3d Pharamacophore Model, Virtual Screening, Binding Affinity and Molecular Dynamics Studies of Potential Anticancerous Compounds Against Mammalian Target Rapamycin

doi:10.21203/rs.3.rs-3100361/v1

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3d Pharamacophore Model, Virtual Screening, Binding Affinity and Molecular Dynamics Studies of Potential Anticancerous Compounds Against Mammalian Target Rapamycin

https://doi.org/10.21203/rs.3.rs-3100361/v1

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Breast cancer is a genetically-based public health issue that affects people worldwide. The advancement of the FKBP12-rapamycin complex of the mammalian target (mTOR), the protein that is dysregulated in breast cancer, is essentially concerned with a hormone receptor. The mTORC1 and mTORC2 multiprotein complexes may be cleaved by mTOR, a serine/threonine kinase. mTOR's fap receptor movement is deregulated, which plays a part in cancer, weight, and maturing. The current study aims to acquire anti-cancer compounds from Naturally occurring Plant-based Anti-cancer Compounds (NPACT) as ligands against the target proteins of human breast cancer and identify promising hits against each target protein by analyzing ADME/T (Absorption, Distribution, Metabolism, and Excretion) properties for the chosen ligands and biomolecular interactions. Analyzing the biomolecular interactions, a pharmacophore model with a structure-based was created. and validated using Maestro 11.1. The best three compounds obtained after high-throughput screening are fap-363127 (taiwanin C), 5490349 (20-hydroxyresiniferol 9,13,14-orthophenylacetate), and 493164 (taiwanin E). The pharmacophore model displayed three main features: ARRR, Hydrogen bond acceptor, donor, and aromatic rings. Molecular Dynamics (MD) is a computer simulation technique for examining the physical fluctuations of molecular complexes using GROMACS (GROningen MAchine for Chemical Simulations). The explicit solvent model simulated the protein-ligand complexes for 100 ns. The RMSD (Root Mean Square Deviation) value of the mTOR-Benzothiazol complex ranges between 0.2 nm and 3 nm. The RMSF (Root Mean Square Fluctuation) and Rg (Radiation of gyration) are done by the highest binding compounds. This study suggests that the selected chemicals should be researched and analyzed further for breast tumor treatment and care strategies.

mTOR Proteins

Breast cancer

Molecular Docking

Virtual Screening

Pharmacophore model

Breast cancer is a complicated and multifactorial disease. The focus on protein, the mammalian target of rapamycin (mTOR) protein could be a profoundly moderated eukaryotic Serine/Threonine (Ser/Thr) protein kinase, which has a place to the phosphatidylinositol 3-kinase-related kinase (PI3K) family it appears the Fig. 1. Mammalian targets of rapamycin (mTOR) are highly moderated serine-threonine protein kinases included within the control of protein translation, interpretation, and control of cell development by detecting intracellular and extracellular vitality status within the shape of phosphorylation enactment (Zhang et al .2019). Since mTOR hindrance causes cell cycle capture at the G1 stage, the mediation on the mTOR pathway might give a noteworthy heading to unused sedate advancement pointed at the distinguishing proof of anticancer, anti-proliferative, anti-restenosis, and immune-suppressor operators. (Ortega, Miguel A., et al).

The proposed workflow phases are shown in Fig. 2. After the identification of the target protein, 573 compounds were collected from the Naturally Occurring Plant-Based Anti-Cancer Compound Activity-Target Database (NPACT) (https://pubchem.ncbi.nlm.nih.gov/). 573 components are mentioned in the supplemented file (573 ligands.xlsx). Ligand compounds are optimized by drug-likeliness properties, an important paradigm in drug discovery programs. (Patidar K. et al., 2019). The selected antimicrobial compounds were then analyzed for drug-relevant properties based on "Lipinski’s five rules.

Docking and the 3D pharmacophore demonstrate exceptionally imperative strategies for medication plans. These strategies will offer assistance in a protein-ligand interaction. These processes can predict the control of receptors and ligands by indicating the course of action of particles within a group. The mapping show is exceptionally helpful for analyzing the common properties of the authoritative bunch to decide what sort of inhibitor is authoritative for a target. To determine the key aspects of how a group of dynamic particles interacts with one another and how an organically dynamic ligand interacts with the target-binding site, pharmacophore mapping models resulting from structure-based and ligand-based processes are used. In addition to helping with hit extension and lead optimization, 3D models can also be used to find bioactive compounds through virtual screening techniques. (Muchtaridi, Muchtaridi, et al., 2017). The atomic energetic simulations on molecular screening results create a more reliable component for describing ligand-protein interactions. Complexes' energetic characteristics have been contrasted in building flexibility, screening score, salt crossover, and hydrogen bond intelligence. when developing at various temperatures in official locale and accessory forms Utilizing GROMACS, examine the deviation change and gyration movement in atomic flow. (Yi Fu.,2018).

2.1. Molecular Docking Using Maestro 12.8

Using the maestro 12.8-Schrodinger program, the molecular interaction between the anti-cancer chemicals from plant extract determined the target protein. The binding affinity and RMSD values were calculated between the ligands and protein molecules. Following the synthesis of the receptor and ligands, molecular docking analysis was carried out using Maestro 12.8, a scoring function-based approach. The Maestro 12.8 exhaustive search docking program was utilized to determine binding affinity and pharmacophore mapping.

2.1.1. Protein Identification and Pre-processing

The rapamycin receptor protein's structure was retrieved for this analysis from the Protein Data Bank, RCSB (http://www.rcsb.org). The PDB structure contains a variety of missing details about a particular arrangement, close to bond ordering and formal alterations. The protein organizing wizard in Maestro imports the protein structure from PDB (Patidar K et al., 2019). The tactics for creating heteroamorous states, identifying H-bonds, and reducing the level of the structure all depend on this pre-processing. They are setting bond requests, filling in circles, counting hydrogen, removing water particles, and counting hydrogen.

2.1.2. Hydrogen bond optimization

Both the computerized optimization, which applies to all hydrogen bonds, and the intelligent optimization, which can successfully optimize different clusters of hydrogen-bonded species. With a regulated decrease, the design was lessened primarily to refine the structure. Additionally, the Fluid Simulations_2005 (OPLS_2005) constraint field's optimized possibilities were used to successfully compute the portion of nuclear charges. (Sahayarayan JJ et al., 2021).

2.1.3. Ligand structure preparation and setting ionization.

The structure of the ligand layout (LigPrep) is a complicated combination of options intended specifically to generate top-of-the-line all-atom 3-dimensional forms for a number of drug-like components, starting with two- or three-dimensional structures from the Maestro or SDF structure. Swiss ADME (http://www.swissadme.ch/) was utilized to compute the compounds' atomic acreage connected to drug-likeness and kinetics (retention, diffusion, digestive system, and excretion). Utilizing the OPLS_2005 constrain field, partial nuclear charges were accurately estimated inside the large-scale demonstration's optimized constrain field shape. The stated particular pH run essentially populates the Produced possible states. For the input ligand, the FKBP12-rapamycin complex was developed. Three steps are involved in setting a stereoisomer: 1: Hold specific chiralities 2. Chirality incorporates bonds and equality, bond directions from SD (Structure Data Files), and 3. Atom counts and chirality properties of Maestro files

2.1.4. Receptor grid generation

The target grid creation can be used to generate the common grid box. Docking a ligand before the lattice arrangement is organized is impossible. This group comprises the Rotatable, Receptor, Location, and imperative bunches. The van der Waals span of non-polar scaling molecules can be used to simulate the receptor and the ligand, which lessens the penalties for near intelligence. The fractional charge cut-off default rate is 0.25 L²atm/mol², and the scaling figure set rate is 0.30 L²atm/mol². The Van der Waals radius scale calculation and the charge scale figure both have default values of 1.0 L²atm/mol². (Sahayarayan JJ et al., 2021).

2.1.5. GLIDE® formation

One of the Schrödinger suites, GLIDE (Grid-based Ligand Docking with Energetic), is utilized for the docking analysis. GLIDE looks for places where ligands and proteins can interact well. It searches for specific ligand sites in the protein molecule's active site using a hierarchical sequence of filters. Every core conformation has orientations, and the target protein's active site is searched for potential places. Only decent ligand poses should be used with GLIDE XP. Only the best ligands have been docked in XP mode. GLIDE docking can be completed utilizing Ligand Docking, Ligand Structure Preparation, Receptor Grid Generation, and Protein Structure Preparation.

XP Glide Score = E_{coul +}E_vdW+ E_{bind +} E_penalty

XP Glide Score is energy terms of summation, such as Coulomb, van der Waals, binding, and penalty

E_bind =E_{hyd enclosure}+ E_{hb nn motif} +E_{hb cc motif} +E_PI+ E_{hb pair} +E_{phobic pair}

E_penalty=E_desolv + E_{ligand strain}

E_bind includes all energetic variables that influence docking, such as the energy parameters of hydrogen bonds, while Epenalty includes terms that interfere with screening, such as energies of desolation and strain of ligand. (Friesner et al., 2006).

2.1.6. Structure-based virtual screening

For the purpose of discovering dynamic compounds, virtual screening can be helpful. The Schrodinger suite, Glide, and the docking tool for the virtual screening workflow were used to conduct this screening investigation into the dynamic targets. Numerous compounds are evaluated against a single target using this screening technique. The agreement also covers the use of LigPrep to prepare ligands. High-Throughput Virtual Screening (HTVS) docking is very beneficial for docking a few biomolecules. A helpful technique for docking a large number of bio-components with uncertain quality is Standard Precision (SP) docking. The Extra-Precision (XP) docking approach and value analysis showed promise, appearing to be a straightforward process that often takes longer to operate than SP docking.

2.1.7. Energy Minimization

According to a few computational demonstrations of chemical holding, the method of finding a trajectory of action in an area for a selection of molecules and the net inter-atomic drive present at each molecule's location on the Potential Energy Surface (PES) and near its zero point are both acceptable could be regarded as stationary, which is known as energy minimization.

2.1.8. Toxicity

In order to determine the security profile of the required substances, a computational technique has made it possible to assess toxicity using a method in silico. Additionally, these substances could be harmful to both humans and animals. Mutagenicity, carcinogenicity, and immunotoxicity can all be evaluated and decided upon objectively and qualitatively using harm profiles. Additionally, ProTox Server was used to determine how hazardous the five chemicals were. (http://tox.charite.de/protox_II). (Opo et al., 2021).

2.1.9. Ligand-based pharmacophore mapping

Virtual ligand database docking and the creation of pharmacophore characteristics are the two components that makeup pharmacophore mapping. Modeling and capacity of the tentatively approved rapamycin receptor in pdf format Once the pharmacophore location has been determined, the client is sent to the next step of seeking out a normal pharmacophore method. Sometime recently, when it was submitted to the library ligand opposite the NPACT database through HTVS mode, it was, to begin with, evaluated for the biggest common theory and the score, illustrating the common pharmacophore characteristics. (Pal S et al., 2019).

2.1.10 Molecular Dynamics Simulation

The simulation also provides details about intermolecular interactions in a given time frame. In this study, the complex docking flights of A reference antagonist and four natural substances bound to the fap receptor were examined using methods for molecular dynamics simulation to validate the consistency of interactions among molecules and proteins throughout a time span of 100 ns. The MD trajectory was extracted using the GROMACS, and the simulation result was described using RMSD, RMSF, and Protein-Ligand (P-L) interaction mapping (Yi Fu et al., 2018). The following RMSD formula is

3.1. Protein preparation of Rapamycin -FAP protein

The Schrodinger program was used to construct the rapamycin protein's crystal composition, which was then obtained from the Protein Data Bank (FAP). The protein-heavy atoms received hydrogen atoms, which were then tuned. The retrieval of protein contains water molecules, and its energy is 752.84 kcal/mol in Fig. 3(A). Cofactors and water molecules were eliminated, and the energy was optimized at 340.74 kcal/mol, as shown in Fig. 3(B). With atoms connected to the backbone, the Optimized Potentials for Liquid Simulations (OPLS-3) force field is also used to minimize energy in proteins.

3.2. ADME properties predictions

The target compound's ADME characteristics were examined. In the development of medications, forms including retention, dissemination, digestion, and excretion were crucial to consider. They were included in Table 1. Eight compounds were examined in this study using several databases to check for pharmaceutically significant properties, and qualities that indicated drug-likeliness were shown in Table 2. Using Lipinski's run-the-show of five rules, the ADME properties were examined using the Schrödinger program suite. (Prakash P et al 2021).

Table 1

Pharmacophore drug-likeness properties of components
S.NO	Component ID	Component name	qPlogPW	%Human oral absorption	Donor HB	Acceptor HB
1	363127	Taiwanin C	8.496	100%	0	6
2	5490349	20-hydroxyresiniferol 9,13, -14-orthophenylacetate	12	100%	2	7
3	493164	Taiwanin E	10.566	94%	1	1
4	177302	Helioxanthin	8.739	100%	0	6
5	10264769	Neo-tanshinlactone	6.109	82%	0	0
6	354689	Benzothiazol	5.838	100	1	2

Table 2

**List of pharmacokinetics properties (physic-chemical, lipophilicity, water solubility, drug-likeness, and medicinal chemistry) of docked ligands**.
Properties	Parameters	363127	5490349	493164	177023	10264769	354689
	MW(g/mol)	348.31	464.55	364.31	348.31	264.28	343.44
	Heavy atoms	26	34	27	26	20	25
	Atom heavy atoms	16	6	16	16	17	21
Physico-chemical	Rotatable bonds	1	4	1	1	0	4
Properties	H-bond acceptor	6	6	7	6	3	2
	H-bond donors	0	2	1	0	0	1
	Molar Refractivity	90.83	126.29	92.86	90.83	79.69	104.54
Lipophilicity	Log P(o/w)	3.00	2.73	2.79	2.99	2.97	3.54
Water solubility	Log S(ESOL)	Soluble	Soluble	Soluble	Soluble	Soluble	Soluble
Pharmacokinetics	GI absorption	High	High	High	High	High	High
Drug-likeness	Lipinski, Violation	0	0	0	0	0	0
Medi.chemistry	Synth. Accessibility	2.94	6.89	3.06	2.96	3.35	3.75

3.3. Toxicity study

In-silico harmfulness assessment is a crucial process for a more accurate lead compound determination as a medication candidate lately enters clinical trials. A computational-based toxic quality estimation has become widely used due to its accuracy, speed, and availability in providing data on practically any union or normal molecule. Using the free and open-source ProTox-II program, we assessed the risky potential and detrimental effects of the five compounds that were selected based on the docking score. Predicted intermediate Lethal Dosage (LD50) in mg/kg weight, each computer program was used to evaluate a few toxicological criteria, including intense poisonous quality, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, and immunotoxicity are depicted in Table 3 .

Table 3

List of toxicity properties (Organ Toxicity, Toxicity Endpoints, Tox21-Nuclear receptor signaling pathways, Tox21-Stress response pathway, Developmental toxicity, Oral rat LD50, Bioaccumulation factor) of the selected 6 compounds.
ENDPOINT	Target	363127	5490349	493164	177302	10264769	354689
Organ toxicity	Hepatotoxicity	Active	Active	Inactive	Inactive	Inactive	Active
Toxicity endpoints	Carcinogenicity	Inactive	Inactive	Active	Active	Active	Inactive
	Immunotoxicity	Active	Active	Active	Active	Inactive	Inactive
	Mutagenicity	Inactive	Inactive	Inactive	Active	Inactive	Inactive
	Cytotoxicity	Inactive	Inactive	inactive	Inactive	Inactive	Inactive
	Toxicity class	5	4	5	5	5	5
Tox21-Nuclear receptor signaling pathways	Androgen Receptor (AR)	Inactive	Inactive	Inactive	Inactive	Inactive	Inactive
Tox21-Nuclear receptor signaling pathways	Aryl hydrocarbon Receptor (AhR)	Inactive	Inactive	Inactive	Inactive	Active	Inactive
Tox21-Stress response pathway	Heat shock factor response element	Inactive	Inactive	Inactive	Inactive	Inactive	inactive
Oral rat LD50	mg/kg	2100	590	2100	2200	2500	2700
Bioaccumulation factor	Log10	3.63	3.21	3.34	NA	4.31	5.25

3.4. Docking studies

In this study, the ligand that was present in the fap receptor model served as the basis for active site formation. The values of the docking interaction between the target and the anti-cancer drugs are displayed in the filtered components. Binding affinity between the compounds ranges from − 11.1 to -5.9 kCal/mol are mentioned in the supplemented file (DOCKED RESULTS.xlsx). In order to create a chemical that works as a therapeutic, hydrogen bonds, ionic bonds, and hydrophobic bonds that occur between proteins and their ligands are crucial. The ligands with the highest docked scores are listed in Table 4 along with the amino acids that make up their binding sites.

Table 4

Structure-based virtual screening
S.NO	Compound PUBCHEM ID	Glide score Standard precision (kcal/mol)	Glide energy (kcal/mol)	Interaction residues
1	363127	-11.1	-54.783	LYS_2095, TYR_2105, ASP_37
2	5490349	-10.8	-64.060	ASP_37, TY_26, PHE_46
3	493164	-10.6	-57.871	TYR_26, PHE_46, TRP_26
4	177023	-10.6	-53.151	TRP_59, TYR_26, ASP_37
5	10264769	-10.5	-42.160	GLU_54, VAL_55, TYR_26
6	354689	-10.2	-58.115	TYR_2105, PHE_2039, TRP_59

Table 5

Interaction results between the selected 6 ligands in complex with the protein mTOR
Compounds ID	Conventional hydrogen bonds	Pi donor hydrogen bond	Alkyl bond	Carbon hydrogen bond	Pi-sigma	Pi-alkyl bond
363127	TRP_59, TYR_2105	PHE_2108	-	PHE_2039	TYR_82	LYS_47, ARG_2109
354689	TYR_269, PHE_2039	TYR-82, PHE_46	TRP_56	ASP_37	-	ILE_90, VAL_55
493164	ARG_57, THR_85	TYR_82	-	PHE_2039	TRP_2101	TYR_82
10264769	-	PHE_2108	-	PHE_2039	-	LYS_47, ARG_2109
177023	ARG_2042, ARG_57	-	ASP_79	-	ASP_2109	GLU_54
354689	-	TYR_26	-	TRP_54	PHE_2039	ILE_56, VAL_55

Table 5 depicts the Hydrogen bond interaction with the length of the Protein-ligand Complex and the distance ranges from 2.0 to 3.5 Å. Then it indicates that amino acids tyrosine, tryptophan, and phenylalanine binds to the active site regions of the protein.

The highest binding affinity of the protein-ligand complex’s 2 D and 3 D interactions are visualized from the Figs. 4 to 9 individually.

3.5. Pharmacophore Model

Using predetermined chemicals, the four-point pharmacophore model ARRR was created. It has one acceptor (A4) and three aromatic rings (R9, R10, and R11). The pharmacophore models have four points, as displayed in Fig. 10. Based on the docking score, they show the top five selected ligand mappings in Fig. 11. The top five ligands were superimposed on the pharmacophore model to emphasize the significance of the electronic characteristics in the receptor-ligand interaction. This demonstrated the relationship between the pharmacophore's site points and functional groups.

Molecular Dynamics (MD) is a computer simulation of the in-silico technique for examining the physical activity of ligands and proteins. Here, the MD simulation has been done using GROMACS. The explicit solvent model simulated the protein-ligand complexes for 100 ns. MD simulation has predicted RMSD, RMSF, and Radius of Gyration (RG) protein complexes. Molecular Dynamic studies have been studied for mTOR (fap)-Taiwanin C and mTOR (fap)-(1,3-Benzothiazol-2-yl)-1,3-diphenylprop-2-en-1-ol complexes.

4.1. RMSD analysis

The RMSD of a molecular complex system, which is the sum of the average distance caused by the activity of a specific ligand over a specified total time, reveals the fitness of a protein. In the 100 ns dynamics run depicted in Fig. 12, the mTOR-Benzothiazol complex varies between 0.2 and 3 nm, while the mTOR-Taiwanin C complex varies between 0.2 and 4.5 nm.

4.2. RMSF analysis

The resulting RMSF information can be utilized to estimate the local conformational change of a protein associated with ligands as well as the average observed atomic alterations in relation to the number of atoms. Figure 13 shows the RMSF plots of mTOR-Ligand complexes.

4.3. RG ANALYSIS

The radius of gyration is calculated as the last part of the stability analysis. This measurement indicates the shape of the molecule at any given time. The radius of gyration is comparable to the hydrodynamic radius available experimentally. The program also outputs the individual components that correspond to the eigenvalues of the inertial matrix. That is, the first single component corresponds to the longest axis of the molecule, and the last component corresponds to the smallest axis. Figure 14 shows the RG plots of mTOR-Ligand complexes.

This work adopted a rational strategy based on in silico prediction capacity for suppressing cancer cell transformation to focus on mTOR. Lipinski's run the appearance of five (RO5) is used to illustrate the drug-like properties of the collected substances. The chosen compounds were discovered to exhibit outstanding drug-like characteristics, according to Lipinski's Appear of Five, which evaluates the drug-like characteristics of chosen compounds to determine whether they are orally bioavailable for individuals. The top five compounds with the highest official partiality were chosen from the compound collection based on their atomic docking score. The molecules Taiwanin C, 20-hydroxyresiniferol 9,13,-14-orthophenylacetate, Taiwanin E, Helioxanthin, and Neo-tanshinlactone had the highest lifted holding, with respective authoritative scores of -11.1 kcal/mol, 10.8 kcal/mol, -10.6 kcal/mol, -10.6 kcal/mol, and − 10.5 kcal/mol, respectively. The ligand-based pharmacophore mapping medication design handle was used in this concept, which also contained lead recognizable proof, virtual hits profiling, pharmacophore-based virtual screening, and pharmacophore modeling and validation ARRR. Analysis was done on the most dynamic and least energetic particles found in the official receptor-ligand location. The complex mTOR-ligand complexes underwent additional atomic energetic analysis, and the RMSD for the mTOR-Benzothiazole complex exhibits a range of 0.2–3 nm. The RMSF is examined to determine which docked ligand has the complex's nuclear charges at their highest level. Utilizing Rg, protein-ligand stability was determined. Therefore, the study suggested that the selected chemicals be examined and evaluated for use in breast cancer treatment and dosing systems. According to a few computational demonstrations of chemical holding, the method of finding a trajectory of action is a collection of molecules, molecular interactions refers to a situation when the net inter-atomic drive on each molecule is acceptable to near zero and the position on the PES might be a fixed location.

Acknowledgment

The authors are thankful to the Principal and Management of K. S. Rangasamy College of Technology (Autonomous), Tiruchengode, Tamil Nadu. And acknowledge DST FIST (Fund for Infrastructure for Science and Technology) FIST No: 368 (SR/FIST/College–235/2014) and DBT-STAR College Scheme (BT/HRD/11/09/2018) for their support for the instrumentation.

Competing Interest

We have no conflicts of interest to disclose. All authors declare that they have no conflicts of interest.

Author Contribution

Aarthi S¹ - A. Prepared the main manuscript content

Keerthana T² - Prepared bio molecular interaction studies content, figures and tables

Puniethaa Prabhu- Prepared molecular dynamics simulation studies content, figures and tables. All authors reviewed the manuscript

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No competing interests reported.

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3d Pharamacophore Model, Virtual Screening, Binding Affinity and Molecular Dynamics Studies of Potential Anticancerous Compounds Against Mammalian Target Rapamycin

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Abstract

Figures

1. INTRODUCTION

2. Materials And Methods

2.1. Molecular Docking Using Maestro 12.8

2.1.1. Protein Identification and Pre-processing

2.1.2. Hydrogen bond optimization

2.1.3. Ligand structure preparation and setting ionization.

2.1.4. Receptor grid generation

2.1.5. GLIDE® formation

2.1.6. Structure-based virtual screening

2.1.7. Energy Minimization

2.1.8. Toxicity

2.1.9. Ligand-based pharmacophore mapping

2.1.10 Molecular Dynamics Simulation

3. Result and Discussion

3.1. Protein preparation of Rapamycin -FAP protein

3.2. ADME properties predictions

3.3. Toxicity study

3.4. Docking studies

3.5. Pharmacophore Model

4. Molecular Dynamics Simulation

4.1. RMSD analysis

4.2. RMSF analysis

4.3. RG ANALYSIS

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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