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Pseudomonas aeruginosa is an opportunistic human pathogen that can infect almost every human tissue if immunity barriers are lowered. Pseudomonas aeruginosa produces two lectins associated with its virulence, D galactose and L-fucose binding lectins, PA-IL and PA-IIL respectively. In contrast with most lectins that display only weak affinity for monosaccharide ligands, the equilibrium dissociation constant of PA-IIL/L-fucose interaction is in the micromolar range [1]. Database searching in newly sequenced bacterial genomes revealed the presence of PA-IIL like proteins within limited number of other opportunistic pathogens. One of them, the close sequence homologue from Ralstonia solanacearum (called RS-IIL), has been recently characterised. Crystal structure of RS-IIL lectin has been solved and showed very high structure similarity with PA-IIL. Both are tetramers with two calcium-mediated sugar binding. The only difference is in a three amino acid motif of the ligand binding loop that is responsible for lectin reverse order preferences toward different monosaccharides. PA-IIL prefers L-fucose and L-galactose whilst RS-IIL prefers D-mannose and D-fructose [2]. In order to characterise the contribution of particular amino acids for such a difference, mutants of PA-IIL concerning Ser22, Ser23 and Gly24 that are responsible for the high preference of PA-IIL to fucose have been designed. Mutants have been prepared by site-directed mutagenesis and purified by affinity chromatography on Mannose-agarose. Interaction of mutants with different monosaccharides has been evaluated using isothermal titration microcalorimetry and some complexes have been crystallised. Experiments showed the importance of amino acid residue on position 22 and opened the feasibility for prediction of specificity for the other PA-IIL-like lectins. Simultaneously, the program TRITON [3], a graphical tool for modelling protein mutants, which has been under development in our laboratory, was used for designing protein mutants in silico. Further docking studies allowed prediction of changes in binding abilities of designed proteins. [1] Mitchell E.P., Sabin C., Šnajdrová L., Pokorná M., Perret S., Gautier C., Hofr C., Gilboa-Garber N., Koča J., Wimmerová M., Imberty A.: Proteins: Structure, Function, and Bioinformatics, 58, 735 - 746 (2005) 2 Sudakevitz D., Kostlánová N., Blatman-Jan G., Mitchell E., Lerrer B., Wimmerová M., Katcoff D.J., Imberty A., Gilboa-Garber N.: Mol. Microbiol. 52, 691-700 (2004) [3] Prokop M., Damborský J., Koča J.: Bioinformatics. 16, 845-846 (2000).
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