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.

Effects of pesticides on honey bees (Apis mellifera L.): study of a specific route of exposure and evaluation of biochemical-physiological changes in the assessment of the pesticides toxicity

In this study, some important aspects of the relationship between honey bees (Apis mellifera L.) and pesticides have been investigated. In the first part of the research, the effects of the exposure of honey bees to neonicotinoids and fipronil contaminated dusts were analyzed. In fact, considerable amounts of these pesticides, employed for maize seed dressing treatments, may be dispersed during the sowing operations, thus representing a way of intoxication for honey bees. In particular, a specific way of exposure to this pesticides formulation, the indirect contact, was taken into account. To this aim, we conducted different experimentations, in laboratory, in semi-field and in open field conditions in order to assess the effects on mortality, foraging behaviour, colony development and capacity of orientation. The real dispersal of contaminated dusts was previously assessed in specific filed trials. In the second part, the impact of various pesticides (chemical and biological) on honey bee biochemical-physiological changes, was evaluated. Different ways and durations of exposure to the tested products were also employed. Three experimentations were performed, combining Bt spores and deltamethrin, Bt spores and fipronil, difenoconazole and deltamethrin. Several important enzymes (GST, ALP, SOD, CAT, G6PDH, GAPDH) were selected in order to test the pesticides induced variations in their activity. In particular, these enzymes are involved in different pathways of detoxification, oxidative stress defence and energetic metabolism. The results showed a significant effect on mortality of neonicotinoids and fipronil contaminated dusts, both in laboratory and in semi-field trials. However, no effects were evidenced in honey bees orientation capacity. The analysis of different biochemical indicators highlighted some interesting physiological variations that can be linked to the pesticide exposure. We therefore stress the attention on the possibility of using such a methodology as a novel toxicity endpoint in environmental risk assessment.