Understanding the intricate motions and conformational changes that govern biological processes remains a fascinating and challenging endeavor. The research presented in this thesis aims to elucidate the dynamic processes underlying biological functions, specifically focusing on the dynamics of pentameric ligand-gated ion channels (pLGICs), which play a crucial role in signal transduction within the brain. To achieve this, molecular dynamics (MD) simulations were employed to examine local protein dynamics within their native environment and in response to perturbations with collaborative engagements with experimentalists. It enabled the investigation of ion permeation pathways in nicotinic receptors, which involve the less-explored lateral portals in the intracellular domain. I further quantified the permeation free energy using enhanced sampling methods for different structural models, subsequently providing functional annotations for the observed states. Furthermore, I revealed the interplay between different ligand binding sites in gamma-aminobutyric acid type (GABA) A receptors and their bacterial homologue, shedding light on how the binding of positive allosteric modulators can influence agonist affinity. Additionally, I used coarse-grained simulations to identify the binding sites of these modulators and illustrated the importance of differential binding in channel gating. These findings led to the formulation of a testable hypothesis regarding a competitive mechanism between lipids, e.g. cholesterol, and lipidic drugs. To validate the hypothesis and enhance our understanding of the complete gating cycle, I conducted extensive simulations of nicotinic receptors, coupled with the development of machine learning algorithms for constructing Markov state model. This comprehensive investigation provided crucial insights into gating dynamics and functional modulation, emphasizing the critical role of symmetry. Moreover, it laid a solid foundation for future research in rational drug design. In conclusion, this thesis contributes to our understanding of the intricate dynamics and complexity of proteins as biological machinery through the analysis of their dynamic landscapes. It expands our knowledge of pLGICs and their essential roles in biological systems, while also offering valuable insights for the development of therapeutics.