A very short guide for installing python packages (pymol in this example) as a non-privileged
Efflux-mediated multi-drug resistance
This project concerns the bacterial resistance to antibiotics, with a focus on Gram-negative pathogens (see the last WHO Global report on antimicrobial resistance at http://www.who.int/drugresistance/documents/surveillancereport/en). The main goal is to understand the molecular basis behind the polyspecifity of the so-called multi-drug resistant (MDR) efflux pumps, which are key to develop resistance to many if not all antibiotics, as well to exploit compounds that can be used as inhibitors in conjunction with drugs. The project involves a broad collaboration network of international partners: Prof. L. Piddock (University of Birmingham, UK), Prof. V. Bavro (University of Essex, Colchester, UK), Profs. H. Zgurskaya and V.V. Rybenkov (University of Oklahoma, Norman, OK, USA), Prof. J. Walker (Saint Louis University; Henry and Amelia Nasrallah Center for Neuroscience, Saint Louis, MO, USA), Prof. S. Gnanakaran (Los Alamos National Laboratory, NM, U.S.A.), Dr. E. Reading and Prof. P. Argyris (King’s College, London, UK), Prof. E. Bibi (Weizmann Institute of Science, Rehovot, Israel), Proff. J.M. Pages and J-M. Bolla (Université de la Méditerranée, Marseille,France), Prof. M.K. Pos (Goethe University, Frankfurt, Germany), Dr. R. Hartkoorn (Institut Pasteur de Lille, France), Dr. M. Flipo, (University of Lille, Inserm, Institut Pasteur de Lille, France), and the pharmaceutical companies “Basilea Pharmaceutica” (Basilea, Switzerland), “Microbiotix” (Worcester, MA, U.S.A.) and “Aziende Chimiche Riunite Angelini Francesco (ACRAF)” (Rome, Italy). Recently we started a collaboration with Dr. I. Mus-Veteau (UMR-CNRS, Valbonne, France) and Dr. S. Azoulay (Université Cote d’Azur, Nice,France), on efflux pumps related to cancer.
Self-assembly hydrogel peptides
Short peptide self-assembling hydrogels are a promising class of soft nanomaterials for drug delivery, regenerative medicine, and nanotechnology. In this project we exploit chirality as a conformational and cheap switch to tune morphological properties of heterochiral peptides. Thanks also to the use of computational modelling, we are deciphering the effects of chirality on peptide self-assembly from the molecular level to materials. This project represents a very fruitful collaboration with Prof. S. Marchesan (Centre of Excellence for Nanostructured Materials (CENMAT) INSTM, Trieste, Italy) and Prof. J. R. Nitschke (University of Cambridge, UK).
Characterization of physico-chemical properties of small molecules
In this project, we set up an on-line database of all-atom force-field parameters and properties of antimicrobial compounds. To our knowledge, this is the first extensive database including dynamical properties of compounds, and it is part of a wider project aiming to build-up a database containing structural, physico-chemical and dynamical properties of medicinal compounds using different force-field parameters with increasing level of complexity and reliability.
Methods to improve the accuracy of in silico molecular recognition
In this project, we are developing computational protocols to improve the description of molecular recognition events involving small ligands and proteins. Specifically, we aim to predict bound-like conformations of proteins without exploiting any a priori knowledge about experimental structures of their complexes with ligands. Being able to generate these structures would be critical to successful predict the correct binding of drugs via molecular docking, affecting in turn the performance of virtual screening. This project involves Prof. A. M. J. J. Bonvin (University of Utrecht, The Netherlands), Dr. F. Pietrucci (Université Pierre et Marie Curie (UPMC) - Paris 6, France), and Prof. V. Carnevale (Temple University, PA, U.S.A.)
Computational study of radiopharmaceuticals for imaging and therapy
Current efforts in the development of new pharmacological treatments are aimed at creating personalized therapies for the specific characteristics of the disease and the patient. The goal of this research line is to identify and develop new highly selective drugs for tumor theranostics (i.e. the coupling of diagnosis with targeted therapy). To this aim, computational approaches coupling ad-hoc QM-based parameterization and extensive MD simulations of the systems of interest are being used. Current systems under investigation include somatostatin receptors, gastrin releasing peptide receptors, and different enzymes of oncological interest.
Studying the impact of mutations on the network of synaptic interactions
Combining genetics and bioinformatics information, we aim at characterizing the impact of single-point mutations on the protein network at synaptic level. The project is based on the collaboration with groups at the Forschungszentrum Jülich (Germany), the Icahn School of Medicine at Mount Sinai (USA), the Universities of Edinburgh, Bologna, and Verona. We will perform a large scale docking of protein pairs in their wild-type and mutated forms to gain insights into the effect of the mutations on the protein-protein interactions. Weakening or stifness of the interactions will be translated in parameters useful to be included in network protocols, which will possibly allow the identification of hubs in the synaptic complexes. Additionally, we will characterize possible hotspots of the proteins, and selected protein complexes will be studied by Molecular Dynamics Simulations in solution and at physiological temperature.