Bioinformatics Approaches to Protein Structure Prediction, Ligand Interaction, and Drug Design: A Detailed Tutorial

1. Downloading Protein Sequence from Uniprot:

a. Go to UniProt

• Type “Green Fluorescent Protein” or “P42212” in the search bar.
• Click on the entry titled “P42212” or the equivalent.
• Here, find the “Sequence” section.
• Click “FASTA” to copy the sequence.

2. Primary Structure Analysis using Expasy ProtParam:

a. Go to ProtParam

• Paste your FASTA sequence into the input box and submit.

b. Interpretation:

• Number of Amino Acids: Gives insight into the size of the protein.
• Molecular Weight: Useful for experimental planning and analysis.
• Theoretical pI: Predicts the pH at which the protein is neutral.
• Amino Acid Composition: Provides insights into the protein’s characteristics, stability, and functionality.
• Extinction Coefficient: Useful for determining protein concentration using absorbance.

3. Secondary Structure Prediction using PSIPRED:

a. Go to PSIPRED

• Paste the FASTA sequence and run the tool.

b. Interpretation:

• PSIPRED provides visual representation of the secondary structure elements: alpha-helices, beta-sheets, and coils. Evaluate the regions where these structures are located and correlate them with functional regions of the protein if known.

4. Homology Modelling using Swiss-Model:

a. Go to Swiss-Model

• Paste the FASTA sequence and submit.
• After modeling, download the model in PDB format.

b. Interpretation:

• QMEAN: A scoring function for model quality estimation, higher is usually better.
• GMQE: Gives an indication about the reliability of the model, it ranges between 0-1; the higher the better.

5. Structure Assessment using SAVES Server:

a. Go to SAVES

• Upload your modeled structure.
• Evaluate the results using tools like ERRAT and Verify3D.

b. Interpretation:

• ERRAT: Quality factor, higher is better.
• Verify3D: Assesses the compatibility of an atomic model with its amino acid sequence, scores above 0.2 are considered acceptable.

6. Get Suitable Ligand from PubChem:

a. Go to PubChem

• Let’s assume we have identified five hypothetical ligands related to fluorescence. We’ll refer to them as Ligand1 (CID: XXXXX1), Ligand2 (CID: XXXXX2), Ligand3 (CID: XXXXX3), Ligand4 (CID: XXXXX4), and Ligand5 (CID: XXXXX5).
• Download the chemical structure of each ligand, preferably in SDF or SMILES format.

7. Check Ligand using SwissADME:

a. Go to SwissADME

• Input each ligand’s SMILES string or SDF file and run the tool.

b. Interpretation:

• Lipophilicity (LogP): A crucial descriptor in ADME prediction; values between 1 and 3 are generally considered optimal for oral drugs.
• Solubility: Important for bioavailability.
• MedChem Friendliness: Helps assess whether a compound is suitable for a drug from a medicinal chemistry perspective.

Analyze these parameters to select ligands with optimal properties.

8. Docking using Swiss-Dock:

a. Go to SwissDock

• Upload the ligand and the protein structures (in the appropriate format, typically PDB).
• After the docking run is complete, you can download the predicted binding poses in terms of their ΔG values (binding free energies).

b. Interpretation:

• Check the cluster details, focusing on the FullFitness (ΔG) value. The lower (more negative) the ΔG, the more favorable the binding is predicted to be.
• Assess the docking poses to ensure they make logical and feasible interactions based on the protein’s known function and the ligand’s chemistry.

9. Results Analysis:

a. Analyze the Binding Poses:

• Visualize the interaction between the ligand and the protein using molecular visualization software like PyMOL or Chimera. Assess the ligand’s binding mode, the residues involved, and compare with available experimental data or literature.

b. Interpretation:

• Are the ligands interacting with crucial residues known to be involved in the protein’s function or other known ligands’ binding?
• Are the interactions (hydrophobic, hydrogen bonds, etc.) logical and favorable for stable binding?
• How does each ligand compare in terms of binding pose and ΔG? Which ligand is predicted to be the most favorable?

10. Summary:

After completing all the steps, compare all the ligands and their interactions with the protein, assess the reliability of the models, and analyze the drug-likeness and pharmacokinetics properties of each ligand. Here’s a potential approach to summarizing the findings:

• Compare the Ligands: Which ligand has the most favorable binding affinity, and does it interact logically with the protein?
• Reliability Assessment: Is the model and the predicted interactions reliable based on the validations, and do they make sense biologically?
• ADMET Properties: Which ligand shows optimal drug-like properties based on SwissADME analysis?

Final Thoughts:

This workflow provides an in-depth step-by-step analysis of protein-ligand interactions, from obtaining protein sequences to docking studies, and it forms the backbone for structure-based drug design. However, in practical scenarios, experimental validation, further in-depth in silico studies, and iterative refinement are crucial to accurately understand and validate the predicted interactions and to potentially discover impactful compounds.