Aims

• To describe methods for protein fold recognition.
• To introduce the problem of predicting associations between peptides and class II MHC molecules.
• To describe the structure of antibody variable domains.

Objectives

After this lecture you will:

• understand different approaches to protein fold recognition;
• be aware of how bioinformatics techniques can be used in predicting MHC-peptide interactions;
• be familiar with the structure of antibody variable domains, and the importance of the complementarity determining regions.

Supplementary Material

For more information on fold recognition and threading, I recommend the following early articles:

Bowie, J.U., Lüthy, R. and Eisenberg, D. (1991) A Method to Identify Protein Sequences That Fold into a Known Three-Dimensional Structure. Science, 253, 164-170.

Jones, D.T., Taylor, W.R. and Thornton, J.M. (1992) A new approach to protein fold recognition. Nature, 358, 86-89.

Sippl, M.J. and Weitckus, S. (1992) Detection of Native-Like Models for Amino Acid Sequences of Unknown Three-Dimensional Structure in a Data Base of Known Protein Conformations. Proteins: Structure, Function and Genetics, 13, 258-271 (Manfred Sippl's publication list)

The pairwise pseudo-energy terms and solvation potentials used in GenTHREADER are described in:

Jones, D.T. (1999) GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. J. Mol. Biol., 287, 797-815 (PubMed)

The structures of the major histocompatability complex, T-cell receptors and antibodies are introduced in the Protein Data Bank Molecule of the Month series.

For more information on modelling class II MHC molecules, see:

Swain, M.T., Brooks, A.J. and Kemp, G.J.L. (2001) An automated approach to modelling class II MHC alleles and predicting peptide binding. Proceedings of the Second IEEE International Symposium on Bio-Informatics and Biomedical Engineering, IEEE Computer Society Press, pp 81-88. (PDF)