🧬 HIV Protease Sequence Analysis

This project focuses on analyzing HIV-1 protease sequences to study variability, conservation, and drug resistance mutations (DRMs).
It was developed as part of my bioinformatics + machine learning learning journey (B.Tech Biotechnology @ NIT Raipur).

---

Features

• Multiple Sequence Alignment (MSA) of HIV protease sequences
• Identification of conserved vs variable positions
• Overlay of known Drug Resistance Mutations (DRMs)
• Visualization with sequence logos and conservation plots
• Mutation frequency table generation for key positions

---

Project Workflow

1️⃣ Data Preparation

• Collected HIV-1 protease sequences (FASTA format).
• Cleaned and standardized dataset.
  

---

2️⃣ Multiple Sequence Alignment

• Performed MSA using Biopython + Clustal/MAFFT.
• Exported alignment in FASTA and Clustal format.
  

---

3️⃣ Conservation Analysis

• Calculated per-position conservation scores.
• Plotted conservation heatmaps & sequence logos.

---

4️⃣ Drug Resistance Mapping

• Mapped known Drug Resistance Mutations (DRMs) from Stanford HIVDB.
• Highlighted variable sites overlapping with DRMs.

---

5️⃣ Mutation Frequency Table

• Generated frequency table of amino acid substitutions at key residues.

---

📊 Results

• Identified highly conserved catalytic motifs.
• Highlighted hotspot residues linked to drug resistance.
• Visualizations provide insight into mutation patterns in HIV protease.

---

🛠️ Tech Stack

• Python (Biopython, Pandas, Matplotlib, Seaborn)
• MSA tools (MAFFT/Clustal Omega)

---

Future Scope

• Expand to machine learning models for predicting drug resistance.
• Incorporate phylogenetic tree analysis for evolutionary tracking.
• Automate into a lightweight web dashboard (MERN + ML).

---

📌 About

👤 Shubham Thakur
B.Tech Biotechnology, NIT Raipur
Exploring Bioinformatics | AI in Healthcare | Full-Stack Development

---

This project showcases my bioinformatics + coding skills, and is intended for research learning purposes.