Across the tree of life, nature is governed by fine-tuned and modulated processes at the molecular level. Proteins, especially membrane proteins, play a pivotal role due to their ability to undergo conformational changes. The sequence-structure-function relationship is the holy grail of the study of proteins. Once deciphered, we can study this relationship under different conditions and alter the function of existing proteins by developing drugs that target specific molecular determinants in a particular member of the family. Standard molecular dynamics (MD) simulations, while enabling essential insights at atomistic resolution, are usually too short to access rare events such as conformational transitions. Our group has thus acquired expertise in developing enhanced sampling techniques to study conformational ensembles. A mathematical description of the different conformational states of the system as a function of its atomic coordinates is required to obtain free energy landscapes (FELs) of conformational change; this description is referred to as a collective variable (CV). Typically, a good CV contains the slowest degrees of freedom, where the highest free energy barriers reside. Obtaining such CVs requires careful consideration: data-driven approaches make it possible to leverage large structural datasets but their application hinges on expert choices. Our group has thus focused on developing expertise in this area and become one of the world-leading groups when it comes to determining CVs for complex conformational changes. In addition to a suitable CV selection, broad sampling is a requirement to obtain FELs; in our group, we have developed in-depth knowledge of the accelerated weight histogram (AWH) method, an efficient sampling algorithm. In this project, we aim to continue to characterize the functional cycles and modulation profiles of three classes of membrane proteins: 1) G-protein coupled receptors (GPCRs); 2) MFS transporters; and 3) voltage-gated ion channels.