From Protein to Drug Target

Slide Presentation — Lecture 1

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Lecture Notes

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Slide 1 — Drug Design with Computers: How Do We Turn a Protein into a Medicine?

Lecture 1 — From Protein to Drug Target

“It costs on average £1.5 billion and 10–15 years to bring one drug to market. Computational biology is changing that.”

Teaching note: Hold up a packet of ibuprofen and tell students: “The molecule in this tablet was designed to fit into a specific cavity in one protein in your body. We are going to simulate that interaction on a computer.”

Slide 2 — You already know more than you think

This module builds directly on your A-level biology knowledge.

Activation exercise (2 minutes): Turn to your partner and name one enzyme inhibitor you have heard of. Expected answers: statins, antibiotics, aspirin, ACE inhibitors.

Slide 3 — One question drives everything we will do

“A disease is caused by a protein working too well. How do we design a molecule to switch it off?”

Every project in this module answers one of these three questions:

• Which protein do we target?
• Where exactly on that protein does the molecule need to bind?
• What shape and chemistry does the molecule need to have?

This is your mental map for the entire term.

Slide 4 — The sequence is the blueprint; the shape is the machine

A protein’s primary structure (the amino acid sequence) is just a linear chain — the blueprint. When that chain folds, it creates a precise three-dimensional machine.

Analogy: Think of a Swiss Army knife. The steel it is made from is the sequence. The exact shape of each blade — the hook, the scissors, the flat blade — is the 3D structure. A screwdriver that has been bent out of shape no longer works, even though it is made of exactly the same steel. Proteins are the same: a misfolded protein cannot do its job.

The same protein chain always folds into the same 3D shape. That shape determines everything the protein can do — including whether a drug can bind to it.

Slide 5 — The 20 amino acids have very different personalities

We will not list all 20 here. What matters for drug design is the side chain — the part that varies between amino acids. Side chains are what make each amino acid different, and it is the side chains that determine how a drug binds.

Foreshadowing Projects 1 and 5: In our simulations, we will swap every amino acid for Alanine one at a time. Alanine is our “Swiss Army blank” — it switches off one residue’s contribution at a time so we can measure what that residue was doing.

Slide 6 — Disease often begins with a protein doing its job too well

Normal state: Stimulus → Enzyme active → Normal amount of product → Healthy outcome

Disease state: Overactivation → Enzyme too active → Too much product → Inflammation / infection

Example 1 — COX-2 and inflammation: COX-2 (Cyclooxygenase-2) converts arachidonic acid into prostaglandins, which cause pain and inflammation. When tissue is damaged, COX-2 is overproduced, leading to chronic inflammation and pain. COX-2 is the target of ibuprofen, and the protein we will dock drugs onto in Projects 3 and 4.

Example 2 — β-Lactamase and antibiotic resistance: Bacteria produce an enzyme called β-Lactamase that destroys penicillin by breaking its ring structure. Natural selection has simply favoured bacteria that happen to produce this enzyme. This is the protein we will model in Project 6.

Slide 7 — Not every protein is a good drug target

Three criteria determine whether a protein is worth targeting:

1. Essential to the disease process. If you switch it off, the disease gets better. Blocking COX-2 relieves pain. ✓

2. Has a binding pocket the drug can physically fit into. Many proteins are flat on the surface. Good drug targets have a deep, well-defined cavity. The COX-2 binding site is a long hydrophobic channel — an ideal pocket. ✓

3. Different enough from healthy human proteins. This prevents serious side effects. β-Lactamase exists only in bacteria; humans do not produce it. ✓

Counterexample: Early cancer drugs targeted rapidly dividing cells — which includes cancer cells, but also hair follicle cells. This is why chemotherapy causes hair loss. Modern targeted drugs look for proteins unique to the cancer cell: a far more precise strategy.

Slide 8 — Drugs work by getting in the way

Competitive inhibition: The drug resembles the natural substrate. It occupies the active site. The enzyme cannot process the real substrate because the drug is already sitting there.

Non-competitive inhibition: The drug binds to a separate allosteric site elsewhere on the protein. This causes the active site to change shape slightly — the real substrate no longer fits, even though the drug is not physically blocking the active site.

Both types work by changing the geometry of the active site. A drug that is the wrong shape simply does not fit, no matter how good its chemistry is.

Concrete numbers: Ibuprofen is approximately 0.5 nanometres across. The COX-2 binding site is about 0.6 nanometres wide at its narrowest point. If ibuprofen were even slightly larger, it would not fit at all. This is why drug design requires atomic-level precision.

Slide 9 — The Protein Data Bank (PDB)

The Protein Data Bank is a global, open-access database of experimentally determined protein structures. As of 2025, it holds over 220,000 structures. Every entry is freely available to anyone in the world.

How structures are deposited:

1. Scientists grow protein crystals in the laboratory
2. X-rays are fired through the crystal
3. The diffraction pattern is used to calculate atomic positions
4. The structure (3D coordinates of every atom) is deposited in the PDB

What a PDB entry contains:

• A unique 4-character ID — e.g. 1LYZ for Lysozyme, 4PH9 for COX-2 + Ibuprofen
• The 3D coordinates (x, y, z) of every atom in the protein
• The full amino acid sequence
• The experimental resolution in Ångströms
• Any bound ligands (drug molecules, cofactors, water)

Every simulation in this module begins with a real PDB structure. You are not working with theoretical proteins — you are working with proteins that have been physically crystallised and measured in a laboratory.

Live demo: Visit https://www.rcsb.org/3d-view/1LYZ to rotate a real Lysozyme structure in your browser. This is the protein you will use in Projects 1 and 2.

Slide 10 — Resolution: how sharp is the picture?

Resolution tells you how precisely the atomic positions in a structure were measured. It is expressed in Ångströms (Å) — a lower number means a sharper, more precise structure.

Think of three photographs of the same building: one perfectly sharp, one slightly blurry, one very blurry. Rosetta’s scoring function is sensitive to this sharpness — higher-quality structures generally receive lower (better) energy scores.

In Project 2, you will download five different crystal structures of Lysozyme and rank them by Rosetta energy score. You will find that higher-resolution structures tend to produce better scores. Choosing the right starting structure is step one of every real drug design project.

“Garbage in, garbage out.” If your starting structure has the binding site geometry wrong by 0.3 Å, your predicted binding energy will be wrong too.

Slide 11 — Ångströms: the unit of atomic distance

1 Ångström (Å) = 0.1 nanometres = 10⁻¹⁰ metres

Scale reference from large to small:

1 mm (flea) → 1 µm (bacterium) → 1 nm (protein) → 1 Å (bond length) → 0.1 Å (nucleus)

Distances you will encounter in the projects:

• A carbon–carbon bond is 1.54 Å
• A hydrogen bond is 1.8–3.5 Å
• The COX-2 binding channel is roughly 6–8 Å wide
• The ibuprofen molecule is about 8 Å across its longest axis

Practical rule for Project 4: We define “contact” between a drug and a protein residue as any atom-to-atom distance less than 5.0 Å. This is the van der Waals contact zone — close enough for atoms to feel each other’s presence. Beyond 8 Å, interactions are too weak to contribute meaningfully to binding.

Slide 12 — The Drug Discovery Pipeline

Developing a new drug takes 10–15 years from target identification to approval. Computational screening dramatically compresses the early stages.

Target ID → [COMPUTATIONAL SCREENING] → Hit ID → Lead Optimisation → Clinical Trials → Approval
 (2 yrs)         (weeks–months)          (1–2 yrs)    (3–4 yrs)          (6–7 yrs)

Real-world examples:

• Computational docking helped identify COVID-19 drug candidates within weeks of the virus’s structure being published.
• HIV protease inhibitors — antiretroviral drugs that transformed HIV from a death sentence to a manageable condition — were designed using structural computational methods.
• Imatinib (Gleevec), a leukaemia drug, was designed by targeting a specific protein conformation identified through structural biology.

Slide 13 — Your Computational Pipeline (Roadmap)

Here is what you will build across the six projects, and the scientific question each one answers:

“By the end of Project 6, you will have run the same computational pipeline that professional drug discovery teams use. The tools differ in scale — real projects use supercomputers — but the scientific logic is identical.”

Slide 14 — Summary and Preparation

Key takeaways from Lecture 1:

• A drug works by binding to a specific protein and disrupting its function
• The protein’s 3D shape — not just its sequence — determines what can bind to it
• A good drug target is essential to disease, has a binding pocket, and differs from healthy human proteins
• The Protein Data Bank holds over 220,000 real, experimentally determined 3D protein structures
• Resolution (Å) tells you how precisely a structure was measured — lower Å means better data
• Computational docking screens thousands of drug candidates in hours, saving years of laboratory work

Preparation for Lecture 2:

Think about this before our next session: If you unfold a protein — stretch it out into a straight line — does that require energy, or does energy get released? Why?

Key Vocabulary

Schedule