Research

We integrate a wide range of techniques for developing nanotechnology solutions of problems of medical interest. In particular, we aim to develop new diagnostic nanodevices by merging a wide range of techniques.

 

 

Peptide-based nanodevices design (S.Fortuna, M.Soler, A.Russo, C.Dongmo)

theory/esperiment interplay

The development of new nanodevices strongly relies on a tight interplay among the available computational and experimental techniques (Fig.1). Here we aim to develop new pepide based devices for molecular recognition. Computational protocols are extremely useful to select and test possible binders to a marker protein and a number of algorithms able to perform part of the task are available. The idea is to test, integrate, and further improve existing protocols to optimise the selection task.  However,  for the design of new devices, once promising ligands are selected it is mandatory for these to be tested in the lab by verify and measuring their properties, such as solubility, affinity and selectivity to their target possibly with a fast and robust experimental protocol. The experimental result will then act as input and feedback for further imporving the computational protocols.

We employ a range set of computational techniques to address open practical issues to support the design of new molecular devices. We run routine simulations on our 64-node AMD cluster, while large scale intensive computations are runned on Fermi.

  • Peptide-based protein recognition

Typical receptors for proteins are antibodies optimized in vivo. However, while monoclonal  antibodies are highly successful in therapeutics and diagnostics, they are highly costly. An alternative would be exploiting advances in computing power together with the development of new and powerful algorithms for protein folding, docking, and structure prediction to  reproduce the same output, namely an optimized antibody,  in silico. While progresses are being made in this direction [1,2], the design of a full antibody from scratch pose a set of challenges due to its structural complexity caused by the large number of monomers forming  its primary structure, and it is still out of reach.

The Maltose Binding ProteinA promising alternative is the in silico design of short peptides, easily synthesizable  in vitro by automated  synthesis. To this aim (1) we are developing a computational protocol based on replica  exchange Monte Carlo [3] for the generation  of an optimized set of peptides able to bind with high affinity to a protein; (2) we chose the maltose binding protein (MBP, Fig.2) as a test case and measured the binding affinity of a set of computationally designed peptides to the MBP showing they actually bind to the  MBP; (3) we look at the entropic contributions to the peptide-protein complexes to maximize their binding affinity.

  • Multiscale modelling for molecular self-organisation

The construction of real nanodevices often exploits the ability of molecules to organise themselves thanks to the intermolecular forces activing between them. As molecular self-organisation is intrinsically hierarchical, spanning multiple time-scales, and existing codes are usually bound to a unique time/lenght-scale, the interplay among the available methods is essential to predict the properties of a given self-organised system. At small time/length-scales quantum chemical studies, usually in the form of DFT calculations, have been successfully used to identify the lowest energy building blocks of larger structures [4]. At the other extreme, where very large time/length-scales prevail with respect to the atomic constituents, as in fluid dynamics or the simulation of mechanical properties, continuous models can be employed. Between the two scales there are the two methods used for the study of self-organisation of ensembles of molecules: molecular dynamics and Monte Carlo [5].

References:
[1] L. Simonelli, et al., PloS one 2013, 8 (2), e55561.
[2] D. Kuroda, et al., Protein Eng. Des. Sel. 2012, 25 (10), 507-521.
[3] R. P. H. Enriquez, et al., J. Chem. Theory Comput. 2012, 8 (3), 1121-1128.
[4] S. Fortuna, P. Gargiani, et al. , J. Phys. Chem. C, 2012, 116 (10), pp 6251–6258.
[5] S. Fortuna, D. L. Cheung, and A. Troisi,  J. Phys. Chem. B, 2010, 114 (5), pp 1849–1858.

CTCs counting and analysis (G. Scoles, F. Del Ben, M. Turetta)

Counting Circulating Tumor Cells (CTCs) have been recently widely accepted as a diagnostic tool with prognostic value and for therapy monitoring[1-5]. Most of techniques available today use antibody labeling or physical features to detect CTCs in blood, resulting in expensive (antibody labeling) and not yet clinically validated methods (physical features). We focused on metabolism differences between cancer and normal cells, being a well-established biological evidence[6] and not requiring antibodies to work. As a proof of concept, we established that cancer cells from several cell lines were detectable in a mixed suspension of mononuclear blood cells using a fluorescent-conjugated glucose (2-NBDG). We are now developing microfluidic circuits to sort out CTCs via magnetic nanoparticles-conjugated glucose. In addition, we are evaluating other metabolites, in order to acquire better specificity in CTCs detection.

References:
[1] Cristofanilli, M., et al., N Engl J Med, 2004. 351(8): p. 781-91.
[2] Riethdorf, S., et al., Clin Cancer Res, 2007. 13(3): p. 920-8.
[3] Riethdorf, S. and K. Pantel, Ann N Y Acad Sci, 2010. 1210: p. 66-77.
[4] Cohen, S.J., et al., J Clin Oncol, 2008. 26(19): p. 3213-21.
[5] Krebs, M.G., et al., J Thorac Oncol, 2012. 7(2): p. 306-15.
[6] Kroemer, G. and J. Pouyssegur, Cancer Cell, 2008. 13(6): p. 472-82.

nanotechnology and physical basis of biology and pathology

 

 

Cell biology and Pathology

 

 

Atomic Force Microscopy and Nanolithography (M.Castronovo, A.Palma, M.Vidonis, A.Stopar, A.Adedeji)

 

 

DNA Nanotechnology (M.Castronovo, A.Stopar, L.Coral, A.Amodio, D.Chang, L.Crevatin)

Monalisa Group

Department of Medical and Biological Sciences - University of Udine - Piazzale Kolbe, 4 - 33100 Udine - Italy

http://monalisa.uniud.it

WEBMASTER: Sara Fortuna (sara.fortuna at uniud.it)

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