Studio della subunità epsilon della DNA polimerasi III di Escherichia coli: stabilità e interazione con la subunità polimerasica

Faithful replication of DNA from one generation to the next is crucial for long-term species survival. Genomic integrity in prokaryotes, archaea and eukaryotes is dependent on efficient and accurate catalysis by multiple DNA polymerases. Escherichia coli possesses five known DNA polymerases (Pol). DNA polymerase III holoenzyme is the major replicative polymerase of the Escherichia coli chromosome (Kornberg, 1982). This enzyme contains two Pol III cores that are held together by a t dimer (Studwell-Vaughan and O’Donnell, 1991). The core is composed of three different proteins named α-, ε- and θ-subunit. The α-subunit, encoded by dnaE, contains the catalytic site for DNA polymerisation (Maki and Kornberg, 1985), the ε-subunit, encoded by dnaQ, contains the 3′→5′ proofreading exonuclease (Scheuermann, et al., 1983) and the θ-subunit, encoded by hole, that has no catalytic activity (Studwell-Vaughan, and O'Donnell, 1983). The three-subunit α–ε–θ DNA pol III complex is the minimal active polymerase form purified from the DNA pol III holoenzyme complex; these three polypeptides are tightly associated in the core (McHenry and Crow, 1979) Despite a wealth of data concerning the properties of DNA polymerase III in vitro, little information is available on the assembly in vivo of this complex enzyme. In this study it is shown that the C-terminal region of the proofreading subunit is labile and that the ClpP protease and the molecular chaperones GroL and DnaK control the overall concentration in vivo of ε. Two α-helices (comprising the residues E311-M335 and G339-D353, respectively) of the N-terminal region of the polymerase subunit were shown to be essential for the binding to ε. These informations could be utilized to produce a conditional mutator strain in which proofreading activity would be titrated by a a variant that can only bind e and that is polymerase-deficient. In this way the replication of DNA made by DNA Pol-III holoenzyme would accordingly become error-prone.

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.