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.

Velocity structure and seismic anisotropy in the crust and upper mantle from Receiver Function analysis: three case studies in Italy

The research for this PhD project consisted in the application of the RFs analysis technique to different data-sets of teleseismic events recorded at temporary and permanent stations located in three distinct study regions: Colli Albani area, Northern Apennines and Southern Apennines. We found some velocity models to interpret the structures in these regions, which possess very different geologic and tectonics characteristics and therefore offer interesting case study to face. In the Colli Albani some of the features evidenced in the RFs are shared by all the analyzed stations: the Moho is almost flat and is located at about 23 km depth, and the presence of a relatively shallow limestone layer is a stable feature; contrariwise there are features which vary from station to station, indicating local complexities. Three seismic stations, close to the central part of the former volcanic edifice, display relevant anisotropic signatures­­­ with symmetry axes consistent with the emplacement of the magmatic chamber. Two further anisotropic layers are present at greater depth, in the lower crust and the upper mantle, respectively, with symmetry axes directions related to the evolution of the volcano complex. In Northern Apennines we defined the isotropic structure of the area, finding the depth of the Tyrrhenian (almost 25 km and flat) and Adriatic (40 km and dipping underneath the Apennines crests) Mohos. We determined a zone in which the two Mohos overlap, and identified an anisotropic body in between, involved in the subduction and going down with the Adiratic Moho. We interpreted the downgoing anisotropic layer as generated by post-subduction delamination of the top-slab layer, probably made of metamorphosed crustal rocks caught in the subduction channel and buoyantly rising toward the surface. In the Southern Apennines, we found the Moho depth for 16 seismic stations, and highlighted the presence of an anisotropic layer underneath each station, at about 15-20 km below the whole study area. The moho displays a dome-like geometry, as it is shallow (29 km) in the central part of the study area, whereas it deepens peripherally (down to 45 km); the symmetry axes of anisotropic layer, interpreted as a layer separating the upper and the lower crust, show a moho-related pattern, indicated by the foliation of the layer which is parallel to the Moho trend. Moreover, due to the exceptional seismic event occurred on April 6th next to L’Aquila town, we determined the Vs model for two station located next to the epicenter. An extremely high velocity body is found underneath AQU station at 4-10 km depth, reaching Vs of about 4 km/s, while this body is lacking underneath FAGN station. We compared the presence of this body with other recent works and found an anti-correlation between the high Vs body, the max slip patches and earthquakes distribution. The nature of this body is speculative since such high velocities are consistent with deep crust or upper mantle, but can be interpreted as a as high strength barrier of which the high Vs is a typical connotation.

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.

Quantum Integrability in Non-Linear Sigma Models related to Gauge/String Correspondences

The Thermodynamic Bethe Ansatz analysis is carried out for the extended-CP^N class of integrable 2-dimensional Non-Linear Sigma Models related to the low energy limit of the AdS_4xCP^3 type IIA superstring theory. The principal aim of this program is to obtain further non-perturbative consistency check to the S-matrix proposed to describe the scattering processes between the fundamental excitations of the theory by analyzing the structure of the Renormalization Group flow. As a noteworthy byproduct we eventually obtain a novel class of TBA models which fits in the known classification but with several important differences. The TBA framework allows the evaluation of some exact quantities related to the conformal UV limit of the model: effective central charge, conformal dimension of the perturbing operator and field content of the underlying CFT. The knowledge of this physical quantities has led to the possibility of conjecturing a perturbed CFT realization of the integrable models in terms of coset Kac-Moody CFT. The set of numerical tools and programs developed ad hoc to solve the problem at hand is also discussed in some detail with references to the code.

Conservation genetics of European wildcat (Felis silvestris silvestris): a wide and integrating analysis protocol for admixture inferences and population structure

Introgression of domestic cat genes into European wildcat (Felis silvestris silvestris) populations and reduction of wildcats’ range in Europe, leaded by habitat loss and fragmentation, are considered two of the main conservation problems for this endangered feline. This thesis addressed the questions related with the artificial hybridization and populations’ fragmentation, using a conservation genetics perspective. We combined the use of highly polymorphic loci, Bayesian statistical inferences and landscape analyses tools to investigate the origin of the geographic-genetic substructure of European wildcats (Felis silvestris silvestris) in Italy and Europe. The genetic variability of microsatellites evidenced that European wildcat populations currently distributed in Italy differentiated in, and expanded from two distinct glacial refuges during the Last Glacial Maximum. The genetic and geographic substructure detected between the eastern and western sides of the Apennine ridge, resulted by adaptation to specific ecological conditions of the Mediterranean habitats. European wildcat populations in Europe are strongly structured into 5 geographic-genetic macro clusters corresponding to: the Italian peninsular & Sicily; Balkans & north-eastern Italy; Germany eastern; central Europe; and Iberian Peninsula. Central European population might have differentiated in the extra-Mediterranean Würm ice age refuge areas (Northern Alps, Carpathians, and the Bulgarian mountain systems), while the divergence among and within the southern European populations might have resulted by the Pleistocene bio geographical framework of Europe, with three southern refugia localized in the Balkans, Italian Peninsula and Iberia Peninsula. We further combined the use of most informative autosomal SNPs with uniparental markers (mtDNA and Y-linked) for accurately detecting parental genotypes and levels of introgressive hybridization between European wild and domestic cats. A total of 11 hybrids were identified. The presence of domestic mitochondrial haplotypes shared with some wild individuals led us to hypnotize the possibility that ancient introgressive events might have occurred and that further investigation should be recommended.

The Structure of Financial Supervision: Consolidation or Fragmentation for Financial Regulators?

This research was designed to answer the question of which direction the restructuring of financial regulators should take – consolidation or fragmentation. This research began by examining the need for financial regulation and its related costs. It then continued to describe what types of regulatory structures exist in the world; surveying the regulatory structures in 15 jurisdictions, comparing them and discussing their strengths and weaknesses. This research analyzed the possible regulatory structures using three methodological tools: Game-Theory, Institutional-Design, and Network-Effects. The incentives for regulatory action were examined in Chapter Four using game theory concepts. This chapter predicted how two regulators with overlapping supervisory mandates will behave in two different states of the world (where they can stand to benefit from regulating and where they stand to lose). The insights derived from the games described in this chapter were then used to analyze the different supervisory models that exist in the world. The problem of information-flow was discussed in Chapter Five using tools from institutional design. The idea is based on the need for the right kind of information to reach the hands of the decision maker in the shortest time possible in order to predict, mitigate or stop a financial crisis from occurring. Network effects and congestion in the context of financial regulation were discussed in Chapter Six which applied the literature referring to network effects in general in an attempt to conclude whether consolidating financial regulatory standards on a global level might also yield other positive network effects. Returning to the main research question, this research concluded that in general the fragmented model should be preferable to the consolidated model in most cases as it allows for greater diversity and information-flow. However, in cases in which close cooperation between two authorities is essential, the consolidated model should be used.