Application of multiscale models on biological systems

Multiscale biomolecular simulations allow to understand sub cellular mechanisms of biological relevance. It provides a powerful tool to link the laws of physics with the complex behaviour of biological systems. Progress of computational resources, combined with the development of powerful algorithms for parallel computing, will increasingly lead to molecular views of large systems. These advances may tremendously affect biological sciences. In this thesis, I have applied multiscale computational biology approaches to different biological systems using state-of-the-art structural bioinformatics and computational biophysics tools. In particular, I performed an extensive computational characterization of the dibenzothiophene (DBT) degradation pathway of Burkholderia fungorum DBT1, a great promising bacterial strain in the field bio-remediation of Polycyclic aromatic hydrocarbons (PAHs). I analyzed the enzymes involved in this pathway at the sequence, structural and functional level. All the sequence/structural data analyzed and produced for this thesis, together with biochemical/enzymatic data retrieved from extensive literature search were included in the PWPAHs database that I have created. Furthermore, I worked in the field of computational molecular medicine. Indeed, in Chapter 4 I describe the work done in collaboration with the Internal Medicine lab of the University of Verona, in which, starting from unpublished experimental data I have computationally characterized a novel Serin Protease Inhibitor (SPI), which acts on the Matriptase 2 (MT2), an important human iron-homeostasis regulator. Then I have performed a detailed structural and functional characterization on two members of the PLP-dependent enzyme family (Chapter 5). In particular, I have used classical molecular dynamic simulations to perform a deep ligand optimization of the PLP-cystathionine ligand in the catalytic pocket of C-S lyase from Corynebacterium diphtheriae. I have been also involved in the creation of a computational protocol to analyze the stability of the molecular tags used in the targeting of DOPA decarboxylase (DDC) for Parkinson's disease treatment, using state of the art coarse-grained computational tools in collaboration with the Biological Chemistry laboratory of the University of Verona.