Genome Sequencing & Proteome Determination Revision Notes | A Level AQA Biology

1. Why Reading the Genome Matters

Imagine having the complete instruction manual for a living organism — every gene, every regulatory switch, every protein blueprint — written out in full. That is exactly what genome sequencing projects have achieved, and the consequences for medicine, agriculture, and evolutionary biology have been profound.

Sequencing projects have now read the genomes of a wide range of organisms: bacteria, yeast, fruit flies, mice, rice, and humans, among many others. Each completed genome is a resource that researchers can mine for decades, asking questions that were not even imaginable before the sequence existed.

The Human Genome Project, completed in 2003, was the landmark achievement in this field. It revealed approximately 20,000–25,000 protein-coding genes in the human genome — far fewer than many scientists had predicted — and opened the door to personalised medicine, genetic disease research, and a new understanding of human evolution.

2. Genome Sequencing: The Scope of the Projects

Sequencing projects have targeted organisms across the full breadth of life. Bacteria such as Haemophilus influenzae were among the first to have their genomes fully sequenced (1995). Since then, thousands of bacterial, archaeal, fungal, plant, and animal genomes have been completed.

The choice of organism is rarely arbitrary. Model organisms — species that are well-understood, straightforward to grow, and genetically tractable — are prioritised because their genomes act as reference points. Sequencing Saccharomyces cerevisiae (baker's yeast) illuminated fundamental eukaryotic cell biology; sequencing Drosophila melanogaster (fruit fly) revealed conserved developmental genes shared with humans.

• Prokaryotic genomes: typically 1–10 Mb, sequenced rapidly and used to study pathogen biology
• Eukaryotic model organisms: yeast (~12 Mb), fruit fly (~180 Mb), mouse (~2.7 Gb)
• Human genome: ~3.2 Gb, containing roughly 20,000–25,000 protein-coding genes
• Agricultural species: rice, wheat, and maize genomes sequenced to improve crop yields and disease resistance

3. From Genome to Proteome

Knowing the DNA sequence is only the starting point. The real power comes from what that sequence encodes. Once the genome of an organism is known, bioinformatics tools can scan the sequence for open reading frames — stretches of DNA that begin with a start codon and end with a stop codon — and predict the proteins those genes will produce.

This is where simpler organisms become especially useful. A bacterium with a genome of 4,000 genes has a relatively manageable proteome. Researchers can identify every open reading frame, predict each protein's amino acid sequence from the genetic code, and then verify those predictions experimentally. The logic is direct: DNA base sequence → (transcription) → mRNA codon sequence → (translation) → amino acid sequence → (folding) → protein structure and function.

For more complex eukaryotes, the relationship between genome and proteome is less straightforward. Alternative splicing means a single gene can produce multiple different proteins. Post-translational modifications (phosphorylation, glycosylation, and so on) further diversify the proteome beyond what the genome alone predicts. This is why the human proteome is estimated to contain well over 100,000 distinct protein forms, despite only ~20,000 protein-coding genes.

4. Determining the Proteome: The Step-by-Step Logic

The pathway from genome sequence to proteome determination follows a clear logical chain. Working through it step by step makes the process concrete and gives you the language to answer 'explain' questions in the exam.

Worked example: From Genome Sequence to Protein Identification

Describe how determining the genome sequence of a simple organism allows its proteome to be determined.

1. Use sequencing technology (e.g. next-generation sequencing) to read the complete base sequence of the organism's DNA. — This gives the full order of A, T, G, and C bases across every chromosome or plasmid in the organism.
2. Bioinformatics software scans the sequence for start codons (ATG) followed by a long stretch of codons before a stop codon (TAA, TAG, or TGA). — Each ORF is a candidate protein-coding gene. In bacteria, most ORFs do encode proteins; in eukaryotes, additional evidence (e.g. comparison with known genes) is needed.
3. Each codon in the ORF is translated using the standard genetic code to predict the amino acid sequence of the resulting polypeptide. — Because the genetic code is universal (with minor exceptions), the same codon table applies across virtually all organisms.
4. The predicted amino acid sequence is compared against databases of known proteins. Structural prediction tools (e.g. AlphaFold) can model the 3D shape. — Sequence similarity to a known protein strongly suggests a shared function — this is the basis of comparative genomics.
5. Techniques such as mass spectrometry, 2D gel electrophoresis, and Western blotting confirm which proteins are actually expressed and in what quantities. — Computational prediction identifies candidate proteins; experimental proteomics confirms their existence and abundance in the cell.

Answer: The genome sequence reveals all potential protein-coding genes. Applying the genetic code to each gene predicts the amino acid sequence of every protein. Together, these predicted (and experimentally verified) proteins constitute the proteome of the organism.

5. Applications: Vaccine Development and Beyond

Knowing the proteome of a pathogen is not merely an academic exercise — it has direct, life-saving applications. The most important for AQA is the identification of potential antigens for use in vaccine production.

Here is the core logic: a vaccine works by presenting the immune system with an antigen — typically a protein on the surface of a pathogen — so that memory cells are formed without the person suffering the disease. To design a vaccine, you need to know which proteins the pathogen expresses, particularly those on its outer surface that the immune system can recognise.

Genome and proteome data make this process systematic. Once the genome of a bacterium or virus is sequenced, researchers can scan the predicted proteome for proteins that are likely to be surface-exposed, conserved across strains, and immunogenic (capable of triggering an immune response). This approach — called reverse vaccinology — was used to develop a vaccine against Neisseria meningitidis group B, a bacterium that causes meningitis.

Beyond vaccines, proteome data from genome projects has a wide range of applications. These are worth knowing for 'suggest' questions, where the examiner expects you to apply your understanding to novel contexts.

• Drug target identification: proteins unique to a pathogen (absent from human cells) are safe targets for new antibiotics or antivirals
• Diagnostic markers: proteins expressed specifically during disease can serve as biomarkers for early detection
• Understanding pathogen virulence: identifying toxins and adhesion proteins explains how a pathogen causes disease
• Comparative proteomics: comparing the proteomes of related organisms reveals evolutionary relationships and conserved biological processes
• Agricultural biotechnology: proteome data from crop pathogens informs the development of disease-resistant plant varieties

6. Comparing Genome Complexity and Proteome Size

A common point of confusion is the relationship between genome size, gene number, and proteome complexity. These do not scale in a simple linear way, and understanding why is a mark-winning insight at A Level.

Notice that humans have only about five times more protein-coding genes than yeast, yet the human proteome is orders of magnitude more complex. The key mechanisms driving this are alternative splicing (one gene, multiple mRNA transcripts, multiple proteins) and post-translational modifications. This is why the proteome cannot be read directly off the genome in complex eukaryotes — it requires experimental investigation alongside computational prediction.

7. Exam Technique and Mark-Winning Strategies

Questions on genome sequencing and proteome determination tend to fall into a few predictable patterns. Knowing how to structure your answers for each command word will protect your marks.

For 'explain' questions linking genome to proteome, the examiner is looking for a clear causal chain: genome sequence → identification of genes → application of genetic code → amino acid sequence → protein. Each arrow in that chain is a potential mark. Do not skip steps, even if they feel obvious.

For 'suggest' questions about applications, draw on the principle that proteome data reveals what proteins an organism makes. Any application that depends on knowing an organism's proteins — vaccines, drug targets, diagnostics — is a valid suggestion. Justify your suggestion with a biological mechanism, not a vague statement.