In the Computational Structural Biology lab at the Department of Biosciences of the University of Milan, we combine computer simulations and experiments to study molecular processes. We apply and develop integrative structural and dynamical biology methods to provide multi-resolution models of biomolecules in motion. The group is integrated in the Structural Biology Lab.

Protein dynamics, encoded in the combination of sequence and environments, is responsible for protein assembly, function and disfunction

Methods development in Computational Structural Biology

Molecular dynamics simulations are a powerful tool to study the structure and dynamics of biological molecules, still they are not as powerful as one would like. We try to overcome the limitations of MD by developing methods to enhance the sampling and method to increase the quality of the results. We try to integrate experimental information from multiple source of equilibrium and non-equilibrium experiment in MD simulation at multiple resolution, from full-atomistic to simple coarse grain models in such a way to study systems of any size without loss of accuracy. Our efforts are made available through the continuous development of PLUMED.

Protein Folding, Misfolding, Aggregation and Diseases (Transition Grant – Horizon 2020)

How protein fold and how they fail are key questions to shed light on protein functional and dysfunctional behaviours. We investigate the processes at play in protein folding as well as in aggregation phenomena. We are interested in the determinants of aberrant aggregation of folded proteins to understand how such process can be modulated and regulated. We are currently studying systemic amyloidosis from beta-2-microglobulin and light chain antibodies, we are also involved in studies of protein aggregation of disordered protein in Alzheimer’s, Parkinson’s and type-2 diabetes. In particular we combine multiple simulation and experimental approaches to try to study all the relevant phases of protein aggregation: accurate equilibrium simulations combined with structural biology techniques (NMR, SAXS, X-Ray) to understand the properties of the native state, coarse-grain simulation and kinetics aggregation experiments to understand the aggregation process and cryo-EM to study the structure of the resulting fibril structures.

Protein Dynamics and Molecular Recognition

Proteins move, fluctuate around their native ground state and exchange among low populated excited states. Such substates can be exploited to facilitate the binding between proteins, proteins and nucleic acids or other molecules. This behaviour is particularly evident in the case of disordered proteins that by lacking a specific tertiary structure exchange continuously among a large number of weakly populated states. We use molecular modelling to characterise protein dynamics and correlate it with functional and thermodynamic features.  We are currently studying  the role of dynamics (among the other)

Structural and functional characterization of hERG potassium channels’ enhancers (Telethon GGP19134)

The goal of this research is to test the hypothesis that small molecules that enhance the rapid component of the delayed rectifier current IKr in cardiac cells are able to rescue the phenotype in three severe variants of Long QT Syndrome (LQTS) caused by loss of function mutations in the HERG gene that encodes for the KV11.1 potassium channels that conducts IKr current (LQT2). We will apply state of the art technology to understand the relationship between structure and function and identify the binding pose of the compounds exploiting the recently resolved cryo-EM structure of the HERG channel in the open state. We will model the structure of the closed state of the channel and provide insight to our collaborators on how to improve the design of the compounds. Thanks to Telethon foundation and in collaboration with Silvia Priori at Maugeri and Giovanni Lentini at the University of Bari we will be able to perform a through assessment of a selection of the most powerful molecules previously reported in the literature and of novel molecules developed within this project.

Joint artificial intelligence and protein structure modelling to guide large-scale screenings for anti-SARS-Cov2 neutralizing antibodies (CoroNAId)

We are part of a network lead by Stefano Casola at IFOM and in collaboration with Raffale Badolato (UNIBS/Spedali Civili di Brescia), Giulia Marchetti (UNIMI/ASST santi Carlo e Paolo) and Maria Rodriguez Martinez (IBM) were will provide a structural and dynamical understanding for the activity of large pools of antibodies heavy chain obtained from COVID-19 patients.

Molecular Design of enhanced MHC-I epitopes