The binding affinity between the Spike (S) glycoprotein and its host cell receptors, angiotensin-converting enzyme 2 (ACE2) and Furin plays a critical role in determining SARS-CoV-2 replication rates. Variants of the S protein, including Alpha, Beta, Gamma, Delta, Omicron BA.1, BA.2, BA.4, and BA.5, have been associated with shifting levels of COVID-19 susceptibility and severity. However, the mechanisms underlying these virus-host protein interactions are not fully understood. The current research employs molecular modeling, protein-protein interaction, and molecular dynamics (MD) simulations of Wild-Type (WT) and variants of virus-host complexes ACE2-Spike-Furin to investigate interactions and stability. The objective is to gain a comprehensive understanding of protein complex dynamics and their relevance to COVID-19. Protein 3D structures were obtained from the Protein Data Bank, while MODELLER was employed to model S protein variants. Glycans were incorporated using the Glycan Reader & Modeller tool. Complexes were chosen based on HADDOCK scores, with lower scores signifying greater reliability. Interaction free energies were calculated using the PRODIGY server. MD simulations were conducted using GROMACS version 2019.3 with the CHARMM36 force field. The complexes were solvated, neutralized, minimized, and equilibrated under an NVT and NPT ensemble. The production phase was performed at 300K for 100ns. Complex selection was based on both the lowest HADDOCK scores and the free energies of each interaction. ACE2-Spike interactions displayed binding affinities ranging from -11.4 to -15.1 kcal/mol for WT Spike and its variants. Spike-Furin interactions exhibited affinities ranging from -10.5 to -12.6 kcal/mol. 28 glycans were incorporated into each complex. Throughout the MD simulations, all complexes demonstrated stability, as corroborated by RMSD, RMSF, Gyration, and SASA analyses, though with specific variations related to each variant. This project uniquely explores the interactions among three proteins and glycans, yielding high-fidelity complex models. These findings are an integral component of a comprehensive analysis that will additionally investigate the interactions of the TMPRSS2 protein and employ machine learning techniques to discern differences among SARS-CoV-2 lineages, aiming to understand genetic variations and gain insights into S protein dynamics, thereby enhancing our comprehension of SARS-CoV-2's protein-protein biology.