Computational methods for genome screening

Motivation An actual issue of great interest, both under a theoretical and an applicative perspective, is the analysis of biological sequences for disclosing the information that they encode. The development of new technologies for genome sequencing in the last years, opened new fundamental problems since huge amounts of biological data still deserve an interpretation. Indeed, the sequencing is only the first step of the genome annotation process that consists in the assignment of biological information to each sequence. Hence given the large amount of available data, in silico methods became useful and necessary in order to extract relevant information from sequences. The availability of data from Genome Projects gave rise to new strategies for tackling the basic problems of computational biology such as the determination of the tridimensional structures of proteins, their biological function and their reciprocal interactions. Results The aim of this work has been the implementation of predictive methods that allow the extraction of information on the properties of genomes and proteins starting from the nucleotide and aminoacidic sequences, by taking advantage of the information provided by the comparison of the genome sequences from different species. In the first part of the work a comprehensive large scale genome comparison of 599 organisms is described. 2,6 million of sequences coming from 551 prokaryotic and 48 eukaryotic genomes were aligned and clustered on the basis of their sequence identity. This procedure led to the identification of classes of proteins that are peculiar to the different groups of organisms. Moreover the adopted similarity threshold produced clusters that are homogeneous on the structural point of view and that can be used for structural annotation of uncharacterized sequences. The second part of the work focuses on the characterization of thermostable proteins and on the development of tools able to predict the thermostability of a protein starting from its sequence. By means of Principal Component Analysis the codon composition of a non redundant database comprising 116 prokaryotic genomes has been analyzed and it has been showed that a cross genomic approach can allow the extraction of common determinants of thermostability at the genome level, leading to an overall accuracy in discriminating thermophilic coding sequences equal to 95%. This result outperform those obtained in previous studies. Moreover, we investigated the effect of multiple mutations on protein thermostability. This issue is of great importance in the field of protein engineering, since thermostable proteins are generally more suitable than their mesostable counterparts in technological applications. A Support Vector Machine based method has been trained to predict if a set of mutations can enhance the thermostability of a given protein sequence. The developed predictor achieves 88% accuracy.

Biomimetic Scaffolds for the Controlled Release of Bioactive Molecules for Tissue Engineering Applications

The temporospatial controlled delivery of growth factors is crucial to trigger the desired healing mechanisms in target tissues. The uncontrolled release of growth factors has been demonstrated to cause severe side effects in its surrounding tissues. Thus, the first working hypothesis was to tune and optimize a newly developed multiscale delivery platform based on a nanostructured silicon particle core (pSi) and a poly (dl-lactide-co-glycolide) acid (PLGA) outer shell. In a murine subcutaneous model, the platform was demonstrated to be fully tunable for the temporal and spatial control release of the payload. Secondly, a multiscale approach was followed in a multicompartment collagen scaffold, to selectively integrate different sets of PLGA-pSi loaded with different reporter proteins. The spatial confinement of the microspheres allowed the release of the reporter proteins in each of the layers of the scaffold. Finally, the staged and zero-order release kinetics enabled the temporal biochemical patterning of the scaffold. The last step of this PhD project was to test if by fully embedding PLGA microspheres in a highly structured and fibrous collagen-based scaffold (camouflaging), it was possible to prevent their early detection and clearance by macrophages. It was further studied whether such a camouflaging strategy was efficient in reducing the production of key inflammatory molecules, while preserving the release kinetics of the payload of the PLGA microspheres. Results demonstrated that the camouflaging allowed for a 10-fold decrease in the number of PLGA microspheres internalized by macrophages, suggesting that the 3D scaffold operated by cloaking the PLGA microspheres. When the production of key inflammatory cytokines induced by the scaffold was assessed, macrophages' response to the PLGA microspheres-integrated scaffolds resulted in a response similar to that observed in the control (not functionalized scaffold) and the release kinetic of a reporter protein was preserved.

A multidisciplinary approach to the study of protein dynamics and signal transmission

Allostery is a phenomenon of fundamental importance in biology, allowing regulation of function and dynamic adaptability of enzymes and proteins. Despite the allosteric effect was first observed more than a century ago allostery remains a biophysical enigma, defined as the “second secret of life”. The challenge is mainly associated to the rather complex nature of the allosteric mechanisms, which manifests itself as the alteration of the biological function of a protein/enzyme (e.g. ligand/substrate binding at the active site) by binding of “other object” (“allos stereos” in Greek) at a site distant (> 1 nanometer) from the active site, namely the effector site. Thus, at the heart of allostery there is signal propagation from the effector to the active site through a dense protein matrix, with a fundamental challenge being represented by the elucidation of the physico-chemical interactions between amino acid residues allowing communicatio n between the two binding sites, i.e. the “allosteric pathways”. Here, we propose a multidisciplinary approach based on a combination of computational chemistry, involving molecular dynamics simulations of protein motions, (bio)physical analysis of allosteric systems, including multiple sequence alignments of known allosteric systems, and mathematical tools based on graph theory and machine learning that can greatly help understanding the complexity of dynamical interactions involved in the different allosteric systems. The project aims at developing robust and fast tools to identify unknown allosteric pathways. The characterization and predictions of such allosteric spots could elucidate and fully exploit the power of allosteric modulation in enzymes and DNA-protein complexes, with great potential applications in enzyme engineering and drug discovery.