Rapporto tra struttura e funzione della cistatina B e dei suoi mutanti in relazione all'epilessia mioclonica progressiva di tipo 1 (EPM1)

This work shows for the first time that native CSTB polymerizes on addition of Cu2+ and DnaK (Hsp70). Cysteines are involved in the polymerization process and in particular at least one cysteine is necessary. We propose that Cu2+ interacts with the thiol group of cysteine and oxidize it. The oxidized cysteine modifies the CSTB structure allowing interaction with DnaK/Hsp70 to occur. Thus, Cu2+ binding to CSTB exposes a site for DnaK and such interaction allows the polymerization of CSTB. The polymers generated from native CSTB monomers, are DTT sensitive and they may represent physiological polymers. Denatured CSTB does not require Cu2+ and polymerizes simply on addition of DnaK. The polymers generated from denatured CSTB do not respond to DTT. They have characteristics similar to those of the CSTB toxic aggregates described in vivo in eukaryotic cells following CSTB over-expression. Interaction between CSTB and Hsp70 is shown by IP experiments. The interaction occurs with WT CSTB and not with the cys mutant. This suggests that disulphur bonds are involved. Methal-cathalyzed oxidation of proteins involves reduction of the metal ion(s) bound to the protein itself and oxidation of neighboring ammino acid residues resulting in structural modification and de-stabilization of the molecule. In this work we propose that the cysteine thyol residue of CSTB in the presence of Cu2+ is oxidized, and cathalyzes the formation of disulphide bonds with Hsp70, that, once bound to CSTB, mediates its polymerization. In vivo this molecular mechanism of CSTB polymerization could be regulated by redox environment through the cysteine residue. This may imply that CSTB physiological polymers have a specific cellular function, different from that of the protease inhibitor known for the CSTB monomer. This hypothesis is interesting in relation to Progressive Myoclonus Epilepsy of type 1 (EPM1). This pathology is usually caused by mutations in the CSTB gene. CSTB is a ubiquitous protein, but EPM1 patients have problems only in the central nervous system. Maybe physiological CSTB polymers have a specific function altered in people affected by EPM1.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.