Understanding the intricate molecular mechanisms underlying biological processes is crucial for tackling multiple biomedical challenges. Molecular dynamics serves as a "computational microscope", offering insights into biomolecular processes with unparalleled spatial and temporal resolution. Yet capturing these processes on biologically relevant scales poses significant computational challenges, especially when additional phenomena, such as nuclear quantum effects (NQEs), must be considered.

However, many processes of interest can be described by a smaller set of collective variables instead of the intractably large number of degrees of freedom arising in atomistic simulation. The idea behind coarse-graining is to integrate out the irrelevant degrees of freedom and model the target system at a lower resolution while preserving the target properties.

This thesis contributes to the development and application of coarse-grained models to increase the computational efficiency of biomolecular simulation and extend the range of molecular processes that can be investigated computationally.

First, we applied a structure-based coarse-grained model combined with all-atom simulations to elucidate the helix formation mechanism following the chromophore isomerization in cyanobacteriochrome Slr1393-g3. Our findings indicate a destabilization of the helical state in the 15-Z configuration compared to the 15-E configuration, which has implications for future experimental investigations. This project also highlights the need for improved coarse-grained models.

Second, the ODEM optimization framework was used to parameterize protein structure-based models using experimental data. The results suggest that incorporating many-body terms to describe nonbonded interactions is crucial to accurately reproduce the protein thermodynamics. This result underscores the importance of using neural networks' potential in approximating coarse-grained force-fields for future research.

Next, a combination of coarse-graining, path integral quantum mechanics, and machine learning was used to develop potentials that incorporate NQEs into all-atom simulation at the cost of classical molecular dynamics. We developed separate models to approximate quantum dynamics and quantum statistics, which demonstrated good performance when applied to test systems. These approaches have the potential to obtain an accurate incorporation of NQEs in biomolecular simulation.

Finally, we discuss how the developed approaches contribute to the bigger goal of effective and accurate methods for computational elucidation of biomolecular processes.