Biophysical processes occurr on scales spanning over twelve order of magnitude in lengths and nearly fifteen in time. Therefore, a number of different methodological approaches must be combined in a coherent way to give a comprehensive representation. This is the so called multi-scale simulations paradigm, developed since the seventies of the last century, consisting in combining different resolution representations from the atomistic to supermolecular meso-scale models, passing through super-atomistic coarse grained models.
This approach allows reaching in silico the macroscopic time scales by a controlled reduction of the degrees of freedom, but at the same time requires a big effort in the coherent parameterization of the empirical force fields at the different resolutions. This is achieved with a combined bottom-up – top-down strategy, using both data obtained with simulations at higher resolution levels and experimental data, which also ensures adherence of models to the experimental observation.
The expertise of the bio-modeling scientists at NANO encompasses ab initio Density Functional Theory calculations, atomistic molecular dynamics, reactive force fields, and coarse grained and mesoscale models development, hybrid simulations (e.g. QM/MM or atomistic/CG), advanced sampling techniques, machine learning and genetic algorithms. These are applied to a number of different systems, involving proteins, nucleic acids, biofunctionalized nanoparticles, even in interaction with non biological surfaces, and to a number of different studies, such as amyloid related diseases, intra-cellular or diagnostic sensors, systems for drug delivery, systems for advanced radiotherapy.
| People | Margherita Bini, Luca Bellucci, Giorgia Brancolini, Ciro Cecconi, Stefano Corni, Rosa Di Felice, Daniele Montepietra, Riccardo Nifosì, Giuseppe Sacco, Valentina Tozzini*, Laura Zanetti Polzi |
| Keywords | Molecular Dynamics simulations, ab initio calculations, Coarse grained models, Force Field developments, clustering methods, advanced sampling, machine learning, bio-quantum computing |
| Publications | |
| M Bini, V Tozzini, G Brancolini Deconstructing Electrostatics of Functionalized Metal Nanoparticlesfrom Molecular Dynamics Simulations J Phys Chem B 2023 | |
| L Marchetti, R Nifosì, PL Martelli, E Da Pozzo, V Cappello, F Banterle, ML Trincavelli, C Martini, M D’Elia Quantum computing algorithms: getting closer to critical problems in computational biology Briefings in Bioinformatics 2022 | |
| S Ozden, S Monti, V Tozzini, NS Dutta, S Gili, N Caggiano, AJ Link, N Pugno, J Higgins, R Priestley, C Arnold Egg protein derived ultralightweight hybrid monolithic aerogel for water purification Mater Today 2022 | |
| M Bini, G Brancolini, V Tozzini Aggregation behavior of nanoparticles: Revisiting the phase diagram of colloids Front Mol Biosci 2022 | |
| G Palermo, AMJJ Bonvin, M Dal Peraro, RE Amaro, V Tozzini Multiscale Modeling from Macromolecules to Cell: Opportunities and Challenges of Biomolecular Simulations Front Mol Biosci 2020 | |
| G Brancolini, V Tozzini Multi-scale modeling of Proteins interactions with functionalized Nanoparticles Curr Opin Coll Int Sci 2019 | |
| F Delfino, Y Porozov, E Stepanov, G Tamazian, V Tozzini Evolutionary Switches Structural Transitions via Coarse-Grained Models J Comp Biol 2019 | |
| F Delfino, Y Porozov, E Stepanov, G Tamazian, V Tozzini Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin Front Mol Biosci 2019 | |
| Giorgia Brancolini, Luca Bellucci, Maria Celeste Maschio, Rosa Di Felice, Stefano Corni, The interaction of peptides and proteins with nanostructures surfaces: a challenge for nanoscience, Curr Opin Coll Int Sci 2019 | |
| P Mereghetti, G Maccari, GLB Spampinato, V Tozzini Optimization of Analytical Potentials for Coarse-Grained Biopolymers Models J Phys Chem B 2016 | |
| F Trovato, V Tozzini Diffusion within the cytoplasm: a mesoscale model of interacting macromolecules Biophys J 2014 |