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Data reporting on structure and dynamics of cellular constituents are growing with increasing pace enabling, as never before, the understanding of fine mechanistic aspects of biological systems and providing the possibility to affect them in controlled ways. Nonetheless, experimental techniques do not yet allow for an arbitrary level of resolution on cellular processes in situ. By consistently integrating a variety of diverse experimental data, molecular modeling is optimally poised to enhance to near-atomistic resolution our understanding of molecular recognition in large assemblies. Within this integrative modeling context, we briefly review in this chapter the recent progresses of molecular simulations at the atomistic and coarse-grained level of resolution to explore protein protein interactions. In particular, we discuss our recent contributions in this field, which aim at providing a robust bridge between novel optimization algorithms and multiscale molecular simulations for a consistent integration of experimental inputs. We expect that, with the ever-growing sampling ability of molecular simulations and the tireless progress of experimental methods, the impact of such dynamic-based approach could only be more effective with time, contributing to provide detailed description of cellular organization.

Characteristic timescales associated with the function of biomolecules, like proteins, range from femtoseconds up to minutes, whereas their corresponding spatial extent ranges from few ̊A to μm when associating in large macromolecular complexes. Moreover, biomolecules are functional in a large variety of different physico-chemical conditions strongly dependent on pH, ionic strength, crowding agents, etc. This huge complexity is hard to be studied with an arbitrary level of resolution embracing all these spatial and temporal scales. Molecular simulation is a well established approach to gain mechanistic insights into the function of biomolecular systems, producing atomistically detailed models of in vitro and/or in vivo conditions. I present in this thesis two projects that aim at improving on the current limitations of multiscale molecular simulations, namely (i) the sampling of large systems, and (ii) the detailed representation and description of realistic physiological conditions. Addressing the first issue, I propose a new coarse-grained model for proteins to be used in molecular dynamics simulations. This coarse-grained model is based on a more accurate description of protein electrostatics, which accounts for dipolar contributions. The parameterization of this force field is based on force-matching methods and on the use of a particle swarm optimization heuristic algorithm. The obtained results are encouraging being structural and electrostatic properties accurately reproduced with the coarse-grained model for a variety of protein folds. Moreover, the parameterization procedure can be straightforwardly applied to any protein, and can be extended to a larger dataset to generate a fully transferable coarse-grained force field, to be applied to any protein and any large macromolecular assemblies for which long all-atom simulations are still a challenge. While the development and use of coarse-grained models are important to tackle the limited sampling of large systems, it is still important to use all-atom molecular dynamics simulation to investigate with high accuracy in physiological conditions protein dynamics. For this reason, I present here the results of state-of-art molecular dynamics simulations applied to study the influence of crowding agents on the internal dynamics of the protein ubiquitin. Their analysis allows to describe how ubiquitin dynamics is slaved by crowding agents. This work demonstrates that the description of protein dynamics should take into account its intrinsic multiscale nature. The development and applications of coarse-grained models permit to simulate proteins at low computational cost, the use of atomistic simulations allows to accurately describe proteins in absence and presence of crowding agents, and both of them permit to highlight the essence of protein dynamics.