Major Facilitator Superfamily Domain containing 2 A (MFSD2A) is a transporter that is highly enriched at the blood-brain and blood-retinal barriers, where it mediates Na+-dependent uptake of ω−3 fatty acids in the form of lysolipids into the brain and eyes, respectively. Despite recent structural insights, it remains unclear how this process is initiated, and driven by Na+. Here, we perform Molecular Dynamics simulations which demonstrate that substrates enter outward facing MFSD2A from the outer leaflet of the membrane via lateral openings between transmembrane helices 5/8 and 2/11. The substrate headgroup enters first and engages in Na+ -bridged interactions with a conserved glutamic acid, while the tail is surrounded by hydrophobic residues. This binding mode is consistent with a “trap-and-flip” mechanism and triggers transition to an occluded conformation. Furthermore, using machine learning analysis, we identify key elements that enable these transitions. These results advance our molecular understanding of the MFSD2A transport cycle.

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Substrates, inhibitors, and various classes of drugs of abuse that target the human dopamine transporter (hDAT) elicit a variety of responses measurable in vitro and in vivo. Classification of such ligands in terms of their chemical and/or pharmacological properties has failed to produce sufficient mechanistic explanations, or hypotheses, to guide a satisfactory understanding of the nature of this diversity in the effects of hDAT ligands. With the objective of advancing the current understanding of these drugs and their mechanisms of action, we set out to investigate the role that the response of the hDAT molecule to the binding of these ligands may have in their known diverse behavioral effects. We use structurally and pharmacologically diverse ligands of hDAT (e.g., typical inhibitors, atypical inhibitors, substrates and releasers) in extensive all‐atom molecular dynamics (MD) simulations to reveal the structural and dynamic effects that characterize the response of the transporter molecule to each of them and to enable a classification of hDAT molecular responses. MD trajectories describing the time‐dependent dynamic behavior of each complex are analyzed to quantify the molecular responses of hDAT, and machine learning algorithms serve to classify each response and reveal specific dynamic features underlying the molecular mechanism of hDAT’s response to each ligand. This approach offers mechanistic insight into the role of the ligand‐determined molecular response of hDAT in determining the diversity of effects expressed in the functional outcomes. Support or Funding Information SB is supported by T32 “Genetic and environmental influences on addiction” (DA03980), research is supported by NIH Grant R01 DA041510 (to H.W.)