Abstract :
The last decade has witnessed a renewed interest in the coarse-grained (CG)models for biopolymers, also stimulated by the needs of modern molecular biology, dealingwith nano- to micro-sized bio-molecular systems and larger than microsecond timescale. Thiscombination of size and timescale is, in fact, hard to access by atomic-based simulations.Coarse graining the system is a route to be followed to overcome these limits, but the ways ofpractically implementing it are many and different, making the landscape of CG models veryvast and complex.In this paper, the CG models are reviewed and their features, applications and performancescompared. This analysis, restricted to proteins, focuses on the minimalist models, namely thosereducing at minimum the number of degrees of freedom without losing the possibility ofexplicitly describing the secondary structures. This class includes models using a single or a fewinteracting centers (beads) for each amino acid.From this analysis several issues emerge. The difficulty in building these models resides in theneed for combining transferability/predictive power with the capability of accuratelyreproducing the structures. It is shown that these aspects could be optimized by accuratelychoosing the force field (FF) terms and functional forms, and combining differentparameterization procedures. In addition, in spite of the variety of the minimalist models,regularities can be found in the parameters values and in FF terms. These are outlined andschematically presented with the aid of a generic phase diagram of the polypeptide in theparameter space and, hopefully, could serve as guidelines for the development of minimalistmodels incorporating the maximum possible level of predictive power and structural accuracy
