Proteins are structurally fluctuating macromolecular systems that perform the vast majority of functions of biological cells. Protein dynamics have different timescale manifestations related to various physical events, ranging from fast local flexibility to slower collective motions. The movements of protein atoms ultimately govern the kinetics and thermodynamics of folding, binding, catalysis, among other biochemical activities. Molecular dynamics (MD) has become an essential tool for the computational study of biological polymers. Detailed physical observations can be directly obtained that otherwise are not directly accessible by traditional wet-lab experimental studies. Nonetheless, the computational cost for running MD simulations still hinders its application for many relevant processes with significant activation barriers. Many efforts have been devoted to enhance conformational space sampling by improving searching algorithms, simplifying force fields (for a recent review, see reference ), and accelerating algorithm execution through code optimization and parallelization. Among several MD techniques, dating back to the pioneering work of Warshel and Levitt, Structure-Based Models (SBMs) emerged as a simplified methodology for studying protein folding dynamics. SBMs are based on simplifying assumptions rooted in energy landscape theory for protein folding that capture essential physical aspects of natural proteins and bridge the gap between physical complexity and computational efficiency. These simplifications allow us to obtain a reasonable kinetic and equilibrium characterization of protein systems to be studied within the framework of statistical mechanics.