The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations.

Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.

Substrate binding-induced conformational transitions in the omega-3 fatty acid transporter MFSD2A.

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.

Modeling global changes induced by local perturbations to the HIV-1 capsid.

The HIV-1 capsid is a conical protein shell made up of hexamers and pentamers of the capsid protein. The capsid houses the viral genome and replication machinery, and its opening, or uncoating, within the host cell marks a critical step in the HIV-1 lifecycle. Binding of host factors such as TRIM5α and cyclophilin A (CypA) can alter the capsid's stability, accelerating or delaying the onset of uncoating and disrupting infectivity. We employ coarse-grained computational modeling to investigate the effects of point mutations and host factor binding on HIV-1 capsid stability. We find that the largest fluctuations occur in the low-curvature regions of the capsid, and that its structural dynamics are affected by perturbations at the inter-hexamer interfaces and near the CypA binding loop, suggesting roles for these features in capsid stability. Our models show that linking capsid proteins across hexamers attenuates vibration in the low-curvature regions of the capsid, but that linking within hexamers does not. These results indicate a possible mechanism through which CypA binding alters capsid stability and highlight the utility of coarse-grained network modeling for understanding capsid mechanics.