An in-silico approach to identify novel Akt1 (protein kinase B- alpha) inhibitors as anticancer drugs

doi:10.21203/rs.3.rs-3908533/v1

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An in-silico approach to identify novel Akt1 (protein kinase B- alpha) inhibitors as anticancer drugs

https://doi.org/10.21203/rs.3.rs-3908533/v1

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Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e., pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38 including two hydrogen bond acceptors (A), hydrogen bond donor (D), and two aromatic rings (R). The obtained R² and Q² values are in the acceptable region with the values of 0.90 and 0.64 respectively. The generated pharmacophore model was employed for virtual screening to find out the potent Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogs by comprising the structural characteristics of the hits acquired from the database. Among the new series, 10 molecules were found to have strong binding interactions and binding free energy values which are comparative and even higher than the screened hits as well as higher than the active compound retrieved from the Asinex. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex. These results suggested that the newly designed molecules are extremely useful for further lead optimization to explore a greater number of compounds in the drug development process.

Pharmacophore-based virtual screening

Akt1 Inhibitors

QSAR model

molecular docking

Binding free energy and Molecular dynamic simulation

The serine/threonine kinase Akt1, also known as protein kinase B (PKB) belongs to the AGC kinase family that functions as an essential node in the PI3K/Akt1/mTOR signaling cascade and is responsible for diverse physiological processes including cell survival, growth, proliferation, metabolism, and angiogenesis [1, 2]. Akt1 consists of three highly homologous isoforms namely Akt1 (PKB-α), Akt2 (PKB-β), and Akt3 (PKB-ϒ), and each displays specific patterns of expression in both normal and abnormal cells [3]. A wide number of human cancers, including prostate, breast, lung, ovarian, pancreatic, and hematologic malignancies, have been discovered to have abnormal Akt1 activation [4–7]. Furthermore, overexpression of Akt1 has been linked to a poor prognosis and resistance to chemotherapy and targeted therapies like BRAF and EGFR kinase inhibitors [8, 9]. As a result, inhibiting Akt1 has great potential in terms of cancer treatment. To date, a number of Akt1 inhibitors, i.e MK-2206, AZD5363, GDC-0068, ARQ-092, BAY1125976, Uprosertib, and Afuresertib have been evaluated in advanced cancer clinical trials, demonstrating the potentiality of Akt1 inhibition for cancer therapy [10]. The chemical structures of Akt1 inhibitors are outlined in Fig. 1.

Recently, various chemical entities were reported (Fig. 2) for Akt1 inhibition like HCl salt of (E)-6-methyl-7-(4-(trifluoromethyl)benzylidene)-4-(4-(trifluoromethyl)phenyl)-1,4,4a,5,6,7, 8,8a-octahydropyrido[4,3-d]pyrimidin-2-amine (I)[11], N-((3S,4S)-4-(3,4-difluorophenyl) piperidin-3-yl)-2-fluoro-4-(1-methyl-1H-pyrazol-5-yl)benzamide (II)[12] and 2-(4-chlorophenyl)-3-(cyclopentylamino)-1-(4-(6,7-dihydro-5H-cyclopenta[4, 5]thieno[2,3-d]pyrimidin-4-yl) piperazin-1-yl)propan-1-one (III)[13]. In addition, a few recent literatures demonstrated the identification of novel Akt1 inhibitors by using several in-silico and experimental approaches [14–16]. For instance, Xiaowu Dong et al. reported the pharmacophore-based modeling studies of various indole and rational analogues followed by molecular docking studies to design novel Akt1 inhibitors using Catalyst/HypoGen program. They even synthesized and screened the 7-hydroxy-6-(3-methylbut-2-en-1-yl)-2-phenyl-4H-chromen-4-one analogues against Akt1 inhibition activity and two molecules (IV, V) were found to be potent Akt1 inhibitors [17]. Structurally diverse Akt1 inhibitors have been computed by using conventional QSAR analyses and molecular binding studies to achieve the highly potent Akt1 inhibitors [18]. Further, pharmacophore modeling studies of Akt1 inhibitors have been reported by Oya G Y et al. They identified potent Akt1 inhibitors by employing ligand-based and structure-based pharmacophore modeling [19]. Hence, pharmacophore and 3D-QSAR modeling techniques are many useful tools to identify potent and selective Akt1 inhibitors. In view of this, we designed new novel Akt1 inhibitors by using a few computational techniques such as atom-based pharmacophore modeling, prime MM/GBSA, molecular docking, and in-silico ADME analysis.

2.1 Dataset

To generate the pharmacophore model, a library of 85 molecules containing different scaffolds with Akt1 inhibitory activity was retrieved from the published literature and the pIC₅₀ values are in the range from 9.00 to 3.73[20–27]. These molecules were divided into two sets: training and test sets based on the distribution of pharmacological activities as well as chemical properties. Molecules in the test set were used for internal validation and the structural characteristics of the molecules in the training set served as a template for developing the QSAR model. To build pharmacophore site points, inhibitors with pIC₅₀ values larger than 7.5 were classified as active, while those with pIC₅₀ values less than 5.5 were classified as inactive. The pIC₅₀, binding energy, and fitness scores of all the data set compounds are depicted in Tables 1–4.

2.2 Ligand preparation

The pharmacophore model generation requires a 3D structure of all atoms that accurately depicts the molecular structure employed in the experiment. Due to the flexibility of the majority of the ligands, it was important to investigate a wide range of thermally accessible conformational states to maximize the chances of discovering anything near the probable binding mode. Initially, the 2D structures of all the molecules were drawn in the maestro build panel and then converted to a 3D model, and these were subjected to ligand preparation by using the Ligprep module[28]. Finally, in the project's phase process low-energy conformers and activity levels are imported. In order to generate statistically robust models, the ligands are allotted active and inactive status by applying a sufficient activity threshold value.

2.3 Pharmacophore model generation

The ligand structure is defined by a set of locations in 3D space that promote non-covalent interactions between the ligand and its target receptor. Generally, PHASE offers a built set of six pharmacophore features to develop the pharmacophore model: H-bond donor (D), H-bond acceptor (A), hydrophobic group (H), aromatic ring (R), negative/anionic (N), and positive/cationic (P)[29]. It also provides the potential pharmacophoric sites for a given dataset based on structural similarities and shared pharmacophoric characteristics. To determine physical qualities, created features by the PHASE will be assigned to geometrical entities in the data set. PHASE estimated the conformations of all ligands with identical sets of properties and related spatial arrangements into a single pharmacophore. A tree-based partitioning technique is applied to find out comparable pharmacophores based on their inter-site distances [30].

2.4 Scoring hypotheses

Generally, the scoring procedure aids in determining a drug from a set of active ligands that would possess the best linkages. The obtained pharmacophore establishes a hypothesis that reveals the binding nature of active ligands to their receptors. The scoring algorithm included the contributions from the alignment of site points and vectors site, volume score, selectivity, number of ligands matched, and activity. Based on the analysis of the scores and the matching of the effective ligands to the created hypothesis, the optimal pharmacophore hypothesis was chosen.

2.5 Building QSAR model

The QSAR model in PHASE studies can be developed by employing the activities of the ligands that are matched with the presented hypothesis. The QSAR models were developed using PLS regression and used on a group of binary variables. The selection of the generated model is primarily determined by whether or not the training set molecules are fixed similarly and sufficiently. Atom-based QSAR models were developed for the Akt1 inhibitors activities using the best 5-point AADRR hypothesis with 49 training set ligands and keeping the grid spacing value at 1.0 Å. Developed QSAR models with one to three PLS variables, this factor was generated using training and test ligands.

2.6 Virtual screening

Virtual screening (VS) is a drug discovery technique used to predict the potential hits from large chemical databases and also searches for structures that bind to a drug receptor or enzyme [31]. As a 3D query, we employed the atom-based pharmacophore and the best 3D-QSAR pharmacophore to find potent compounds against Akt1inhibitors [32]. To find pharmacophore matches, the database molecules are used to search and match a minimum of four sites of five features of the hypothesis. By utilizing the generated pharmacophore model (AADRR38) of PHASE, Pharmacophore matched molecules separated from the total 91,453 Asinex elite synergy database compounds without known Akt1 inhibitors [33].

2.7 Molecular docking and binding free energy calculations

Molecular docking helps to identify how compounds bind to the target receptor and leads to drug design. X-ray crystal structure of Akt1 (PDB ID: 4EJN) was downloaded from the RCSB (protein data bank: https://www.rcsb.org/). Protein was used for the protein preparation process using the “protein preparation wizard” of Maestro. Using the OPLS2005 force field, protein structure-controlled energy minimization and geometry optimization were performed. The receptor grid generation module in Glide 5.6 (Schrodinger, LLC, New York, NY, 2012) was used to define the active site for docking ligands. Grid was generated (0R4 in Akt1, 4EJN resolution 2.19Å), by selecting the cocrystallized ligand and the vander Waals scale was limited to 0.9. Grid box dimensions were fixed at 10Å x 10Å x 10Å. All low-energy conformation of ligands were docked into the generated grid[34]. Extra precision (XP) docking protocol was selected to analyse binding mode and interaction studies.

The computation of the binding free energies of receptor-ligand complexes was performed using Molecular Mechanics with Generalized Born Surface Area (MM/GBSA)[35]. The calculations were performed based on molecular docking. The output file containing all the docked complexes generated after XP docking was imported to Prime wizard in the Schrodinger suite and utilized for MM-GBSA calculations. Using the OPLS-2005 force field, the calculation of binding energy change for a group of receptors and ligands was determined.

${\varDelta G}_{bind}$ bind was estimated by following equation

$${\varDelta G}_{bind}={E}_{complex\left(minimized\right)}-\left[{E}_{ligand\left(unbound,minimized\right)}+{E}_{receptor\left(unbound,minimized\right)}\right]$$

where ${\varDelta G}_{bind}$ is the calculated relative free energy that includes both ligand and receptor strain energy, ${E}_{complex\left(minimized\right)}$ is the MM/MBSA energy of the minimized complex. ${E}_{ligand\left(unbound,minimized\right)}$ is the MM/MBSA energy of the ligand after removing it from the complex and allowing it to relax, ${E}_{receptor\left(unbound,minimized\right)}$ is the MM/MBSA energy of protein after separating it from the ligand [36].

2.8 Molecular dynamic (MD) simulation

Molecular dynamic simulation (MD) is a technique to analyse the confirmation changes of a whole protein–ligand system for a certain time. MD simulation for a period of 50 ns was performed for one of the selected ligands and 4ejn complex obtained from the docking studies by using WebGro server, a GROMACS based software [37]. The required parameters for the ligand i.e., ligand topology was generated with the PRODRG server and the GROMOS96 43a1 force field was used. The SPC water solvation model was implemented in the triclinic system and neutralized by adding an appropriate amount of Na⁺ and Cl⁻ counter ions. Then, the 4ejn and ligand complex energy was minimized by using the steepest descent integrator in presence of solvent at every 5000 steps. The NVT/ NPT equilibration was performed at 300 K temperature and 1.0 bar pressure. Further, the Leap-frog MD integrator was taken for a simulation time of 50 ns and the number of frames was fixed to 1000. The resulted MD trajectories were analysed with the help of Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of gyration (Rg) and Solvent Accessible Surface Area (SASA).

2.9 In silico ADME prediction

In drug development, ADME (absorption, distribution, metabolism, and excretion) and drug-likeness properties are the most significant parameters, which are validated as one of the primary causes of failure for the potential bioactive leads [38–40]. QikProp module was used to predict the ADME and another ten pharmacokinetics properties viz. aqua solubility (QPlogS), the partition coefficient (QPlogPo/w), polar surface area (PSA), number of hydrogen bond donors/acceptors, caco-2 cell permeability (QPPCaco), human serum albumin binding (Qplogkhsa), human oral absorption (%HOA) and blood/brain partition co-efficient (QPlogBB) of the hits and newly designed compounds.

3.1 Determination of pharmacophore model

In the common pharmacophore identification process three variant combinations were derived such as AADRR, ADRRR, and DRRRR encompassing of H-bond acceptors (A), H-bond donors (D), and aromatic rings (R). After rating these three common pharmacophores, 22 five-point pharmacophore hypotheses were generated. The survival active, inactive, vector, and volume scores were used to rank the generated hypotheses. For further investigation, the hypothesis AADRR associated with five pharmacophore site points taken into consideration and the geometry of AADRR is described in Figure 3. A1 and A2 spheres represent the H-bond acceptor feature, whereas D5 sphere represents the H-bond donor, and R12, R15 represents aromatic ring features.

3.2 Validation of 3D-QSAR model

Using atom-based analysis, the top three pharmacophore hypotheses were built as 3D-QSAR models. In the PLS regression approach, the pIC₅₀ values used as dependent variables and the maximal PLS factors with PHASE descriptions used as independent variables. Table 5 summarises the detailed results of the 3D-QSAR models (Akt1) based on test and training set selection. Out of the top three hypothesis, AADRR gave good statistical model with high values of correlation coefficient; R² = 0.90, low standard deviation; SD = 0.37, variance ratio; F = 137, high predictive coefficient; Q² = 0.64, low RMSE = 0.56 and Pearson’s R value = 0.78. As a result, the 3D-QSAR model that was created had excellent statistical criteria and might be utilised for future optimization and development. Figure 4 displays the scatter plot of the experimental versus predicted pIC50 values for the training and test set ligands. The graph revealed that the predicted and experimental values had a positive association. As a result, the robustness and significance of the developed 3D-QSAR model may be validated.

3.3 3 D-QSAR visualization of best active compound

In atom-based 3D-QSAR model, steric incompatibilities with target receptors will be prioritised over pharmacophoric characteristics to predict the activity of screened compounds. Whereas the activity in pharmacophore-based 3D-QSAR model will be predicted based on the pharmacophore's sites and locations. Generally, the 3D-QSAR visualization aids in a better understanding of the structure-activity relationship (SAR) while evaluating the activity of screened hits. The 3D-QSAR visualization of the best active molecule 79 (pIC₅₀=9.00) is shown in Figure 5. In Figure 5a, the green cubes signified the hydrogen bond donor region where as hydrogen bond acceptor region was showed by blue cubes and it shows the existence of H-bond donor characteristics at NH group of fused pyrazole ring, NH group of substituted aniline and pyrrolidine -NH. However, there is only one donor (D5) in the hypothesis. Two H-bond acceptor features were identified, which was validated with the hypothesis (A1&A2): one is on the Br attached to pyrazole and one more is on the piperazine ring. In Figure 5b, the green cubes represent the hydrophobic favoured region and the disfavoured region is indicating by orange cubes. The NH of pyrazole ring, benzene ring, NH of pyrrolidine ring, and the NH of piperazine ring are the four hydrophobic characteristics that can be seen superimposed. Surprisingly, additional H-bond donors, acceptors, and hydrophobic characteristics are particularly beneficial for the activity.

3.4 Pharmacophore-based virtual screening

In drug development, virtual screening is one of the most promising approaches in which the pharmacophore-based virtual screening of existing molecular libraries used to find a novel and potential entities for further development. 91,453 Molecules were taken from the Asinex elite synergy database and screened by applying the generated pharmacophore model (AADRR). As a result, 5960 hits were retrieved for efficient Akt1 inhibition. In the present work, the ligands were examined to match all the five features of the hypotheses. During this process, the screening hits obtained from the pharmacophore-based virtual screening are intelligible and will undoubtedly have a significant possibility to function as Akt1 inhibitors.

3.5 Molecular docking-based virtual screening

Molecular docking studies were performed against the hits from Asinex elite synergy database and the active site of Akt1 receptor by using two docking techniques: standard precision (SP) and extra precision (XP). Initially, 5960 molecules were subjected to SP docking in which 2% of hits (115 molecules) exhibited high dock score and that are computed to XP docking. Finally, the resulted 11 molecules with high dock score were directed to binding energy calculations by using Prime MM/GBSA analysis. The schematic diagram for complete virtual screening procedure was outlined in Figure 6.

3.6 Interaction studies of screened hits

LID (Ligand interaction diagram) of Schrodinger was used to examine the interactions of screened hits and the outcome is shown in Figure 7. At first, the best active molecule 79 exhibited three hydrogen bond interactions: the first between the nitrogen of pyrrolidine ring and Asn 54 residue; the second between the -NH of pyrazole ring and Gln 203 residue and the last one was between the nitrogen of fused pyrimidine ring and Ser 205 residue. In Addition, four pi-pi stacking interactions were observed between 1H-pyrazolo[3,4-d] pyrimidine ring and Trp 80 residue. The hit molecules with high binding energy (A1-A11) formed one or more hydrogen bonding interactions with amino acid residues in the active pocket of Akt1 protein (Supplementary Information figure S1). For example, molecule A5 with binding energy -93.93 kcal/mol demonstrated specific hydrogen bonding interactions, where the oxygen of 5-methylpyrazolo[1,5-a] pyrimidin-7(4H)-one ring with Asn 54, -NH and oxygen of phthalazin-1(2H)-one ring with Thr 211 and Ser 205 respectively. Also, four prominent pi-pi stacking interactions were observed with Trp 80 residue, which was the key binding interaction of the active compound.

3.7 Binding free energy calculation

The binding free energies of top 10% of the hit molecules retrieved from the database are shown in Table 6. From Table 6, it is evident that all hits possessed high binding free energy values and were compared with the best active compound 79. In particular A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 and A11 with the good binding energy values of -90.63, -94.36, -86.66, -90.93, -93.20, -87.17, -86.89, -86.71, -87.37, -88.66 and -90.84 Kcal/mol respectively which are greater than the best active compound 79. As a result, these hits can undoubtedly behave as effective Akt1 inhibitors.

3.8 Designing of N-(3-(6-methoxybenzo[d]oxazol-2-yl)pyridin-2-yl)acetamide analogues (MPA’s)

The hits taken from the Asinex database exhibited high binding free energy values and strong binding interactions. Especially, molecules A2 and A5 are comparable with the best active compound 79 in terms of both interactions and free energy. Total 11 hits showed higher binding free energy values than the active compound (> -85.61 kcal/mol). However, A1-A11 hits exhibited two to three hydrogen bonding interactions that are responsible for Akt1 inhibition. The hypothesis generated from this current research resulted with five characteristics, including two acceptors group(A), one donor group(D) and two aromatic rings (AADRR). Interestingly, most of the screened compounds possessed either pyridine ring (A4, A8, A9) or fused pyridine derivative in their core structures. Therefore, we have designed a set of new pyridine derivatives: N-(3-(6-methoxybenzo[d]oxazol-2-yl)pyridin-2-yl)acetamide (MPA’s) analogues, based on the structural characteristics received from virtual screening, which could be exhibits strong interactions and better binding free energy values.

3.9 Molecular docking and Binding free energy calculations of MPA’s

The newly designed MPA’s further subjected to molecular docking against the active site of Akt1 (Figure 8) by using extra precision (XP) and the same grid was used, which was used for earlier docking of dataset molecules. The ligand interaction diagram of newly designed MPA’s is depicted in Figure 9. Most of the molecules formed the π- π stacking interaction with Tyr 80, which was the most promising interaction in both the active compound 79 and molecule A5 (moderate pIC₅₀ with higher binding free energy) retrieved from Asinex elite synergy database. For example, molecule D4 exhibited two hydrogen bonding interactions in which the acyl oxygen was with Thr 211 and oxygen linked with aryl ring was with Tyr 326 residues. Also, D4 exhibited two π - π stacking interactions in which the pyridine ring and fused oxazole rings were formed with Trp 80 residue. Similarly, D5 molecule (highest free binding energy -105.457) showed four interactions: 1) two hydrogen bonding interactions with Thr 211 and Ser 205 residues, 2) two π - π stacking interactions with Trp 80 and Tyr 272 residues. Another molecule D10 displayed three π - π stacking interactions with Trp 80 and two hydrogen bonding interactions with Thr 211 and Tyr 326 residues. Overall, the newly designed MPA’s possessed a stronger binding affinity towards the Akt1 protein by forming multiple hydrogen bonds and π - π stacking interactions.

Further, MPA’s have been evaluated based on generated five-point hypothesis followed by prime MM/GBSA analysis. As a result, 10 hits were identified with good binding free energy values (Table 7), i.e., D1, D2, D3, D4, D5, D6, D7, D8, D9 and D10 showed binding free energy values of -100.30, -100.09, -104.25, -105.45, -104.46, -102.02, -102.85, -97.33, -98.13, and -97.85 kcal/mole respectively. Comparative study between the active compound and newly designed MPA’s indicated that all the 10 molecules exhibited significant free binding energies than the active compound 79.

3.10 MD Simulation analysis

RMSD value is an important parameter to obtain the stability of the protein - ligand complex during the simulation process [41]. In this study, RMSD was calculated for the backbone of 4ejn and its complex formed with D10 molecule (one of the best molecules with high binding energy and a greater number of interactions). As shown in Figure 10a, 4ejn and D10 complex accomplished a stable state after 2 ns and remained as such with exceptional leaps (less than 0.1 nm). In the 4ejn protein, the trajectory showed stability after 26 ns but earlier it was found highly dynamic in nature with more fluctuations. This clearly specifies the RMSD of the 4ejn-D10 complex is more stable than 4ejn only throughout the simulation period. RMSF is another essential parameter to analyse the fluctuation of residual variation [42] and it can also identify the rigid and flexible regions of a protein-ligand complex. The RMSF profile of the 4ejn-D10 complex (Figure 10b) exhibited lower fluctuations than the actual protein 4ejn. The higher RMSF values were observed in both complex and 4ejn in the f2 region, having certain residues namely Lys 112, Gln 113, Ala 114 and Ala 115. A variation in 4ejn near Arg 48 residue (f1 region) has shown high fluctuation when compare with the 4ejn-D10 complex. The RMSF revealed that the fluctuations of residues for the 4ejn-D10 complex are quite lower than their native protein.

Radius of gyration (Rg) is a helpful tool to understand the folding properties and compactness of the protein and protein-ligand complexes during the simulation [43]. The conformational changes of Rg can serve as a demonstration of the impact of a drug molecule on a protein structure. A protein molecule with a high Rg value indicates its loose packing, whereas a lower Rg value indicates tight packing. The Rg plot of the 4ejn-D10 complex displayed (Figure 11a) a constant gyration of ~2.22 nm after 3700 ps and till the end it was maintained. This indicates the 4ejn-D10 complex is highly stable in comparison to the 4ejn protein.

SASA values of the protein-ligand complex were analysed to examine the protein surface accessible to solvent molecules [44]. Greater SASA values represent an increase in surface area, whereas lower SASA values represent a reduction in protein volume. SASA plot is shown in Figure 11b and it is evident that there is no drastic change in the 4ejn-D10 complex compare to 4ejn. The average count of H-bonds formed in the 4ejn-D10 complex displayed in Figure 11c and found four in number throughout the simulation.

3.11 In-silico ADME properties

The results are presented in Tables 8 and 9. Estimation of partition coefficient of octanol-water (lipophilicity <5) is examined by QPlogPo/w. Higher values generally result in a high risk of metabolic clearance and are related to poor adsorption. From Table 9, it is evident that the MPA derivatives with good binding scores follow Lipinski’s rule of five and showed favourable pharmacokinetic profiles except molecular weights (Mwt≤500). PSA is another important property (should not be >140 Å) linked to drug bioavailability and the values for the derivatives MPA are in the range of 81.49– 139.42 Å. QPlogS aqua solubility values for the MPA derivatives are less than 0.5 predicting excellent intestinal absorption. QPlogBB measures the ability of a drug to cross the blood-brain barrier and the values are in the acceptable range (-2.10 - 0.45). Further, QPlogKhsa Human serum albumin binding coefficient and QPPcaco cell permeability are the other key factors for a drug to be a successful candidate and showed satisfactory predictions. The predicted human oral absorption values for the MPA derivatives are in the range of 81-96 %, which indicates the possibility of excellent oral bioavailability. Thus, MPA derivatives with good binding scores and good in-silico ADME properties were considered for the synthesis and in vitro studies.

In conclusion, pharmacophore-based virtual screening was employed for the identification of potent Akt1 inhibitors. The best pharmacophore model AADRR38 was developed with the help of 85 known Akt1 inhibitors and was adopted to evaluate the Asinex elite synergy database. The generated hypothesis was further investigated and validated by standard protocol and the Correlation coefficient (r²) was found to be 0.90. The filtered hits were subjected to molecular docking against the Akt1 active pocket and binding free energy calculations. Out of 120 hits, 11 (A1-A11) molecules with the endorsed pharmacophoric properties were found to act as potent Akt1 inhibitors. Owing to the generated hypothesis, a series of 6-methoxybenzo[b]oxazole analogs were designed and employed in molecular docking, binding free analysis, and ADME studies. As an output, 10 (D1-D10) molecules were found to possess good binding free energy values (range from − 97.335 to -105.457 kcal/mol), which is greater than the active compound 79 (-85.61 kcal/mol). Additionally, most of the newly designed molecules exhibited a promising π - π staking interaction with Tyr 80, which is one of the key binding interactions of active compound 79 in the active pocket of Akt1 protein. MD simulation studies demonstrated the stability and flexibility of 4ejn-D10 complex. Further, In-silico ADME analysis for D1-D10 hits revealed that all these molecules are having a possibility of excellent oral bioavailability. Overall, the results of the present work suggest that the combined pharmacophore-based virtual screening, molecular docking, insilico ADME studies, and Prime MM/GBSA analysis could be useful in identifying the new Akt1 inhibitors.

Author Contribution

UE performed on methodology, conceptualization and wrote the main manuscript. RR provides the concept and prepared the figures and Tables. VT and VK methodology. VM supervise, final draft making, funding, Sources, Lab facility. All authors reviewed the manuscript.

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An in-silico approach to identify novel Akt1 (protein kinase B- alpha) inhibitors as anticancer drugs

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Abstract

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1. Introduction

2. Materials and methods

2.1 Dataset

2.2 Ligand preparation

2.3 Pharmacophore model generation

2.4 Scoring hypotheses

2.5 Building QSAR model

2.6 Virtual screening

2.7 Molecular docking and binding free energy calculations

2.8 Molecular dynamic (MD) simulation

2.9 In silico ADME prediction

3. Results and discussion

4. Conclusion

Declarations

Author Contribution

References

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Supplementary Files

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