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.

Photoswitchable Serotonins for Optical Control of the 5-HT2A Receptor.

Serotonin receptors play central roles in neuromodulation and are critical drug targets for psychiatric disorders. Optical control of serotonin receptor subtypes has the potential to greatly enhance our understanding of the spatiotemporal dynamics of receptor function. While other neuromodulatory receptors have been successfully rendered photoswitchable, reversible photocontrol of serotonin receptors has not been achieved, representing a major gap in GPCR photopharmacology. Herein, we develop the first tools that allow for such control. Azo5HT-2 shows light-dependent 5-HT2A R agonism, with greater activity in the cis-form. Based on docking and test compound analysis, we also develop photoswitchable orthogonal, remotely-tethered ligands (PORTLs). These BG-Azo5HTs provide rapid, reversible, and repeatable optical control following conjugation to SNAP-tagged 5-HT2A R. Overall, this study provides a foundation for the broad extension of photopharmacology to the serotonin receptor family.

Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol.

Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery.

Ca2+-dependent mechanism of membrane insertion and destabilization by the SARS-CoV-2 fusion peptide.

Cell penetration after recognition of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus by the ACE2 receptor and the fusion of its viral envelope membrane with cellular membranes are the early steps of infectivity. A region of the Spike protein of the virus, identified as the "fusion peptide" (FP), is liberated at its N-terminal site by a specific cleavage occurring in concert with the interaction of the receptor-binding domain of the Spike. Studies have shown that penetration is enhanced by the required binding of Ca2+ ions to the FPs of coronaviruses, but the mechanisms of membrane insertion and destabilization remain unclear. We have predicted the preferred positions of Ca2+ binding to the SARS-CoV-2-FP, the role of Ca2+ ions in mediating peptide-membrane interactions, the preferred mode of insertion of the Ca2+-bound SARS-CoV-2-FP, and consequent effects on the lipid bilayer from extensive atomistic molecular dynamics simulations and trajectory analyses. In a systematic sampling of the interactions of the Ca2+-bound peptide models with lipid membranes, SARS-CoV-2-FP penetrated the bilayer and disrupted its organization only in two modes involving different structural domains. In one, the hydrophobic residues F833/I834 from the middle region of the peptide are inserted. In the other, more prevalent mode, the penetration involves residues L822/F823 from the LLF motif, which is conserved in CoV-2-like viruses, and is achieved by the binding of Ca2+ ions to the D830/D839 and E819/D820 residue pairs. FP penetration is shown to modify the molecular organization in specific areas of the bilayer, and the extent of membrane binding of the SARS-CoV-2 FP is significantly reduced in the absence of Ca2+ ions. These findings provide novel mechanistic insights regarding the role of Ca2+ in mediating SARS-CoV-2 fusion and provide a detailed structural platform to aid the ongoing efforts in rational design of compounds to inhibit SARS-CoV-2 cell entry.

Ca 2+ -dependent mechanism of membrane insertion and destabilization by the SARS-CoV-2 fusion peptide.

Cell penetration after recognition of the SARS-CoV-2 virus by the ACE2 receptor, and the fusion of its viral envelope membrane with cellular membranes, are the early steps of infectivity. A region of the Spike protein (S) of the virus, identified as the "fusion peptide" (FP), is liberated at its N-terminal site by a specific cleavage occurring in concert with the interaction of the receptor binding domain of the Spike. Studies have shown that penetration is enhanced by the required binding of Ca 2+ ions to the FPs of corona viruses, but the mechanisms of membrane insertion and destabilization remain unclear. We have predicted the preferred positions of Ca 2+ binding to the SARS-CoV-2-FP, the role of Ca 2+ ions in mediating peptide-membrane interactions, the preferred mode of insertion of the Ca 2+ -bound SARS-CoV-2-FP and consequent effects on the lipid bilayer from extensive atomistic molecular dynamics (MD) simulations and trajectory analyses. In a systematic sampling of the interactions of the Ca 2+ -bound peptide models with lipid membranes SARS-CoV-2-FP penetrated the bilayer and disrupted its organization only in two modes involving different structural domains. In one, the hydrophobic residues F833/I834 from the middle region of the peptide are inserted. In the other, more prevalent mode, the penetration involves residues L822/F823 from the LLF motif which is conserved in CoV-2-like viruses, and is achieved by the binding of Ca 2+ ions to the D830/D839 and E819/D820 residue pairs. FP penetration is shown to modify the molecular organization in specific areas of the bilayer, and the extent of membrane binding of the SARS-CoV-2 FP is significantly reduced in the absence of Ca 2+ ions. These findings provide novel mechanistic insights regarding the role of Ca 2+ in mediating SARS-CoV-2 fusion and provide a detailed structural platform to aid the ongoing efforts in rational design of compounds to inhibit SARS-CoV-2 cell entry. STATEMENT OF SIGNIFICANCE: SARS-CoV-2, the cause of the COVID-19 pandemic, penetrates host cell membranes and uses viral-to-cellular membrane fusion to release its genetic material for replication. Experiments had identified a region termed "fusion peptide" (FP) in the Spike proteins of coronaviruses, as the spearhead in these initial processes, and suggested that Ca 2+ is needed to support both functions. Absent structure and dynamics-based mechanistic information these FP functions could not be targeted for therapeutic interventions. We describe the development and determination of the missing information from analysis of extensive MD simulation trajectories, and propose specific Ca 2+ -dependent mechanisms of SARS-CoV-2-FP membrane insertion and destabilization. These results offer a structure-specific platform to aid the ongoing efforts to use this target for the discovery and/or of inhibitors.

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.