Energy, Stability and the Rosetta Score

Slide Presentation — Lecture 2

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Lecture Notes

Slide 1 — Energy, Stability and the Rosetta Score

Lecture 2 — From Physical Forces to a Single Number

Opening question (show of hands):

“If you unfold a protein — stretch it out into a straight line — does that require energy, or does energy get released? Why?”

The correct answer: unfolding requires energy input — meaning the folded state is lower energy and more stable. This is the conceptual foundation for everything in this lecture.

Slide 2 — Why does stability matter for drug design?

Stable protein ✓

• Maintains its 3D shape under physiological conditions
• Binding site geometry is consistent and predictable
• Drug binds reliably every time — good drug target

Unstable protein ✗

• Fluctuates between multiple shapes
• Binding site geometry changes unpredictably
• Drug may bind sometimes but not others — unreliable drug target

Before we can design a drug to fit a binding site, we need to know that binding site holds its shape. Measuring stability is step zero of drug design.

In Project 1 you will measure the stability of Lysozyme and test how single amino acid changes affect that stability. This slide explains why that measurement is meaningful.

Slide 3 — Four forces hold every protein together

1. Van der Waals interactions Weak attractive forces between any two atoms that are very close together (3.5–5.0 Å). Like two pieces of cling film pressed together — individually almost nothing, but thousands of them across a whole protein surface add up to something substantial.

2. Hydrogen bonds An attraction between a hydrogen atom attached to N or O, and another N or O nearby (1.8–3.5 Å). Like a firm handshake — directional, specific, and relatively strong compared to van der Waals.

3. Electrostatic interactions Attraction between oppositely charged amino acids (e.g. positive Arginine near negative Aspartate). Like opposite poles of a magnet — they pull toward each other across a slightly longer range than hydrogen bonds.

4. The hydrophobic effect Non-polar amino acids cluster together in the protein core to avoid contact with water. Like oil in water — they group together not because they attract each other strongly, but because water molecules strongly prefer to hydrogen-bond with each other rather than surround non-polar molecules.

Rosetta’s scoring function calculates all four of these forces simultaneously, for every pair of atoms in the protein. The total is the Rosetta score.

Slide 4 — Proteins fold because the folded state is more stable

An energy diagram with two states:

• Unfolded — high energy, unstable
• Folded — low energy, stable

Three key points:

1. Lower energy = more stable. The folded protein sits in an energy valley. It needs energy input to unfold.
2. The depth of the valley = stability. A very stable protein has a deep valley. A marginally stable one has a shallow valley and unfolds more easily under stress.
3. Mutations can change the valley depth. In Project 1 we ask: does this mutation make the valley deeper (more stable) or shallower (less stable)?

Concrete example: Lysozyme is found in egg whites and human tears. It can withstand temperatures up to 75°C before unfolding. That stability comes from a particularly favourable network of hydrogen bonds and hydrophobic contacts throughout its core. In Project 1 you will see exactly which amino acids contribute most to that stability.

Slide 5 — Scientists measure stability using free energy — ΔG

ΔG (delta G) tells you whether a process is favourable or not.

Important caveat: Rosetta does not calculate true thermodynamic free energy — it uses an approximation that correlates well with real stability measurements but is not an exact free energy. This is why we use REU (Rosetta Energy Units) rather than kJ/mol.

Slide 6 — Rosetta’s scoring function: a stability calculator

“Rosetta adds up all non-covalent interactions across every atom in the protein and produces a single number representing overall stability.”

You do not need to memorise these terms. The key concept is that the score is a sum of many physical contributions — the get_fa_scorefxn() function you call in Project 1 stands for “full atom scoring function” and examines every single atom in the protein.

Slide 7 — REU: the unit you will see in every project

The key rule: Lower REU = more stable protein. More negative = better.

When you run Cell 3 of Project 1, you will see a number like −1,742.38 REU printed on your screen. That number is the sum of every van der Waals contact, every hydrogen bond, every electrostatic interaction in Lysozyme — a single number capturing the entire physical chemistry of a real protein.

Slide 8 — Score per residue: making fair comparisons

A larger protein always has a more negative raw score simply because it has more atoms. Dividing by residue count makes comparisons fair.

2LZT looks better by raw score — but per residue it is actually the weaker structure. Normalisation tells the true story.

In Project 2 you will download five different crystal structures of Lysozyme and rank them by score per residue. This normalisation step is what makes the comparison valid.

Slide 9 — Every measurement needs a reference point

A baseline is the original, unmodified measurement that all results are compared against — the same idea as a control group in a science experiment.

In Project 1, before mutating any amino acid, we score the original Lysozyme:

original_score = scorefxn(pose)   # this is the baseline
difference = mutant_score - original_score

• Positive difference → mutation made the protein less stable (destabilising)
• Negative difference → mutation made the protein more stable (stabilising)

Analogy: Measuring your running speed after changing shoes only means something if you know your original time. The original time is your baseline. The difference tells you whether the new shoes helped or hurt.

Slide 10 — Project 1 in one picture

The full computational pipeline for Project 1:

1. Load Lysozyme (1LYZ)
2. Score with get_fa_scorefxn() → record as baseline
3. Clone the protein
4. Mutate one residue to Alanine
5. Score the mutant → calculate difference (mutant − original)

6. Positive → Destabilising

   Negative → Stabilising

7. Repeat for 7 positions → plot bar chart

Red bars = destabilising mutations (the protein needs that amino acid to stay stable). Green bars = stabilising mutations (removing that amino acid actually helps). The height of each bar tells you how important that position is.

Preview of Project 2: In Project 2 you apply the same scoring logic — but instead of mutating one protein, you download five different crystal structures of the same protein and compare their scores. You are asking: which experimentalist did the best job solving this structure?

Slide 11 — Summary and Preparation

Key takeaways from Lecture 2:

• Proteins are stable because the folded state has lower free energy than the unfolded state
• Four non-covalent forces hold proteins together: van der Waals, hydrogen bonds, electrostatics, and the hydrophobic effect
• ΔG < 0 means a process is favourable — both protein folding and drug binding have negative ΔG
• The Rosetta scoring function sums all non-covalent interactions across every atom to produce a single stability score in REU
• Lower REU = more stable — more negative is always better in Rosetta
• Dividing by residue count gives a normalised score for fair comparisons between proteins of different sizes

Preparation for Lecture 3:

“Ibuprofen is a non-polar molecule with a large hydrophobic region. The COX-2 binding site is also largely hydrophobic. Why does this chemical match matter? What would happen if you tried to fit a highly charged, polar drug into a hydrophobic pocket?”

Key Vocabulary

Schedule