Drug Design Research Programme

Why This Project?

Getting into a top university science programme is more competitive than ever. Strong grades are expected. What sets an application apart is genuine scientific experience — the ability to describe, in your own words, a real research question you investigated, the tools you used, and what you found.

This project gives you exactly that. You will not be watching videos or reading textbooks. You will be working directly with the same computational software used in professional drug discovery research, guided step by step, and producing results that are genuinely yours to write about.

What is the Project About?

Every medicine you have ever taken works by binding to a specific protein in your body. The shape of the drug must match the shape of the protein’s binding pocket — like a key fitting a lock. If the fit is good, the drug works. If a single amino acid in the protein mutates and changes the shape of the lock, the drug may stop working entirely. This is how antibiotic resistance develops.

In this project, you will investigate that process from the molecular level up — first seeing it with your own eyes in 3D, then calculating it computationally.

What Will You Actually Do?

The project is structured as a series of hands-on tutorials across two software platforms used daily in academic and pharmaceutical research:

Part 1 — Visualising Proteins with PyMOL

PyMOL is the industry-standard tool for viewing 3D molecular structures. You will:

• Load real protein structures directly from the global Protein Data Bank — a database of over 200,000 experimentally determined structures freely available to scientists worldwide
• Explore a protein’s architecture: its helices, sheets, surface shape, and binding pocket
• Find a drug molecule sitting inside its target protein and identify every amino acid touching it
• Measure the distances between drug atoms and surrounding residues, and display the hydrogen bonds holding the drug in place
• Compare three real pain-relief drugs — Ibuprofen, Celecoxib, and Meloxicam — all targeting the same protein, side by side, to see how their different molecular shapes lead to different binding behaviour
• Produce publication-quality molecular images for your portfolio

Part 2 — Computational Drug Design with PyRosetta

PyRosetta is a Python-based platform built on the Rosetta molecular modelling suite, used in universities and pharmaceutical companies for protein engineering and drug design. You will:

• Load the same proteins you visualised in PyMOL and calculate their stability scores using Rosetta’s physics-based energy function
• Perform alanine scanning on Lysozyme — systematically mutating each amino acid to alanine and measuring which positions are critical for protein stability. This is a standard technique used to map the functional importance of residues
• Calculate binding energies for Ibuprofen, Celecoxib, and Meloxicam against COX-2, and rank them computationally — connecting what you saw visually in PyMOL to quantitative scores
• Simulate clinically documented antibiotic resistance mutations in TEM-1 β-Lactamase, a bacterial enzyme that destroys penicillins. You will model the real mutations found in drug-resistant patients and calculate exactly how much each one weakens the antibiotic’s ability to bind

Skills You Will Master

By the end of this programme, you will have hands-on experience with skills that are directly relevant to university-level biochemistry and biomedical science — and that most of your peers will not have encountered until their second or third year of university.

Scientific thinking

• Reading and interpreting a protein structure from the Protein Data Bank
• Designing a computational experiment, collecting results, and drawing conclusions from data
• Understanding how molecular interactions (hydrogen bonds, hydrophobic contacts) translate into measurable binding energy
• Reasoning about how a single point mutation at the molecular level leads to clinical drug resistance

Computational and data skills

• Writing and running Python scripts in a Jupyter Notebook environment
• Using loops and data structures to automate repetitive experiments across multiple conditions
• Organising results into structured data tables using pandas
• Producing publication-quality bar charts and figures using matplotlib
• Interpreting quantitative scores (Rosetta Energy Units) to rank and compare drug candidates

Molecular visualisation

• Navigating a 3D molecular viewer with professional-level precision
• Selecting, colouring, and labelling specific atoms, residues, and molecular regions
• Measuring inter-atomic distances and identifying hydrogen bond networks
• Aligning multiple protein structures on top of each other for direct visual comparison
• Rendering and exporting high-resolution images suitable for a portfolio or report

Communication

• Translating computational results into clear scientific language
• Describing your methodology and findings in a way that is accurate, concise, and persuasive — skills directly transferable to a personal statement or university interview

Tools You Will Use

All software used in this programme is free, open-source, and used daily in universities and pharmaceutical research companies worldwide. You will leave the programme not just having used these tools, but understanding what they do and why.

You will receive step-by-step installation guides for all software before the programme begins, and full support in setting everything up on your own computer — whether Windows or Mac.

What Will You Produce?

By the end of the programme, you will have:

• A portfolio of molecular figures — rendered, labelled images of protein structures and drug binding pockets that you produced yourself
• A data table and bar chart of mutation effects on protein stability
• A drug comparison analysis ranking three real drugs by computed binding energy
• A resistance mutation profile for a clinically relevant antibiotic target, with your own interpretation of the results
• Enough understanding of the underlying biology and computational methods to write about this work confidently in a personal statement

Who is This For?

This project is designed for students who:

• Are interested in applying for Biology, Chemistry, Biochemistry, Biomedical Science, Pharmacology, or Medicine
• Want to demonstrate genuine scientific engagement beyond the classroom in their university application
• Have no prior programming experience — all code is provided and explained line by line
• Are willing to engage seriously with the material over the duration of the programme

No prior knowledge of protein biology or computational science is assumed. Everything is introduced from scratch.

Programme Details

Apply

Places are limited. To enquire or apply, contact Michelle Chen at chensimichelle@gmail.com.