Despite the enormous advances in structural studies of biological systems we are frequently left without a clear structure function correlation and cannot fully describe how different systems actually work.
This introduces a major challenge for computer modeling approaches that are aimed at a realistic simulation of biological functions.
The unresolved questions range from the elucidation of the basis for enzyme action to the understanding of the directional motion of complex molecular motors.
Here we review the progress in simulating biological functions, starting with the early stages of the field and the development of QM/MM approaches for simulations of enzymatic reactions.
We provide overwhelming support to the idea that enzyme catalysis is due to electrostatic preorganization and then move to the renormalization approaches aimed at modeling long time processes, demonstrating that dynamical effects cannot change the rate of the chemical steps in enzymes.
Next we describe the use our electrostatic augmented coarse grained (CG) model and the renormalization method to simulate the action of different challenging complex systems.
It is shown that our CG model produces, for the first time, realistic landscapes for vectorrial process such as the actions of F1 ATPase, F0 ATPase and myosinV.
It is also shown that such machines are working by exploiting free energy gradients and cannot just use Brownian motions as the vectorial driving force.
Significantly, at present, to the best of our knowledge, theses studies are the only studies that reproduced consistently (rather than assumed) a structure based vectorial action of molecular motors.
We also describe a breakthrough in CG modeling of voltage activated ion channels.
We also outline a recent simulation of the tag of war between staled elongated peptide in the ribosome and the translocon as an illustration of the power of our CG approach.
The emerging finding from all of our simulations is that electrostatic effects are the key to generating functional free energy landscapes. Finally we present some thought on the future of the field, taking drug resistance as an example.