for both free A2AAR and HSA protein. This result is taken as a reference for, A2AAR- M1069 and HSA- M1069 complex. After simulation the data is extracted and plotted.

Root-mean-square deviation (RMSD) is commonly used to express the relative change in protein conformation between two sets of atomic coordinates, such as two time points from the trajectory [40]. In Figure. 4, the RMSD plot of (a) A2AAR- M1069 and (b) HSA- M1069 complex is shown. The value shows that free A2AAR protein and A2AAR- M1069 protein complex is get equilibrized and oscillated when simulation time reaches 75ns. The A2AAR protein complex has a variation of approximately 0.2 nm, indicating that the target complex is stable and did not undergo any significant conformational changes during simulation. Similarly for HSA protein, the peaks in the HSA-M1069 complex indicate that a portion of the protein is unfolding in order to bind with the ligand, causing it to deviate from its average position.

Root mean square fluctuation (RMSF) is typically used to evaluate the fluctuation of a specific residue relative to its reference structure [41]. RMSF plot can highlight flexible or rigid areas of the protein or protein complex, which can assist to explain its function. The RMSF value of the HSA and A2A adenosine receptor is measured and taken as the reference to the transport and target complex to study about the fluctutions of the protein residues from its mean position due to its interaction with the ligand respesctively.In Figure. 5 (a) & (b), the black lines represent the free A2AAR protein used as a point of reference and red coloured lines represent the A2AAR-M1069 complex. Significant variation was observed in the loop regions, but only a small amount in the active site regions, as shown by this scatter plot. Due to its long looped region and conformational fluctuations under the influence of ligand molecules, the N-terminal region of target protein has a higher RMSF than the C-terminal region of transport protein complex. Due to ligand interactions, A2AAR -M1069 complex, the RMSF plot reveals that it experience a large fluctuations near residue index 10–25 and small fluctuations near 200–250. Similarly, in case of HSA-M1069 complex, almost all the residues exhibit more fluctuations, which indicates larger structural conformation during the simulation. Additionally, from Figure. 6, the gyration of radius indicates that both protein are stable while binding to the drug molecule.

Using principal component analysis on the molecular dynamics trajectory, the unique global and collective motions of Apo and complex proteins were identified. To further analyse the trajectory projections, a scatter plot involving the first two principal components (PC1 vs. PC2) was generated. In Fig. 7 (a & b), which corresponds to principal component analysis landscape of free HSA and HSA – M1069 complex. In which, free HSA is sparsely populated cluster in the negative side of PC1 and PC2 might represent rare or transient conformational states that the system occasionally encounters during its dynamics. These states could correspond to conformational intermediates between more stable states. If the system has multiple flexible regions or undergoes conformational changes, such as protein folding/unfolding., the PCA plot will show scattered points [32].

In case of A2AAR and its complex, the dense cluster in the negative side of PC1 and PC2 might represent the most prevalent or dominant conformational state of the system. The cluster might represent a structurally conserved region where the system maintains a relatively fixed conformation throughout the simulation [31].

3.3 DFT Analysis

3.3.1 Molecular Electrostatic Potential (MEP)

Molecular electrostatic potential has proven to be an invaluable resource for studying the connection between the three-dimensional structures of biomolecules and drugs and their physiochemical properties[42]. Figure. 8 Shows the electrostatic potential plot of M1069. The colors represent the varying electrostatic potentials of the surface, with red and yellow indicating the most favorable site for electrophilic reactive groups. The value of MEP lies in the fact that it color-codes regions of positive, negative, and neutral electrostatic potential on molecules at the same time, allowing for a more complete picture of the molecular charge distribution. Here, -C = O is the electronegative site (red) and -N-H is electropositive region (blue) in the drug molecule. The above-mentioned regions are the active binding site of the drug molecule which acts readily during molecular docking with the protein.

3.3.2 Frontier Molecular Orbit Analysis

The energies and atomic orbital assignments of the frontier molecules play a crucial role in computational chemistry. Frontier Molecular Orbitals (FMO) refer to the Highest Occupied Molecular Orbital (HOMO) and the Lowest Unoccupied Molecular Orbitals (LUMO). Chemical reactivity, kinetic stability, and presence of active site(s) can be illustrated with the help of position of these orbitals. The ability of a molecule to donate electrons is represented by the HOMO state (full) and the ability to gain electrons is represented by the LUMO state (empty). The difference between these orbitals i.e., ΔE = E_LUMO-E_HOMO is called as energy gap or frontier orbital gap. Based on the size of this gap one can predict the reactivity and stability of the molecule. To understand the bonding structure of the current compound, surfaces for the frontier orbitals are drawn. Figure. 9 Represents the HOMO and LUMO of the optimized drug molecule (FMO of the drug before docking). Here, the HOMO is spread over the central region of the drug molecule. Similarly, the LUMO occupies the lateral end and spread up to the center of the molecule.

The global chemical reactivity descriptors of the drug molecule like hardness, chemical potential, softness, electronegativity, and electrophilicity index have been defined by using the HOMO and LUMO energy values of the drug molecule [43–45]. The chemical potential is a global reactivity index that is directly related to the flow of electrons from systems with a higher chemical potential to those with a lower one. A molecule or atom with a positive electron affinity is called an electron acceptor and can undergo charge transfer reactions. The ability of a molecule to donate electrons is quantified by its ionisation potential, or the amount of energy required to eject an electron from the highest occupied molecular orbital. Table. Illustrates the global reactivity descriptors of the drug molecule before and after docking. In a molecule, the chemical potential (V) determines both the magnitude and direction of the electrophilicity index, which is a positive, quantifiable index. Because an electrophile is a chemical species with the ability to accept electrons from its surroundings, its energy must decrease when it does so. As a result, its electronic chemical potential must be negative. M1069 has a chemical softness of 0.42 eV and a chemical hardness of 2.37 eV; therefore, the ligand has more strength and has excellent chemical stability. Additionally, one can relate stability to its hardness, which implies that a molecule with a smaller HOMO-LUMO gap will be more reactive. After docking, the global parameters are varied due to its intermolecular interaction with the surrounding amino acid residues. From this, the overall DFT result suggests that there is a M1069 binding effect in atomic level distribution in the protein complex and shows the good intermolecular stability in all the protein molecule microenvironments.

Table 2

Globalized descriptors of M1069 before and after docking
Global Descriptors	M1069 before docking (in eV)	After docking M1069 with HSA (in eV)	After docking M1069 with A2AAR (in eV)
HOMO	-5.9353	-9.4187	-9.5308
LUMO	-1.1812	-9.0676	-9.2281
Ionization Potential I=[-E_HOMO]	5.9353	9.4187	9.5308
Electron Affinity A = [-E_LUMO]	1.1812	9.0676	9.2281
Global harness (ղ=(I-A)/2)	2.37705	0.17555	0.15135
Softness (σ = 1/ղ)	0.42068951	5.696382797	6.60720185
Chemical potential (µ = -[(I + A)/2])	-3.55825	-9.24315	-9.37945
Electrophilicity (ω = µ²/ղ)	5.326410072	486.6751462	581.2625193
Electronegativity (χ = (I + A)/2)	3.55825	9.24315	9.37945
Energy Gap (ΔE = E_LUMO−E_HOMO)	4.7541	0.3511	0.3027

3.3.3 Mulliken Population Analysis

In order to get a better grasp on the behaviors of molecules, it is essential to pay attention to a phenomenon known as charge transition, which occurs between atoms. The net charge associated with each atom in a molecule is represented by the mulliken population. A positive mulliken charge indicates that the atom has lost electron density and is electron-deficient, while a negative charge indicates an electron-rich atom. It can be used to understand the reactivity, bonding, and other properties of the molecule[46]. Figure. S1 represents the graphical plot of mulliken charges of drug molecule M1069 before and after docking. Here, before docking it is seen that C4 has high positive charge and C6 has very low mulliken charge. It infers that C4 will be withdrawn to electron deficit species while C6 is attracted by electron rich regions. But after docking the results suggest C19 has highly positive in both cases and N18 is negative. This may be due to the fact that there is intermolecular interaction between the drug molecule M1069 and protein structures HSA and A2AAR.

3.4 ADMET

Drug-likeness and in silico ADMET profiling are essential steps in the drug discovery process because they provide details on the characteristics of the ligand structure that are similar to those of drugs. Only if a compound succeeds in the drug-likeness prediction it i designated as a drug candidate. One method used to identify compounds with drug-like properties is Lipinski's rule of five. The number of hydrogen bond donors (nHBD), hydrogen bond acceptors (nHBA), Molecular weight (MW), the number of and molar refractivity (MR) and lipophilicity (mLog P) are the five phytochemical parameters that make up the rule of five. The compounds that are well absorbed by the human gastrointestinal tract are represented by an ellipse in a linear pattern which is created by the combination of the lipophilicity and polarity profiles[47]. ADMET properties of the drug described in table. 2 and 3. Here, in Table. 2 demonstrates that the selected compound has high gastrointestinal absorption (GIA), with WlogP values of 2.97 and TPSA values of 114.05 2. Also, this table shown absorption and permeability for compounds in the body occurs when a compound's molecular weight is 430.52 g/mol and its hydrogen bond acceptors and donors are 6 and 1, respectively. Additionally, logS were used to determine the solubility of water, and the results showed that it is moderately soluble. From the table. 3, We can conclude that the hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity of the ligand M1069 are inactive. M1069 is one of the antagonists for cancer cells, and the result indicates a 0.59 probability that the drug is carcinogenically inactive.

The yellow and white regions in Figure. S2 represent the passage of the drug through a specific pathway. Yellow indicates the drug's pathway through the blood-brain barrier (BBB), whereas white indicates the drug's pathway through the gastric intact [37]. The color codes blue and red for the ligand indicate PGP + and PGP⁻ respectively. In this plot, the drug M1069 is located in the white portion of the egg and it is PGP+, indicating that it will pass through the stomach undamaged and be eliminated through the excretion system. Using a combination of machine learning algorithms and expert principles, the ProTOX-2 software predicts potential toxicities, including acute toxicity, genotoxicity, carcinogenicity, and developmental toxicity. This allowed us to determine that the M1069 ligand has an average similarity of 36.2% and a toxicity class of 4, indicating that it is likely inactive.

Table.2 Estimated absorption and distribution properties as well as predicted "drug-like" properties

Compound

Lipinski Drug Like Veber Drug Like

Solubility

ABS Absorption & distribution

g/mol

MlogP

HBA

HBD

TPSA

Nrot

Class

(ESOL)

WlogP

GIA

Pgp

BBB

430.52

1.75

114.05

Moderately

soluble

-4.78

2.97

High

yes

Note: MlogP (Partitioning coefficient calculated by the Moriguchi et al., method used in the Lipinski drug-like criteria), MW (Molecular weight in g/mol), HBA (Hydrogen bond acceptors), WlogP (Partitioning coefficient calculated by the Wildman et al., the method used in BOILED-Egg), HBD (Hydrogen bond donors), Pgp (P-glycoprotein substrate) and BBB (Blood-brain barrier permeation),TPSA (Topological polar surface area in Å²), Nrot (Number of rotatable bonds), ABS (A bioavailability score in > 10% F), GIA (Gastrointestinal absorption)

Table.3 The compound's inhibition of metabolism and excretion by CYP450 isoenzymes and toxicity endpoints

Metabolism and Excretion

CY450 Inhibitors

Toxicity endpoints

1A2

2C19

2C9

2D6

3A4

Hepato

Carcino

Immuno

Mutagen

Cyto

Yes

0.58

0.59

0.97

0.64

0.69

Note: CYP450 (Inhibition of Cytochrome P450 isoenzymes), - (Inactive toxic class (probability score)), LD50 (Letal dose in 1000 mg/kg), and + (Active toxic class (probability score)).

According to our findings, the drug molecule M1069, which is currently under development to treat alpha-1 antitrypsin deficiency (AATD), a genetic disorder that can lead to lung and liver damage, can also be effective in treating cancer. It shows promising results when docked with the A2AAR and HSA proteins. The drug molecule's chemical structure and characteristics have not changed, as a result of the non-covalent interactions. The docking result indicate that M1069 forms a higher number of non-covalent interactions with both the A2AAR and HSA proteins. Molecular dynamics result infer that the system is stable when the drug molecule M1069 is binding with the A2AAR and HSA protein throughout 100ns simulation time, although there is some folding and unfolding of HSA during interaction. DFT studies also indicate that M1069 exhibits both acceptor and donor behavior which contributes to the very good binding with both proteins. From this work, we conclude that M1069 can effectively inhibit the action of the A2AAR protein and halt the angiogenic signaling process, resulting in regulated cell growth.

Acknowledgement

The authors thank VIT University for providing VIT RGMES SEED GRANT for carrying out this research work.

Ethical Approval

Not applicable

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Authors' contributions

1. Shajith Ahamed Azees : Software, Data curation, Data analysis, Manuscript writing

2. Manoharan Rupavarshini : Data analysis, Manuscript writing

3. Alanthata Govindan Navaneeth: Data analysis, Manuscript writing

4. Leon Bernet Shano : Data analysis, Manuscript writing

5. Subramani Karthikeyan : Editing, Corresponding author

Funding

No funding

Availability of data and materials

Data will be available upon request.

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Insight into the Binding Interaction Mechanism of the ligand M1069 with Human Serum Albumin and A2A Adenosine Receptor – A Biophysical Approach

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Abstract

Figures

1. Introduction

2.Methods

2.1 Ligand and Protein structure preparation

2.2 Molecular Docking

2.3 Molecular Dynamics

2.4 Principal Component Analysis

2.5 Density Function theory

2.6 ADMET

3.Results & Discussions

3.1 Molecular Docking

3.2 Molecular Dynamics

3.3 DFT Analysis

3.3.1 Molecular Electrostatic Potential (MEP)

3.3.2 Frontier Molecular Orbit Analysis

3.3.3 Mulliken Population Analysis

3.4 ADMET

4.Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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