Human RAD9 is a key cell-cycle checkpoint protein that participates in DNA repair, activation of multiple cell cycle phase checkpoints, and apoptosis. Aberrant RAD9 expression has been linked to breast, lung, thyroid, skin, and prostate tumorigenesis. Overexpression of RAD9 interacts with BCL-2 proteins and blocks the binding sites of BCL-2 family proteins to interact with chemotherapeutic drugs and leads to drug resistance. Focusing on this interaction, the present study was designed to identify the interaction sites of RAD9 to bind BCL-2 protein and also to inhibit RAD9-BCL-2 interactions by designing novel small molecule inhibitors using pharmacophore modeling and to restore BCL-2 for interacting with anticancer drugs. The bioactive molecules of natural origin act as excellent leads for new drug development. Thus, in the present study, we used the compounds of natural origin like camptothecin, ascididemin, and Dolastatin and also compared them with synthetic molecule NSC15520. The results revealed that camptothecin can act as an effective inhibitor among all the ligands taken and can be used as an RAD9 inhibitor. The amino acids ARG45 and ALA134 of RAD9 protein are interacting commonly with the drugs and BCL-2 protein.
RAD9 is a multidomain protein, functions in multiple pathways, and is involved in the regulation of cell cycle checkpoints which contribute to genome integrity and apoptosis [
High expression of BCL family proteins was found in a wide variety of human cancers [
Medicinal plants derivatives are playing an important role in the treatment of cancer. Indeed, many new clinical applications of plant secondary metabolites and their derivatives are forging towards combating cancers. Many of the clinical applications have been discussed extensively by the researchers [
We present here the structural analysis of RAD9 and BCl-2 enzymes in order to understand the protein conformations and energy minimal poses and RAD9 and BCL-2 interaction studies to understand the functional aspects followed by pharmacophore designing and ligand docking interactions to blockade the RAD9 active site.
RAD9 structure was downloaded from Protein Data Bank (PDB ID: 3GGR, resolution 3.20 Å) [
Protein-protein docking was done to study the BCL-2-RAD9 interaction by using RosettaDock server [
Ligand Scout 3.1 [
The pharmacophore model that performed better in all the validation procedures was considered as the best pharmacophore model. It was used further as a 3D structural query to search chemical databases like PubChem [
The ligand and protein (RAD9) molecules were prepared in AutoDockTools. Polar hydrogens were added into the model PDB file for the preparation of protein in docking simulation. Gasteiger charge was assigned and nonpolar hydrogens were merged to the ligands. In order to carry out the docking simulation, AutoDock was used. It is one of the most suitable methods for performing molecular docking of macromolecules and ligands. The structures of the ligand and receptor were then saved in pqbqt format to be used for docking calculations. A three-dimensional grid box with dimensions
Investigating the relationship between RAD9 structure and function, the impact of protein-binding partners on RAD9 activity as well as how the tumor microenvironment might influence RAD9-mediated processes could be exploited to devise novel targeted therapies to treat cancer and possibly by rationally reducing or increasing RAD9 levels. This approach can be considered part of an emerging new strategy to combat cancer, where treatments are custom-designed and based on the molecular genetic profile of normal versus cancerous tissues in patients. RAD9 can function in multiple cellular processes, raising special challenges when considering the utility of ultimately targeting the protein level or activity for therapy. The RAD9 has shown to interact with BCL family proteins which are critical for resistance to chemotherapy. Thus, the RAD9 site which is prominently bound to BCl-2 family protein is identified as a target.
The RAD9 structure retrieved from the PDB (ID: 3GGR) was subjected to energy minimization. Energy minimization was performed with Swiss-Pdb software by using GROMOS 43B1 force field. This force field allows evaluating the energy of a structure as well as repairing distorted geometries through energy minimization. The geometry of the BCL-2 model was evaluated with Ramachandran’s plot calculations computed with the PROCHECK program (Figure
Structural parameters of the model.
Protein | MHBD (Å) | MHBE | MHPh | MHPs | MCG+ | MCG− | MRV (Å3) | TV (Å3) |
---|---|---|---|---|---|---|---|---|
BCL-2 | 2.2 | −1.4 | −65.4 | −38.3 | −66.5 | 64.5 | 132.2 | 31592.6 |
RAD9 | 2.2 | −1.9 | −65.1 | −36.2 | −64.9 | 67.6 | 140.7 | 37144.2 |
MHBD: mean hydrogen bond distance; MHBE: mean hydrogen bond energy; MHPh: mean helix phi; MHPs: mean helix psi; MCG+: mean chi gauche+; MCG−: mean chi gauche−; MRV: mean residue volume; TV: total volume (packing).
BCL-2 protein amino acids distribution in Ramachandran plot.
(a) Homology model of BCL-2 represented in solid ribbon pose, (b) BCL-2 model in surface representation, (c) RAD9 protein structure in solid ribbon pose, and (d) RAD9 protein structure in surface representation.
BCL-2 protein has shown to interact with ARG45, ARG109, SER116, ALA134, GLN210, and GLY244 (Figure
Docking results of BCL-2 and RAD9 molecules.
S.No | Interacting amino acids (H bond) | Bond distance ( |
|
---|---|---|---|
RAD9 | BCL-2 | ||
1 | ARG45:NH1 | GLU29:OE2 | 2.957 |
2 | ARG45:NH2 | GLU29:OE2 | 2.824 |
3 | ALA134:N | GLY83:O | 2.833 |
4 | GLN210:NE2 | GLU114:OE1 | 3.132 |
5 | GLY244:N | ASN163:OD1 | 2.945 |
6 | ARG109:NH2 | PRO142:O | 3.109 |
7 | SER116:OG | ASP241:OD2 | 3.048 |
BCL-2 and RAD9 interaction represented in ball and stick (a), surface (b), sticks (c), and interacting amino acids of both proteins (d).
In a computerized pharmacophore generation process, the accurate choice of the training set (rough data of drugs) is a key issue. The built pharmacophore hypothesis can be as good as the input data information. The following criteria should be considered during the selection of data set in order to achieve a significant pharmacophore hypothesis. All compounds used in the training set have to bind to the same receptor in roughly the same fashion. Compounds having more binding interaction with the receptor are more active than those with fewer. The most active compounds should inevitably be included in the training set and all biologically relevant data should be obtained by homogenous procedures. Every individual feature in the resulting hypotheses will invade a certain weight that is proportional to its relative contribution to biological activity.
Based on the principle of structural diversity and wide coverage of activity range, 20 compounds were carefully selected as training set compounds and the rest were used as test set in model validation. Structural and chemical features of the training set are depicted in Supplementary Table (see Supplementary Material available online at
Common feature pharmacophore models were built with five active training set compounds using ligand scout. The training set included homologues of camptothecin, ascididemin, dolastatin, and molecule NSC15520 chemical scaffolds. Several pharmacophore runs were carried out by changing the control parameters to develop the best model. The best pharmacophore model containing three hydrophobic and two hydrogen bond acceptor features is shown in Figure
Two-dimensional spatial arrangement of the pharmacophore model showing hydrogen bonds between “SER37A and O”, “TYR48A and O”. Hydrogen bonds were represented by red-coloured dotted arrows and yellow colour represents hydrophobic (HY) interaction with ALA134A and PHE136A residues.
Most active compounds in the training set and screened best hit compounds have been identified and are docked using AutoDock 4.2. Finally, four compounds, namely, camptothecin, ascididemin, dolastatin, and molecule NSC15520 were selected. In order to block the RAD9 and BCL-2 interaction, the residues on RAD9 which are interacting with the BCL-2 are taken for the docking simulations with ligands. The binding pose of all the ligand molecules at the active site of RAD9 has been analyzed. RAD9 and ligand docking statistics are depicted in Table
RAD9 and ligands interacting statistics.
Ligand | H-bonding residue | Bond distance ( |
---|---|---|
NSC15520 | Arg39 | 2.245 |
Tyr48 | 1.758 | |
Val135 | 2.018 | |
| ||
Camptothecin | Glu30 | 2.038 |
Val135 | 2.013 | |
| ||
Dolastatin | Arg39 | 2.047 |
| ||
Ascididemin | Glu33 | 1.885 |
RAD9 and ligands interacting conformations (a), RAD9 and NSC15520 interacting complex, and (b) RAD9 and camptothecin interacting complex.
Superimposition of Pharmacophore and best hit compounds. Yellow coloured sticks represents shared common structural features, small Orange coloured spheres represents similar bond order and green spheres represents hydrogen bond features.
The data from this study demonstrate that the pharmacophore model can be used to describe the stereoselective binding of compounds at one of the sites on the RAD9 molecule. Thus, the pharmacophore model is a potential
The authors contributed equally to the work.
The authors wish to thank Dr. Ravichand and Dr. P. V. Bramhachari for their valuable suggestions. Angamba Potshangbam Meetei is supported by the CSIR Fellowship.