Computational approaches for the study of biotechnologically-relevant macromolecules.

Computer-based techniques have become especially important in molecular biology, since they often represent the only viable way to understand some phenomena at atomic and molecular level. The complexity of biological systems, which usually needs to be analyzed with different levels of accuracy, requires the application of different approaches. Computational methodologies applied to biotechnologies allow a molecular comprehension of biological systems at different levels of depth. Quantum mechanics (QM) ab-initio techniques allow the study of enzymes and organometallic models at sub-atomic levels keeping into account electronic effects on stereochemistry and chemical reactivity. We set to study [FeFe]-hydrogenases, enzymes able to both produce and oxidize H2 at high rate. The study was focused to better elucidate some redox states of the cofactor during catalysis. The principal aim of this work was to take advantage of hydrogenases biomimetic complexes to gain further inside on the catalysis of the enzyme, and pinpoint the structural and stereo-electronic features necessary to improve the efficiency of the synthetic models. H2 is a desirable fuel but the usage of this gas is somehow problematic due to its physical properties, leading to safety concerns and low energy density. A possible way to overcome these problems is to store H2 in safe and valued added chemicals. In this perspective we studied the catalytic mechanism of the first iron-containing synthetic complex able to catayze the chemical storage of H2 and CO2, converting it into HCOOH. QM methodologies were also used in a project in collaboration with the Dept. of Forensic Medicine at the University of Verona, aimed at the use of carbohydrate deficient transferrin (Tf) as marker of alcohol abuse. Tf is the protein deputed for the iron transport in the blood stream. Low glycosylated forms are known to be associated to alcohol abuse. Different spectroscopies were useful tools to discover the binding site of terbium and the best experimental conditions for terbium-Tf binding. To get more information about the active site, we optimized a method that allowed us to determine the molecular structure of the metal environment through QM computational techniques. Docking techniques to study small-ligand protein binding are useful methods to predict the binding mode of a molecule to a receptor, in order to understand its mechanism of action and improve its activity. Here, we focused on different pharmacological targets involved in different pathological mechanisms to understand how the ligand is able to interact with the receptor and exert its pharmacological effect, and how to ameliorate its structure to increase the specificity. Since the evolution of the human species exists a struggle for survival between host and pathogens, with measure and countermeasure to respectively infect and defend against infections. Positively selected sites on protein genes are the result of evolutionary pressure on certain aminoacidic residues that could be fundamental for host and pathogen infections. Protein-protein docking is a useful tool, together with computational stability analysis, to understand how residues variations modify the binding among different proteins in the immune system and how the proteins stability is affected. In conclusion, the choice of the computational methods is what determines the level of the description of the molecular system. The study of biotechnologically relevant system with computational techniques is a powerful tool to gain insight into molecular properties that are otherwise not explorable by experimental techniques.