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.

Advanced applications of X-ray Absorption Spectroscopy to the study of protein metal sites.

We present a study of the metal sites of different proteins through X-ray Absorption Fine Structure (XAFS) spectroscopy. First of all, the capabilities of XAFS analysis have been improved by ab initio simulation of the near-edge region of the spectra, and an original analysis method has been proposed. The method subsequently served ad a tool to treat diverse biophysical problems, like the inhibition of proton-translocating proteins by metal ions and the matrix effect exerted on photosynthetic proteins (the bacterial Reaction Center, RC) by strongly dehydrate sugar matrices. A time-resolved study of Fe site of RC with μs resolution has been as well attempted. Finally, a further step aimed to improve the reliability of XAFS analysis has been performed by calculating the dynamical parameters of the metal binding cluster by means of DFT methods, and the theoretical result obtained for MbCO has been successfully compared with experimental data.

Comparison of affinity chromatography supports for separation of protein

Chromatography is the most widely used technique for high-resolution separation and analysis of proteins. This technique is very useful for the purification of delicate compounds, e.g. pharmaceuticals, because it is usually performed at milder conditions than separation processes typically used by chemical industry. This thesis focuses on affinity chromatography. Chromatographic processes are traditionally performed using columns packed with porous resin. However, these supports have several limitations, including the dependence on intra-particle diffusion, a slow mass transfer mechanism, for the transport of solute molecules to the binding sites within the pores and high pressure drop through the packed bed. These limitations can be overcome by using chromatographic supports like membranes or monoliths. Dye-ligands are considered important alternatives to natural ligands. Several reactive dyes, particularly Cibacron Blue F3GA, are used as affinity ligand for protein purification. Cibacron Blue F3GA is a triazine dye that interacts specifically and reversibly with albumin. The aim of this study is to prepare dye-affinity membranes and monoliths for efficient removal of albumin and to compare the three different affinity supports: membranes and monoliths and a commercial column HiTrapTM Blue HP, produced by GE Healthcare. A comparison among the three supports was performed in terms of binding capacity at saturation (DBC100%) and dynamic binding capacity at 10% breakthrough (DBC10%) using solutions of pure BSA. The results obtained show that the CB-RC membranes and CB-Epoxy monoliths can be compared to commercial support, column HiTrapTM Blue HP, for the separation of albumin. These results encourage a further characterization of the new supports examined.

NMR Based Foodomics to Investigate the Digestibility of Protein-Rich Food Products

Nowadays, in developed countries, the excessive food intake, in conjunction with a decreased physical activity, has led to an increase in lifestyle-related diseases, such as obesity, cardiovascular diseases, type -2 diabetes, a range of cancer types and arthritis. The socio-economic importance of such lifestyle-related diseases has encouraged countries to increase their efforts in research, and many projects have been initiated recently in research that focuses on the relationship between food and health. Thanks to these efforts and to the growing availability of technologies, the food companies are beginning to develop healthier food. The necessity of rapid and affordable methods, helping the food industries in the ingredient selection has stimulated the development of in vitro systems that simulate the physiological functions to which the food components are submitted when administrated in vivo. One of the most promising tool now available appears the in vitro digestion, which aims at predicting, in a comparative way among analogue food products, the bioaccessibility of the nutrients of interest.. The adoption of the foodomics approach has been chosen in this work to evaluate the modifications occurring during the in vitro digestion of selected protein-rich food products. The measure of the proteins breakdown was performed via NMR spectroscopy, the only techniques capable of observing, directly in the simulated gastric and duodenal fluids, the soluble oligo- and polypeptides released during the in vitro digestion process. The overall approach pioneered along this PhD work, has been discussed and promoted in a large scientific community, with specialists networked under the INFOGEST COST Action, which recently released a harmonized protocol for the in vitro digestion. NMR spectroscopy, when used in tandem with the in vitro digestion, generates a new concept, which provides an additional attribute to describe the food quality: the comparative digestibility, which measures the improvement of the nutrients bioaccessibility.

Impact of Protein Induced by Vitamin K Absence (PIVKA-II) in diagnosis and monitoring of patients with hepatocellular carcinoma

Background and Aims: Hepatocellular carcinoma (HCC) represents the second leading cause of cancer deaths worldwide. Protein induced by vitamin K absence (PIVKA-II) has been proposed as potential screening biomarker for HCC.This study has been designed to evaluate the role of PIVKA-II as diagnostic HCC marker, through the comparison between PIVKA-II and alpha-fetoprotein (AFP) serum levels on HCC patients and the two control groupsof patients with liver disease and without HCC. Methods: In an Italian prospective cohort, PIVKA-II levels were assessed on serum samplesby an automated chemiluminescent immunoassay (Abbott ARCHITECT). The study population included 65 patients with HCC (both “de novo” and recurrent), 111 with liver cirrhosis (LC) and 111 with chronic hepatitis C (CHC). Results: PIVKA-II levels were increased in patients with HCC (median 63.75, range: 12-2675 mAU/mL) compared to LC (median value: 30.95, range: 11.70–1251mAU / mL, Mann Whitney test p < 0.0001) and CHC (median value: 24.89, range: 12.98-67.68mAU / mL, p < 0.0001).The area under curve (AUC) for PIVKA-II was 0.817 (95% Confidence Interval(CI), 0.752-0.881). At the optimal threshold of 37 mAU / mL, identified by the Youden Index, the sensitivity and specificity were 79% and 76%, respectively. PIVKA-II was a better biomarker than AFP for the diagnosis of HCC, since the AUC for AFP was 0.670 (95% CI 0.585-0.754, p<0.0001) and at the best cutoff of 16.4 ng / mL AFP yielded 98% specificity but only 34% sensitivity. Conclusions:These initial data suggest the potential utility of this tool in the diagnosis of HCC.PIVKA-II alone or in combination may help to an early diagnosis of HCC and a significant optimization of patient management.

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.