In Silico Drug Design of Anti-Breast Cancer Agents

Abstract

Cancer is a condition marked by abnormal cell proliferation that has the potential to invade or indicate other health issues. Human beings are affected by more than 100 different types of cancer. Some cancer promotes rapid cell proliferation, whereas others cause cells to divide and develop more slowly. Some cancers, such as leukemia, produce visible tumors, while others, such as breast cancer, do not. In this work, in silico investigations were carried out to investigate the binding mechanisms of four major analogs, which are marine sesquiterpene, sesquiterpene lactone, heteroaromatic chalcones, and benzothiophene against the target estrogen receptor-α for targeting breast cancer using Schrödinger suite 2021-4. The Glide module handled the molecular docking experiments, the QikProp module handled the ADMET screening, and the Prime MM-GB/SA module determined the binding energy of the ligands. The benzothiophene analog BT_ER_15f (G-score -15.922 Kcal/mol) showed the best binding activity against the target protein estrogen receptor-α when compared with the standard drug tamoxifen which has a docking score of -13.560 Kcal/mol. TRP383 (tryptophan) has the highest interaction time with the ligand, and hence it could act for a long time. Based on in silico investigations, the benzothiophene analog BT_ER_15f significantly binds with the active site of the target protein estrogen receptor-α. Similar to the outcomes of molecular docking, the target and ligand complex interaction motif established a high affinity of lead candidates in a dynamic system. This study shows that estrogen receptor-α targets inhibitors with better potential and low toxicity when compared to the existing market drugs, which can be made from a benzothiophene derivative. It may result in considerable activity and be applied to more research on breast cancer.

Keywords: 3D-QSAR; benzothiophene analog; breast cancer; docking studies; molecular dynamics; pharmacophore modeling.

Figure 5

2D and 3D interaction diagram of BT_ER_15f with protein 2IOG.

Figure 6

(a) Electron-withdrawing group favorable positions (blue colour); (b) Hydrogen bond donor group non-favorable positions for BT_ER_15f(red colour); (c) Hydrophobic group favorable (blue) and non-favorable (red) positions for BT_ER_15f.

Figure 7

(a) Bond angle between the AAHHH.3; (b) Bond distance between the AAHHH.3.

Figure 8

(a) Scatter plot for the test set; (b) Scatter plot for the training set.

Figure 9

Ligand atom interactions with the protein residues.

Figure 10

Interaction fraction of amino acids of BT_ER_15f.

Figure 11

Interaction fraction of amino acids of the standard tamoxifen drug.

Figure 12

Standard tamoxifen interactions with the protein residues.

Figure 13

Interaction time of each amino acid.

Figure 14

PL-RMSD of simulated protein 2IOG in complex with BT_ER_15f during 100 ns MD.

Figure 15

PL-RMSD of simulated protein 2IOG in complex with Tamoxifen during 100 ns MD.

Figure 1

Structure of benzothiophene (BT) analogs.

Figure 1

Structure of benzothiophene (BT) analogs.

Figure 2

Structure of marine sesterterpene (MS) analogs.

Figure 3

Structure of heteroaromatic chalcones (HC) analogs.

Figure 4

Structure of sesquiterpene lactone (SL) analogs.

Figure 4

Structure of sesquiterpene lactone (SL) analogs.

Figure 4

Structure of sesquiterpene lactone (SL) analogs.

Figure 16

Ligand properties of BT_ER_15f in complex with 2IOG during 100 ns MD simulation.

Figure 17

(a) L-RMSF of simulated protein; (b) P-RMSF of simulated protein 2IOG in complex with BT_ER_15f.