anticrcwu

Deciphering Anti-Colorectal Cancer Mechanisms of Warburgia ugandensis: A Network Pharmacology, Bioinformatics, Molecular Docking, and Inflammatory Analysis

Anti-Colorectal Cancer Potential of Warburgia ugandensis

Background

Colorectal cancer (CRC) is a major global health concern, with rising incidence and mortality worldwide. Current treatments including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy face challenges such as toxicity, resistance, high cost, and limited efficacy in many patients. In Africa, late-stage diagnoses and limited screening exacerbate poor outcomes. Warburgia ugandensis, an East African medicinal plant, shows anticancer potential, but the specific bioactive compounds and molecular mechanisms underlying its anti-CRC effects remain unclear. There is an urgent need to explore its therapeutic potential for novel CRC treatments.

Team Members

• Team Lead: Mr. Yohana Amos (Tanzania)
• Co-Lead: Mr. Bienvenu Nsengimaana (Uganda)
• Team Members:
  1. Mr. Orace Mathieu Kenou (Benin)
  2. Ms. Mercy Bisola Faleyimu (Nigeria)
  3. Ms. Aishat Lawal (Nigeria)
  4. Mr. Adeniyi Peter Olorundunsin (Nigeria)

Objectives

General Objective

• To elucidate the anti-colorectal cancer mechanisms of W. ugandensis using computational and network-based approaches.

Specific Objectives

1. To identify phytochemicals of W. ugandensis with potential anti-colorectal cancer activity through literature search and curated phytochemical databases.
2. To predict and analyze potential molecular targets using network pharmacology.
3. To perform molecular docking and molecular dynamics simulations to assess binding affinities of key phytochemicals to colorectal cancer targets.
4. To evaluate inflammatory pathway involvement and conduct enrichment analysis.
5. To assess the ADMET properties of the top-rated compounds.

Research Workflow

AntiCRCWU Project – Step-by-Step GUI Guide

Table of Contents

1. Install Required Software
2. Phytochemical Retrieval & Screening
3. Target Gene Prediction
4. Protein-Protein Interaction (PPI) Network
5. Functional Enrichment Analysis
6. Molecular Docking
7. Molecular Dynamics (Simplified Setup)
8. ADMET Profiling
9. Final Organization
10. Documentation

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1. Install Required Software

• Cytoscape: cytoscape.org → Download & install for your OS
• PyRx: pyrx.sourceforge.io → Download & install
• Discovery Studio Visualizer: BIOVIA Discovery Studio
• Optional: PyMOL: pymol.org

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2. Phytochemical Retrieval & Screening

2.1 Retrieve Phytochemicals from COCONUT

• Go to COCONUT → Search Warburgia ugandensis → Download all 2D structures
• Save as raw_data/coconut_phytochemicals.csv

2.2 Add Literature Compounds

• Collect from PubMed, Scopus, Web of Science, and Google Scholar
• Create raw_data/literature_compounds.csv with columns:
  Compound_Name, MW, LogP, HBD, HBA, MR, TPSA, RB

2.3 Screen Compounds Using Lipinski’s Rule of 5 and Veber’s Rule

• Go to SwissADME → Draw or paste SMILES → Run → Check rules
• Export table → processed_data/druglike_compounds.csv

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3. Target Gene Prediction

3.1 SwissTargetPrediction

• SwissTargetPrediction → Input compounds → Organism: Homo sapiens → Export CSV
• Save as processed_data/targets_[compound].csv

3.2 CRC Genes from GeneCards

• GeneCards → Search "colorectal cancer" → Download protein-coding genes
• Save as raw_data/genecards_crc_genes.txt

3.3 Find Overlapping Targets

• Venny: Venny → Paste SwissTargetPrediction & GeneCards lists → Save Venn diagram → figures/venn_diagram.png

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4. Protein-Protein Interaction (PPI) Network

4.1 Build PPI Network (STRING)

• STRING → Paste 411 common genes → Confidence 0.9 → Export TSV + Image
• Save as processed_data/string_network.tsv & figures/ppi_network.png

4.2 Identify Hub Genes (Cytoscape + CytoHubba)

• Import network → Apps → CytoHubba → MCC → Top 10 nodes
• Export table & network image:
  • processed_data/top_hub_genes.txt
  • figures/hub_genes_network.png

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5. Functional Enrichment Analysis

• SRplot → Functional Enrichment → Input 10 hub genes
• Download GO & KEGG images → Save in figures/

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6. Molecular Docking

6.1 Protein Structures

• RCSB PDB → Download PDBs for hub proteins → raw_data/PDB_structures/

6.2 Ligand Structures

• PubChem → Download 3D SDF → raw_data/ligands/

6.3 Prepare Proteins

• Discovery Studio → Remove water/heteroatoms → Add hydrogens → Save prepared PDBs → processed_data/

6.4 Docking with PyRx

• Import proteins & ligands → Convert ligands to PDBQT → Set grid → Run AutoDock Vina → Save CSV → results/docking_[protein]_[ligand].csv

6.5 Analyze Interactions

• Open complexes in Discovery Studio → Show 2D interactions → Save screenshots → figures/interaction_[complex].png

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7. Molecular Dynamics (Simplified Setup)

7.1 CHARMM-GUI Setup

• CHARMM-GUI → Solution Builder → Upload complex → Generate GROMACS input → processed_data/MD_setup_[complex].zip

7.2 Run Simulations (Command Line)

gmx mdrun -deffnm minim   # Energy minimization
gmx mdrun -deffnm nvt     # Equilibration NVT
gmx mdrun -deffnm npt     # Equilibration NPT
gmx mdrun -deffnm md_100ns # Production MD

7.3 Analyze MD

RMSD, RMSF, Rg, H-bonds → Plot with Grace/Xmgrace
Save plots → figures/MD_[analysis]_[complex].png

8. ADMET Profiling

• SwissADME → Export CSV → results/swissadme_results.csv
• ProTox-II → Predict toxicity → results/protox_results.csv

9. Final Organization

• Master Excel → results/final_results.xlsx
  • Sheets: Druglike_Compounds, Hub_Genes, Docking_Scores, MD_Analysis, ADMET_Properties
• Presentation → results/final_presentation.pptx → Include all figures

10. Documentation

• README.md → Include software versions, instructions, manual steps
• Ensure filenames match folder structure for reproducibility