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.

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.