Membrane transporters are a class of proteins which alternate through conformational states in order to transport ligands through the plasma membrane. Using molecular dynamics (MD), a trajectory containing the time-evolution of the transporter with atomic level detail is generated with these conformational changes. However, the high dimensionality of the trajectory makes parsing such space a challenge. Using time independent component analysis (tICA), a dimensionality reduction technique which learns the slowest mode of a given protein feature, a decomposition of the trajectory is carried out in order to learn which structural elements of a protein contribute toward a conformational change. By applying tICA to the functionally critical “elevator motion” of the NapA membrane protein, a Na + /H + secondary active transporter that exchanges protons for sodium ions across the cell membrane. We find time-independent components (IC) on the microsecond time scale that correlate with the large conformational transition observed in the MD simulations and allow us to distinguish different functional conformations of NapA, namely, the outward facing (OF) open, occluded (occ), and an inward-facing (IF) open conformation by using dihedral angles and salt bridges as input features. More specifically, tICA was able to identify the formation of a salt bridge network associated with the OF-OCC transition. It has also identified the breaking of said networks associated with OCC-IF transition. In the OCC state, tICA with the combination of hydrogen bond analysis has yielded the formation of a key hydrogen bond between two residues across a broken helix across two transmembrane domains. In summary, we demonstrate how we can use tICA as a data analysis method to discover the molecular processes that are directly correlated with slow, large collective motions in macromolecular systems such as the conformational transitions underlying secondary active transport.
Uy et al. (Sun,) studied this question.