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.

Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening

The dynamic character of proteins strongly influences biomolecular recognition mechanisms. With the development of the main models of ligand recognition (lock-and-key, induced fit, conformational selection theories), the role of protein plasticity has become increasingly relevant. In particular, major structural changes concerning large deviations of protein backbones, and slight movements such as side chain rotations are now carefully considered in drug discovery and development. It is of great interest to identify multiple protein conformations as preliminary step in a screening campaign. Protein flexibility has been widely investigated, in terms of both local and global motions, in two diverse biological systems. On one side, Replica Exchange Molecular Dynamics has been exploited as enhanced sampling method to collect multiple conformations of Lactate Dehydrogenase A (LDHA), an emerging anticancer target. The aim of this project was the development of an Ensemble-based Virtual Screening protocol, in order to find novel potent inhibitors. On the other side, a preliminary study concerning the local flexibility of Opioid Receptors has been carried out through ALiBERO approach, an iterative method based on Elastic Network-Normal Mode Analysis and Monte Carlo sampling. Comparison of the Virtual Screening performances by using single or multiple conformations confirmed that the inclusion of protein flexibility in screening protocols has a positive effect on the probability to early recognize novel or known active compounds.

Cancer and aging: a multidisciplinary medicinal chemistry approach on relevant biological targets such as proteasome, sirtuins and interleukin 6

It is well known that ageing and cancer have common origins due to internal and environmental stress and share some common hallmarks such as genomic instability, epigenetic alteration, aberrant telomeres, inflammation and immune injury. Moreover, ageing is involved in a number of events responsible for carcinogenesis and cancer development at the molecular, cellular, and tissue levels. Ageing could represent a “blockbuster” market because the target patient group includes potentially every person; at the same time, oncology has become the largest therapeutic area in the pharmaceutical industry in terms of the number of projects, clinical trials and research and development (R&D) spending, but cancer remains one of the leading causes of mortality worldwide. The overall aim of the work presented in this thesis was the rational design of new compounds able to modulate activity of relevant targets involved in cancer and aging-related pathologies, namely proteasome and immunoproteasome, sirtuins and interleukin 6. These three targets play different roles in human cells, but the modulation of its activity using small molecules could have beneficial effects on one or more aging-related diseases and cancer. We identified new moderately active and selective non-peptidic compounds able to inhibit the activity of both standard and immunoproteasome, as well as novel and selective scaffolds that would bind and inhibit SIRT6 selectively and can be used to sensitize tumor cells to commonly used anticancer agents such gemcitabine and olaparib. Moreover, our virtual screening approach led us also to the discovery of new putative modulators of SIRT3 with interesting in-vitro and cellular activity. Although the selectivity and potency of the identified chemical scaffolds are susceptible to be further improved, these compounds can be considered as highly promising leads for the development of future therapeutics.