S-nitrosylation homeostasis in plants: key enzymes and regulatory pathways

The nitrosylated form of glutathione (GSNO) has been acknowledged to be the most important nitrosylating agent of the plant cell, and the tuning of its intracellular concentration is of pivotal importance for photosynthetic life. During my time as a PhD student, I focused my attention on the enzymatic systems involved in the degradation of GSNO. Hence, we decided to study the structural and catalytic features of alcohol dehydrogenases (GSNOR and ADH1) from the model land plant Arabidopsis thaliana (At). These enzymes displayed a very similar 3D structure except for their active site which might explain the extreme catalytic specialization of the two enzymes. They share NAD(H) as a cofactor, but only AtGSNOR was able to catalyze the reduction of GSNO whilst being ineffective in oxidizing ethanol. Moreover, our study on the enzyme from the unicellular green alga Chlamydomonas reinhardtii (Cr) revealed how this S-nitrosoglutathione reductase (GSNOR) specifically use NADH to catalyze GSNO reduction and how its activity responds to thiol-based post-translational modifications. Contextually, the presence of NADPH-dependent GSNO-degrading systems in algal protein extract was highlighted and resulted to be relatively efficient in this model organism. This activity could be ascribed to several proteins whose contribution has not been defined yet. Intriguingly, protein extract from GSNOR null mutants of Arabidopsis displayed an increased NADPH-dependent ability to degrade GSNO and our quantitative proteome profiling on the gsnor mutant revealed the overexpression of two class 4 aldo-keto reductases (AKR), specifically AtAKR4C8 and AtAKR4C9. Later, all four class 4 AKRs showed to possess a NADPH-dependent GSNO-degrading activity. Finally, we initiated a preliminary analysis to determine the kinetic parameters of several plant proteins, including GSNOR, AKR4Cs, and thioredoxins. These data suggested GSNOR to be the most effective enzyme in catalyzing GSNO reduction because of its extremely high catalytic proficiency compared to NADPH-dependent systems.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.