BMS5310 · AI integrated proteomics and protein design

Overview

In this unit you will explore proteomics as a discipline of biological inference rather than simple protein cataloguing. You will develop a conceptual framework by contrasting proteomics with genomics and transcriptomics, and by examining challenges such as dynamic range, post-translational modifications, and proteoforms. You will learn the principles of protein chemistry and mass spectrometry, including how peptides are generated, detected, and computationally interpreted. Through guided analysis of real datasets, you will perform peptide identification, protein inference, and quantitative analysis, with emphasis on false discovery control, ambiguity, and probabilistic reasoning. You will design proteomics workflows and evaluate how experimental choices shape downstream statistical and biological conclusions. You will apply statistical and computational approaches to interpret proteomics data at functional and systems levels, and use these insights to generate and test biological hypotheses. You will also compare data-driven and physics-based approaches to protein structure prediction and design, and apply modern tools to biological and engineering problems. Throughout the unit, you will engage in critical discussion of limitations, reproducibility, and responsible scientific interpretation, alongside emerging areas such as AI-assisted protein design.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Explain the principles of proteomics and evaluate its role alongside genomics and transcriptomics in biological analysis.
2. Analyse and interpret mass spectrometry proteomics data.
3. Design quantitative proteomics experiments and workflows.
4. Interpret proteomics data to generate and evaluate biological hypotheses, and communicate research findings effectively to scientific and broader audiences.
5. Compare data-driven and physics-based approaches to protein modelling and design.
6. Apply modern protein prediction and design tools to biological or engineering problems.

Workload

Average of 12 hours teacher-directed learning / week (on-campus workshops, online learning materials) plus 12 hours student-directed learning. Total per week = 24 hours

Student experience, not official rules. Nothing here changes what the Handbook says.