BMS5021 · Introduction to Bioinformatics
Introduction to Proteomics & Personalised Medicine
Week 4 of Monash University BMS5021 moves from the genome to the proteome and closes Topic 1. It explains why the protein complement matters beyond the gene sequence, how proteome complexity vastly exceeds gene number, why mRNA and protein abundance do not always correlate, and how bioinformatics underpins personalised (precision) medicine. This is the last block tested by the Topic 1 MCQ quizzes and it motivates the whole of Topic 3 — so expect conceptual items on the genome-transcriptome-proteome scale-up and on why proteomics is needed alongside transcriptomics.
What this chapter covers
- 01Genome (fixed sequence) vs transcriptome (varies with time/conditions) vs proteome (all proteins expressed at a given time)
- 02Proteome complexity scale-up: ~20-25,000 genes -> ~100,000 transcripts -> >1,000,000 proteins/proteoforms
- 03Sources of extra proteoforms: alternative promoters, alternative splicing, mRNA editing and post-translational modifications (PTMs)
- 04mRNA-protein abundance correlation is gene- and condition-specific (sometimes strong, sometimes weak) - a key reason proteomics is needed
- 05NGS vs proteomics: 4 nucleotides, amplifiable, scalable vs 20 amino acids, >300 PTMs, not amplifiable, harder to quantify
- 06Mass spectrometry measures the mass-to-charge ratio (m/z) of ions - only charged molecules can be measured
- 07Personalised / precision medicine: using a person's own genes and proteins to prevent, diagnose or treat disease
- 08Bioinformatics is central to managing and interpreting the resulting data (ULO1, ULO2)
Why the proteome is bigger than the genome (Topic 1 short-answer)
- +1Give the scale-up. The genome holds roughly 20,000-25,000 protein-coding genes, which give rise to on the order of 100,000 transcripts, which in turn yield more than 1,000,000 distinct proteins/proteoforms. So the number grows by roughly an order of magnitude at each step.
- +1Name the mechanisms. A single gene generates many transcripts and proteoforms through alternative promoters, alternative splicing and mRNA editing (increasing transcript diversity), and then through post-translational modifications (PTMs) such as phosphorylation, acetylation and glycosylation on the resulting proteins. There are more than 300 identified PTM types.
- +1Explain the correlation gap. mRNA and protein abundance are only correlated in a gene- and condition-specific way - sometimes strongly, sometimes weakly - because protein levels are also set by translation rate, protein stability/degradation and PTM state, none of which are captured by counting transcripts. So transcriptomics alone cannot predict the proteome, which is why proteomics is needed.
Key terms
- Proteome
- The entire set of proteins expressed by a genome at a given time and under given conditions. It is far larger than the gene count because of alternative splicing and post-translational modifications.
- Proteoform
- A specific molecular form of a protein arising from a given gene, including splice variants and post-translational modifications; a single gene can yield many proteoforms.
- Post-translational modification (PTM)
- A chemical change to a protein after translation (e.g. phosphorylation, acetylation, methylation, glycosylation, ubiquitination). More than 300 types are known; PTMs alter protein function, localisation and stability.
- Mass-to-charge ratio (m/z)
- The quantity a mass spectrometer measures - the mass of an ion divided by its charge. Only charged (ionised) molecules can be measured, which is why proteins are ionised before analysis.
- Transcriptome
- The complete set of RNA transcripts present in a cell at a given time; unlike the fixed genome it varies with cell type, time and conditions.
- Personalised / precision medicine
- Medical care that uses a person's own genes and proteins to prevent, diagnose or treat disease; in cancer it uses tumour-specific information for diagnosis, treatment planning, monitoring and prognosis, with bioinformatics managing the data.
Introduction to Proteomics & Personalised Medicine FAQ
How does Week 4 appear in the assessment?
It is the final Topic 1 block, tested by the in-workshop MCQ quizzes (30% across Weeks 1-4). Expect conceptual items on the genome-transcriptome-proteome scale-up, the mechanisms that expand the proteome, why mRNA does not predict protein, and the definition of personalised medicine. It also sets up Topic 3 (mass-spectrometry proteomics), so understanding it well pays off later. Confirm quiz timing on Moodle.
Why measure proteins if we already have the genome and transcriptome?
Because proteins are the functional molecules and their abundance is not predictable from the gene or transcript. A single gene can produce many proteoforms through alternative splicing and post-translational modifications, and mRNA-protein correlation is only gene- and condition-specific. Proteomics therefore captures information - PTM state, protein-level regulation, complexes - that sequencing simply cannot.
What makes proteomics harder than sequencing?
Sequencing deals with just four nucleotides, and DNA can be amplified (PCR), multiplexed and read at scale, making it relatively easy to quantify. Proteins are built from 20 amino acids, carry more than 300 kinds of modification, act in complexes, and cannot be amplified - so proteomics is intrinsically harder to quantify and analyse. That difficulty is a recurring theme of Topic 3.
Can AI help me connect the genome to the proteome?
Yes. Sia can walk you through the genome -> transcriptome -> proteome scale-up, explain how splicing and PTMs multiply diversity, and re-explain the mRNA-protein correlation gap until it clicks. It helps you understand and rehearse the concept; it does not complete your graded quiz, and Monash University academic-integrity rules apply.
Exam move
This week is conceptual, so aim to be able to EXPLAIN, not just recall. Fix the three numbers (~25k genes, ~100k transcripts, >1M proteoforms) and the two expansion mechanisms (alternative splicing/editing, then PTMs) as a single mental picture, and be ready to say in one or two sentences why transcript abundance cannot predict protein abundance. Note the NGS-versus-proteomics contrast (amplifiable/4 bases vs not-amplifiable/20 amino acids/>300 PTMs) because it frames why Topic 3 is harder. Do the pre-workshop material first, then use Sia to test your explanations and to bridge this week into the mass-spectrometry chapters. Confirm quiz dates and rules on Moodle.
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