Computational approaches, particularly molecular dynamics (MD) simulations, offer powerful tools for studying the membrane binding of peripheral membrane proteins (PMPs) and their interactions with lipids. However, the simulation of membranes at an atomic level is constrained by timescale limitations, further compounded by the slow diffusion of lipids in the membrane. The highly mobile membrane mimetic (HMMM) model was developed as one of the alternative membrane representations to overcome some of these limitations, particularly for investigating membrane binding of PMPs. By using short-tailed lipids, the HMMM model significantly enhances the lateral diffusion of lipids, thereby substantially accelerating membrane-associated phenomena. In this chapter, we outline best practices for setting up protein–HMMM systems and provide examples of relevant analyses. Ideally, one begins by thoroughly researching and understanding the protein of interest, including its cellular localization, the type of membrane it interacts with, and any biochemical evidence for structural changes upon membrane binding. Additional considerations include the quality of the available structures, as well as relevant posttranslational modifications, and involvement of ligands or cofactors. After constructing and equilibrating a representative full-length (FL) membrane with the desired lipid composition, it can be converted into an HMMM representation by using an in-house script provided in the SI materials or resources such as CHARMM–GUI. The PMP is then positioned on top of the HMMM membrane at a long enough distance from it and in different orientations to avoid biasing the membrane-binding process across multiple replicas with shuffled lipid arrangements. Following a careful equilibration protocol, one can capture spontaneous membrane-binding events for PMPs during production MD simulations. To quantify the membrane-binding events, one would first identify stable membrane-bound segments of the HMMM trajectories, which can be done by monitoring protein–membrane contacts. Analyzing these membrane-bound frames can then provide insights into membrane interaction hotspots, potential membrane-anchoring residues, the protein’s selectivity for particular lipid types, and distinct membrane-bound poses that may be relevant to function or activation. Finally, it is crucial to verify the results from HMMM simulations by analyzing the findings after converting the protein-membrane system back to FL membranes, which can be done using an in-house script provided in the SI materials, and conducting additional simulations.