Immunological Bioinformatics

• list the structural and functional characteristics of MHC class I and II molecules, and their respective antigen processing pathways and ligands.
• describe the structural and functional differences between an antibody/BCR and a TCR.
• identify relevant immunological databases on the internet and extract desired data.
• identify the used germ-line genes in a final rearrangement of antibody encoding genes.
• explain what a Position Specific Scoring Matrix is and how a PSSM is used to create a sequence logo from a set of peptides.
• conceptually explain how an artificial neural network is constructed, trained and predictions are made, and illustrate their use in the different predictors.
• generalize advantages and limitations on applying predictors for peptide-MHCI/II interactions and linear/conformational B cell epitopes.
• select the appropriate tool for predicting: i. Peptide-MHCI/II binding (T-cell epitopes), ii. Linear/conformational B cell epitopes, iii. Interaction between TCR and pMHC complex and IV. T-Cell receptor and antibody structure.
• use different tools to identify allele frequencies and vaccine population coverage.
• use web-based tools for the analysis of repertoires of TCRs and BCRs.
• when presented with a proposed peptide vaccine, determine if it meets target disease criteria and population coverage and evaluate its potential effectiveness.
• using the knowledge obtained in the course by applying in silico methods, plan, conduct and present a research project to design i. A peptide vaccine, ii. A protein drug de-immunization.

The course aims to introduce the students to state-of-the-art methods within computational immunology.

There is a strong focus on introducing the methods in context with immunology as domain specific knowledge area. Furthermore, introduction to the theory of the methods will be followed by practical exercises, enabling the student to independently perform analyses. The course covers immunological bioinformatics and computational vaccinology with an outlook to infectious diseases, cancer immunotherapy, and autoimmunity.

The course is taught in two parts. Part 1 covers lectures and group based exercises and part 2 will cover group based project work aiming at creating a full project workflow.

22111/27322/22117, or equivalent. NB! Prerequisite courses in the DTU course base are recommended. This means, that they are not mandatory per se, but the learning objectives covered by the prerequisite courses are expected prior knowledge. Students who do not fulfill the prerequisites are expected to take independent responsibility for any missing learning objectives. This means, that it is not impossible to follow the course without having passed prerequisite courses, but an additional work effort may be needed. Expected prior knowledge covers basic immunology, basic bioinformatics, and basic knowledge of amino acids and proteins. Contact course responsible for any questions regarding this.

Lectures and exercises (students are required to bring a laptop, capable of connecting to the internet)