Molecular Docking: A Computational Approach to Predict Protein-Ligand Interactions and Accelerating Drug Discovery

Cancer, also known as malignancy, malignant tumor, or neoplasm, occurs when cells grow uncontrollably and invade other tissues. These excess cells can form a tumor. Tumors can be benign or malignant. Benign tumors are non-cancerous, often removable, and rarely return, while malignant tumors are cancerous, capable of spreading to other tissues and organs. This spread is known as metastasis. Cancers are typically named after the organ or cell type where they originate, such as colon cancer or melanoma, which arises from skin melanocytes. Molecular docking is a technique used to predict how a ligand binds to a protein, crucial in drug discovery for optimizing binding affinities. Statins, like Atorvastatin and Fluvastatin, inhibit HMG-CoA reductase to lower cholesterol, with varying efficacy and side effects. This study uses docking simulations to explore their binding modes to protein targets, aiming to optimize statin-based therapeutics. Docking methods include scoring functions, molecular dynamics, and QM/MM models. Advances in medicinal chemistry have shifted from organic chemistry to biotechnology, with high-throughput technologies and bioinformatics leading the way. Statin repurposing is being explored for conditions beyond cholesterol regulation, including anti-cancer effects. Combinatorial chemistry aids in discovering drug leads quickly, and techniques like QSAR and computer-aided drug design (CADD) are critical in optimizing drug properties. ADMET (absorption, distribution, metabolism, excretion, toxicity) is vital for evaluating drug safety. Molecular docking identifies optimal binding sites and interactions between proteins and ligands, aiding drug discovery but requiring validation and complementary experimental data. Statins, including Atorvastatin, are commonly prescribed medications used to lower cholesterol levels and reduce the risk of cardiovascular diseases. Statins primarily work by inhibiting the enzyme HMG-CoA reductase, which is crucial for cholesterol production in the liver. This inhibition leads to decreased LDL ("bad") cholesterol and triglycerides while potentially increasing HDL ("good") cholesterol. Atorvastatin, in particular, not only lowers LDL and triglyceride levels but also offers additional benefits like improving endothelial function, stabilizing atherosclerotic plaques, and reducing oxidative stress and inflammation. These effects make Atorvastatin an effective treatment for preventing heart disease, strokes, and heart attacks. It is often used in combination with lifestyle changes such as diet, exercise, and weight loss. Beyond its lipid-lowering effects, Atorvastatin is used to treat conditions like familial hypercholesterolemia and is considered an essential therapy for individuals at high cardiovascular risk. Fluvastatin inhibits HMG-CoA reductase, reducing cholesterol production in the liver. This leads to increased LDL receptors, lowering LDL ("bad" cholesterol) in the bloodstream. It also helps increase HDL ("good" cholesterol) and slows plaque buildup in blood vessels. Fluvastatin is used to reduce heart disease risks and complications in people with coronary heart disease. It is suitable for adults and children aged 10 and above. It improves lipid profiles and reduces cardiovascular disease risk. Etoposide works by inhibiting DNA topoisomerase II, an enzyme that is crucial for DNA replication and repair. It blocks the re-ligation of DNA strands, causing DNA breaks that disrupt cell division. This results in apoptosis, particularly in rapidly dividing cancer cells. Etoposide is most effective during the S and G2 phases of the cell cycle. It primarily targets the topoisomerase II alpha isoform, essential for cell proliferation. However, its effect on the beta isoform may contribute to the risk of secondary cancers with long-term use.

Experimental

Aim

To predict the protein-ligand interactions and accelerating drug discovery by using molecular docking.

Objectives

• To prepare and optimize protein and ligand structures.

• To perform Molecular Docking Simulations

• To analyze and validate the results of binding interactions and scoring of the docking results.

MATERIALS AND METHODS

RESULTS AND DISCUSSION

The docking analysis assessed the interactions of Atorvastatin, Fluvastatin, and Etoposide with different target proteins, using Etoposide and its standard target protein (5NNE) as the benchmark. Etoposide exhibited a binding energy of -4.27 KJ/mol with 5NNE, indicating moderate binding stability. However, its high inhibition constant of 741.06 suggests weak inhibitory potential despite forming stable interactions. In comparison, Atorvastatin demonstrated a stronger binding affinity, particularly with 2IEJ (-5.76 KJ/mol) and 1LD8 (-5.72 KJ/mol), indicating a more stable interaction. The inhibition constant for Atorvastatin ranged from 59.56 to 384.13, significantly lower than that of Etoposide, suggesting enhanced inhibition efficiency. Fluvastatin, in contrast, exhibited weaker binding interactions, with binding energies ranging from -1.88 KJ/mol (6MBB) to - 4.57 KJ/mol (5UUP). The inhibition constants of Fluvastatin varied, with some values as low as 2.62 (6E3J), indicating poor inhibitory potential. Additionally, the van der Waals and hydrogen bonding interactions were strongest for Atorvastatin (-9.96 for 1LD8 and -9.02 for 2IEJ), followed by Etoposide (-6.62 for 5NNE) and Fluvastatin (ranging between -3.82 and - 6.82). Electrostatic interactions also favored Atorvastatin, with values as low as -1.22 (2IEJ), compared to 0.04 (5NNE) for Etoposide, suggesting that Atorvastatin forms more stable electrostatic interactions.

Pharmacokinetics Of Statin

Atorvastatin

Fig.1.1: Ligand Atorvastatin and Protein 1S63 before In-silico docking process

Fig.1.2: Ligand Atorvastatin and Protein 1S63 after In-silico docking process

Fig.1.3: Ligand Atorvastatin and Protein 3E31 before In-silico docking process

Fig.1.4: Ligand Atorvastatin and Protein 3E31 after In-silico docking process

Fig.1.5: Ligand Atorvastatin and Protein 2IEJ before In-silico docking process

Fig.1.6: Ligand Atorvastatin and Protein 2IEJ after In-silico docking process

Fig.1.7: Ligand Atorvastatin and Protein 1LD8 before In-silico docking process

Fig.1.8: Ligand Atorvastatin and Protein 1LD8 after In-silico docking process

Fig.1.9: Ligand Atorvastatin and Protein 2H6H before In-silico docking process

Fig.1.10: Ligand Atorvastatin and Protein 2H6H after In-silico docking process

Fig.2.1: Ligand Fluvastatin and Protein 6MBB before In-silico docking process

Fig.2.2: Ligand Fluvastatin and Protein 6MBB after In-silico docking process

Fig.2.3: Ligand Fluvastatin and Protein 6E3I before In-silico docking process

Fig.2.4: Ligand Fluvastatin and Protein 6E3I after In-silico docking process

Fig.2.5: Ligand Fluvastatin and Protein 6E3J before In-silico docking process

Fig.2.6: Ligand Fluvastatin and Protein 6E3J after In-silico docking process

Fig.2.7: Ligand Fluvastatin and Protein 5UUP before In-silico docking process

Fig.2.8: Ligand Fluvastatin and Protein 5UUP after In-silico docking process

Fig.2.9: Ligand Fluvastatin and Protein 4CIM before In-silico docking process