Phosphoinositide-dependent protein kinase-1 (PDK1), the class of serine threonine kinase, is a master regulator of the AGC family of kinases. It is a main component of the PI3K pathway. As it is reported that this pathway is most commonly, and this pathway is the most commonly deregulated among many cancers. So designing a selective inhibitor of PDK1 may have the efficacy as an anticancer agent. Herein, we describe our work focused on the structure based on screening of 95% similar analogues of Myricetin deposited in PubChem database as earlier studies have been suggested that myricetin acts as an anti cancer agent. Further molecular docking as well as the in silico ADMET studies are incorporated on these compounds to evaluate the binding and pharmacokinetic properties of these compounds. Due to low oral bioavailability, clinical use of myricetin is limited. Therefore this study is an attempt towards screening of structurally similar better compounds as compare with myricetin which can act as better inhibitor against PDK-1.
Protein kinases are critical components of cellular signal transduction cascades [
It is confirmed by the previous study that Myricetin acts as a putative inhibitor for several cancer. The presented work is an attempt to screen 95% similar compounds deposited in Pubchem database. Several computational methods like Lipinski filter molecular docking and in silico ADMET study have been incorporated on the screen compounds to predict these molecules behavior as a putative future drug for cancer treatment.
The two dimensional chemical structure of natural flavonoid Myricetin (CID5281672) was retrieved from the NCBI PubChem database (http://www.pubchem.ncbi.nlm.nih.gov) and similarity search was performed on the chemical compounds deposited in the Pubchem database to retrieve the related compound and analogues. The search parameters were set at 95% similarity. 2D structures of all screened compounds were downloaded from pubchem database. The whole methodology used for study is shown in
The three dimensional format of all filtered compounds were downloaded from Pubchem database in .sd file formt. Subsequently CharMM [
Energy minimization is an important step in molecular modeling of proteins/peptides. It was used to compute the equilibrium configuration of molecules. Energy minimization methods can be divided into different classes depending on the order of the derivative used for locating a minimum on the energy surface.
The steepest descent method uses the first derivative to determine the direction towards the minimum. It is not particularly efficient because it must be combined with a line search to determine the step size. The line search uses the direction vector obtained from the first derivative of the potential function to find the optimum step size along this vector direction. Once this local minimum along the direction of the derivative is found the step can be taken. The next derivative will be orthogonal to the first. A line search is requires several function evaluations, however, in order to determine the optimum step size. This technique is robust and is used to minimize initially when the structure is far from the minimum configuration.
More efficient minimization can be obtained using conjugate gradients algorithms. The conjugate gradient technique uses information from previous first derivatives to determine the optimum direction for a line search.
The drug likeliness properties of all the retrieved compounds were evaluated by Lipinski drug filter [
The X ray crystal structure of PDK-1 (PDB id 1UU7) [
Molecular docking was performed by the CDOCKER docking method implemented in Discovery Studio 2.5. CDOCKER is a simulated annealing based molecular docking method. In this docking method ligands are treated as fully flexible while protein is kept rigid. The minimized structure of all compounds was used as input ligand in the protocol explorer of CDOCKER. Each of them is given as input in another parameter meant for ‘input ligands’ and the protocol are run as many times as the number of inhibitors are selected for the experiment. The various conformations for ligand in this procedure were generated by using molecular dynamics. The generated initial structures for the ligand may be further refined using simulated annealing. The CDOCKER energy (-(protein-ligand interaction energies)) of best configuration docked into the receptor of all the selected natural inhibitors, which were calculated and compared with that of interacting residues at active site region with the crystallized inhibitors, PDK-1 kinase protein. Binding energy of protein and ligands were calculated by following calculation:
Insilico ADMET studies have been done by using the ADMET protocol implemented in D.S 2.5 (Accelrys Discovery studio software). In silico ADME studies solely depend on the chemical structure of molecules. In silico ADMET properties such as ADMET BBB level [
Toxicity profiling of all selected ligands were performed by employing Toxicity prediction―extensible protocol implemented in Accelrys discovery studio 2.5.
Toxicity prediction profile includes screening for aerobic biodegradability, developmental toxicity potentials, AMES mutagenicity, carcinogenicity & skin irritancy [
Lipinski filter is used to study the drug likeness of all screened molecules.
Most of drug failures have been reported in early and late pipeline stage due to undesired pharmacokinetics and toxicity problems. If these issues can be addressed early, it would be extremely advantageous for the drug discovery process. The use of in silico methods to predict ADMET properties is projected as a first step in this direction to analyze the novel chemical entities to prevent wasting time on lead candidates that would be toxic or
Compounds | ALogP | Molecular Weight | Num_Rings | Num Aromatic Rings | Num_H Acceptor | Num_H Donar | Molecular Fractional Polar Surface area |
---|---|---|---|---|---|---|---|
CID_5281701 | 1.926 | 302.236 | 3 | 2 | 7 | 5 | 0.47 |
CID_10517292 | 1.872 | 286.236 | 3 | 2 | 6 | 4 | 0.419 |
CID_10636768 | 2.731 | 284.263 | 3 | 2 | 5 | 3 | 0.335 |
CID_13964548 | 2.619 | 314.289 | 3 | 2 | 6 | 2 | 0.289 |
CID_13964550 | 2.394 | 300.263 | 3 | 2 | 6 | 3 | 0.349 |
CID_24721178 | 1.872 | 286.236 | 3 | 2 | 6 | 4 | 0.419 |
CID_5281697 | 2.168 | 286.236 | 3 | 2 | 6 | 4 | 0.419 |
CID_5281953 | 1.839 | 346.288 | 3 | 2 | 8 | 4 | 0.391 |
CID_5315126 | 3.487 | 370.353 | 3 | 2 | 7 | 5 | 0.361 |
CID_5318214 | 2.394 | 300.263 | 3 | 2 | 6 | 3 | 0.349 |
CID_5320287 | 2.081 | 330.289 | 3 | 2 | 7 | 3 | 0.342 |
CID_5322065 | 2.41 | 270.237 | 3 | 2 | 5 | 3 | 0.358 |
CID_5393164 | 2.168 | 286.236 | 3 | 2 | 6 | 4 | 0.419 |
CID_57402278 | 2.728 | 344.315 | 3 | 2 | 7 | 4 | 0.364 |
CID_6477684 | 2.878 | 296.274 | 3 | 2 | 5 | 3 | 0.321 |
CID_6477685 | 2.636 | 312.274 | 3 | 2 | 6 | 4 | 0.377 |
CID_66574000 | 2.283 | 322.217 | 3 | 2 | 6 | 4 | 0.393 |
CID_9839293 | 2.098 | 300.263 | 3 | 2 | 6 | 3 | 0.349 |
Myricetin | 1.388 | 318.23 | 3 | 2 | 8 | 6 | 0.532 |
metabolized by the body into an inactive form and unable to cross membranes, and the results of such analysis are herein reported in
useful parameter for prediction of drug transportation in different part of the body. The predefined models usually neglect the effect of other descriptors. The drug transportation and permeability has been demonstrated by PSA (plasma surface area). The cell membrane phospholipidbilayer is able to form hydrophobic and hydrophilic interactions as suggested by the fluid mosaic model, so lipophilicity is also play an essential property for drug designing and development. Lipophilicity of any compound could be expressed as the logarithms of the partition coefficient between n-octanol and water (log P). Thus the all the information about H-bonding could be govern by both PSA as well as log P calculation .Therefore in all model a plot between descriptors AlogP98 and PSA 2D at 95% and 99% confidence ellipses was considered for the precise prediction for the cell permeability of compounds. The region of 95% confidence ellipse depicts the chemical area well-absorbed compounds (≥90%) 95 out of 100 times. Whereas 99% is a confidence ellipse depicts chemical area of those compounds which having excellent absorption through cell membrane. Compound having an optimum cell permeability should follow the criteria (PSA < 140 Å2 and AlogP98 < 5) as describe in the model. The results show that all the compounds except myricetin (151.23 A2) showed polar surface area (PSA) < 140 Å2. It was shown in
Insilico toxicity profile of all selected ligands was shown in
Myricetin (3,5,7-Trihydroxy-2-(3,4,5-trihydroxyphenyl)-4-chromenone), is natural occurring flavanol [
present in the plant kingdom as a secondary metabolite. It is the most well defined group of polyphenolic compounds. Myricetin is commonly found as O-glycosides with one of its hydroxyl group are substituted by sugars of various types. Molecular docking study of all compounds within the active sites of the PDK-1 kinase was carried out using CDOCKER docking method implemented in Discovery studio 2.5. Molecular docking results of Myricetin and the top hits analogues were tabulated in
Compounds | Docking Energy Kcal/mole | Hydrogen bonding Residues | Hydrogen Bond distances (Å) |
---|---|---|---|
Myricetin | −41 | A:LYS111:HZ1 - 5281672:O5 A:LYS111:HZ3 - 5281672:O5 A:ALA162:HN -5281672:O8 5281672:H28-AGLU:OE2 5281672:H30-AASP223:OD1 5281672:H32 A:SER162:O 5281672:H33 A:SER162:O | 2.44 2.19 2.34 2.31 2.09 2.14 1.98 |
CID_66574000 | −42.8 | A:LYS111:HZ3 - 66574000:O8 A:ALA162:HN - 66574000:F1 A:THR222:HG1 - 66574000:O7 66574000:H30 - A:GLU130:OE2 66574000:H31 - A:ASP223:OD1 | 1.89 2.31 2.24 2.33 2.45 |
CID_6677685 | −35.6 | A:LYS111:HZ2 - 6477685:O6 A:LYS111:HZ3 - 6477685:O6 A:ALA162:HN - 6477685:O4 6477685:H32 - A:ALA162:O 6477685:H34 - A:GLU130:OE2 6477685:H35 - A:ASP223:OD1 | 2.47 2.10 2.39 1.97 2.22 2.03 |
CID_6677684 | −37.90 | A:LYS111:HZ2 - 6477684:O5 A:LYS111:HZ3 - 6477684:O5 A:ALA162:HN - 6477684:O2 6477684:H33 - A:GLU130:OE2 6477684:H34 - A:ASP223:OD1 | 2.37 2.12 2.14 2.09 2.03 |
CID_57402278 | −38.17 | A:LYS111:HZ3 - 57402278:O4 A:ALA162:HN - 57402278:O7 57402278:H39 - A:GLU90:O 57402278:H41 - A:SER160:O | 1.77 2.33 2.48 1.92 |
CID_5322065 | −38.98 | A:LYS111:HZ3 - 5322065:O3 A:ALA162:HN - 5322065:O5 5322065:H28 - A:ASP223:OD1 5322065:H29 - A:SER160:O 5322065:H30 - A:SER160:O | 2.16 1.89 2.04 1.90 2.05 |
CID_5281701 | −41.87 | A:LYS111:HZ3 - 5281701:O3 5281701:H28 - A:ASP223:OD1 5281701:H29 - A:GLU209:OE2 5281701:H31 - A:SER160:O | 1.91 2.02 2.09 1.87 |
---|---|---|---|
CID_10517292 | −39.36 | A:LYS111:HZ3 - 10517292:O5 10517292:H28 - A:GLU166:OE2 10517292:H29 - A:ASP223:OD2 10517292:H31 - A:ASP223:OD2 10517292:H29 - A:ASN210:OD1 | 1.80 2.19 2.33 2.36 1.92 |
CID_13964550 | −36.82 | A:LYS111:HZ3 - 13964550:O3 A:ALA162:HN - 13964550:O6 13964550:H32 - A:GLU209:OE2 13964550:H33 - A:SER160:O 13964550:H34 - A:SER160:O | 1.72 2.21 2.14 1.87 2.22 |
CID_5281697 | −40.31 | A:LYS111:HZ3 - 5281697:O3 5281697:H29 - A:ASP223:OD1 5281697:H30 - A:GLU130:OE2 5281697:H31 - A:ALA162:O | 2.10 2.35 2.16 1.93 |
CID_5281953 | −37.8 | A:LYS111:HZ3 - 5281953:O40 A:LYS111:HZ3 - 5281953:O7 A:ALA162:HN - 5281953:O8 5281953:H30 - A:GLU166:OE2 5281953:H32 - A:SER94:OG 5281953:H33 - A:SER160:O | 1.80 2.02 1.97 2.17 2.03 2.40 |
can also act as a probable drug as PDK-1 kinase inhibitor.
It can be concluded that myricetin and the analogues have better binding interactions with PDK1 kinase (PDB: 1UU7.) The binding energies of the protein-ligand interactions also confirm that the ligands are fit into the active pockets of receptor tightly. Insilico ADMET study concludes that all the analogues have better profiles when compare with myricetin. These may held better potential as drug candidates that inhibit the PDK-1 kinase. Further development and modification of these analogues may lead to generation of novel high potent anticancer drug in future.
The authors thank to UGC for providing financial support and GVK Biosciences Pvt., Ltd., for their cooperation and providing software facilities.