Regulation of retromer recruitment to endosomes by sequential action of Rab5 and Rab7

The retromer complex mediates retrograde transport of transmembrane cargo from endosomes to the trans-Golgi network (TGN). Mammalian retromer is composed of a sorting nexin (SNX) dimer that binds to phosphatidylinositol 3-phosphate–enriched endosomal membranes and a vacuolar protein sorting (Vps) 26/29/35 trimer that participates in cargo recognition. The mammalian SNX dimer is necessary but not sufficient for recruitment of the Vps26/29/35 trimer to membranes. In this study, we demonstrate that the guanosine triphosphatase Rab7 contributes to this recruitment. The Vps26/29/35 trimer specifically binds to Rab7–guanosine triphosphate (GTP) and localizes to Rab7-containing endosomal domains. Interference with Rab7 function causes dissociation of the Vps26/29/35 trimer but not the SNX dimer from membranes. This blocks retrieval of mannose 6-phosphate receptors to the TGN and impairs cathepsin D sorting. Rab5-GTP does not bind to the Vps26/29/35 trimer, but perturbation of Rab5 function causes dissociation of both the SNX and Vps26/29/35 components from membranes through inhibition of a pathway involving phosphatidylinositol 3-kinase. These findings demonstrate that Rab5 and Rab7 act in concert to regulate retromer recruitment to endosomes

Standardised Data on Initiatives – STARDIT: Beta Version

There is currently no standardised way to share information across disciplines about initiatives, including felds such as health, environment, basic science, manufacturing, media and international development. All problems, including complex global problems such as air pollution and pandemics require reliable data sharing between disciplines in order to respond efectively. Current reporting methods also lack information about the ways in which diferent people and organisations are involved in initiatives, making it difcult to collate and appraise data about the most efective ways to involve diferent people. The objective of STARDIT (Standardised Data on Initiatives) is to address current limitations and inconsistencies in sharing data about initiatives. The STARDIT system features standardised data reporting about initiatives, including who has been involved, what tasks they did, and any impacts observed. STARDIT was created to help everyone in the world fnd and understand information about collective human actions, which are referred to as ‘initiatives’. STARDIT enables multiple categories of data to be reported in a standardised way across disciplines, facilitating appraisal of initiatives and aiding synthesis of evidence for the most effective ways for people to be involved in initiatives

Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein–Protein Interaction Interfaces

The ability to target protein–protein interactions (PPIs) with small molecule inhibitors offers great promise in expanding the druggable target space and addressing a broad range of untreated diseases. However, due to their nature and function of interacting with protein partners, PPI interfaces tend to extend over large surfaces without the typical pockets of enzymes and receptors. These features present unique challenges for small molecule inhibitor design. As such, determining whether a particular PPI of interest could be pursued with a small molecule discovery strategy requires an understanding of the characteristics of the PPI interface and whether it has hotspots that can be leveraged by small molecules to achieve desired potency. Here, we assess the ability of mixed-solvent molecular dynamic (MSMD) simulations to detect hotspots at PPI interfaces. MSMD simulations using three cosolvents (acetonitrile, isopropanol, and pyrimidine) were performed on a large test set of 21 PPI targets that have been experimentally validated by small molecule inhibitors. We compare MSMD, which includes explicit solvent and full protein flexibility, to a simpler approach that does not include dynamics or explicit solvent (SiteMap) and find that MSMD simulations reveal additional information about the characteristics of these targets and the ability for small molecules to inhibit the PPI interface. In the few cases were MSMD simulations did not detect hotspots, we explore the shortcomings of this technique and propose future improvements. Finally, using Interleukin-2 as an example, we highlight the advantage of the MSMD approach for detecting transient cryptic druggable pockets that exists at PPI interfaces

Selecting an Optimal Number of Binding Site Waters To Improve Virtual Screening Enrichments Against the Adenosine A_2A Receptor

A major challenge in structure-based virtual screening (VS) involves the treatment of explicit water molecules during docking in order to improve the enrichment of active compounds over decoys. Here we have investigated this in the context of the adenosine A_2A receptor, where water molecules have previously been shown to be important for achieving high enrichment rates with docking, and where the positions of some binding site waters are known from a high-resolution crystal structure. The effect of these waters (both their presence and orientations) on VS enrichment was assessed using a carefully curated set of 299 high affinity A_2A antagonists and 17,337 decoys. We show that including certain crystal waters greatly improves VS enrichment and that optimization of water hydrogen positions is needed in order to achieve the best results. We also show that waters derived from a molecular dynamics simulation  without any knowledge of crystallographic waters  can improve enrichments to a similar degree as the crystallographic waters, which makes this strategy applicable to structures without experimental knowledge of water positions. Finally, we used decision trees to select an ensemble of structures with different water molecule positions and orientations that outperforms any single structure with water molecules. The approach presented here is validated against independent test sets of A_2A receptor antagonists and decoys from the literature. In general, this water optimization strategy could be applied to any target with waters-mediated protein–ligand interactions

Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation

The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A_2AAR, β₁ adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A_2A receptor (A_2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects

Standardised data on initiatives—STARDIT:Beta version

Background and objective There is currently no standardised way to share information across disciplines about initiatives, including fields such as health, environment, basic science, manufacturing, media and international development. All problems, including complex global problems such as air pollution and pandemics require reliable data sharing between disciplines in order to respond effectively. Current reporting methods also lack information about the ways in which different people and organisations are involved in initiatives, making it difficult to collate and appraise data about the most effective ways to involve different people. The objective of STARDIT (Standardised Data on Initiatives) is to address current limitations and inconsistencies in sharing data about initiatives. The STARDIT system features standardised data reporting about initiatives, including who has been involved, what tasks they did, and any impacts observed. STARDIT was created to help everyone in the world find and understand information about collective human actions, which are referred to as ‘initiatives’. STARDIT enables multiple categories of data to be reported in a standardised way across disciplines, facilitating appraisal of initiatives and aiding synthesis of evidence for the most effective ways for people to be involved in initiatives. This article outlines progress to date on STARDIT; current usage; information about submitting reports; planned next steps and how anyone can become involved. Method STARDIT development is guided by participatory action research paradigms, and has been co-created with people from multiple disciplines and countries. Co-authors include cancer patients, people affected by rare diseases, health researchers, environmental researchers, economists, librarians and academic publishers. The co-authors also worked with Indigenous peoples from multiple countries and in partnership with an organisation working with Indigenous Australians. Results and discussion Over 100 people from multiple disciplines and countries have been involved in co-designing STARDIT since 2019. STARDIT is the first open access web-based data-sharing system which standardises the way that information about initiatives is reported across diverse fields and disciplines, including information about which tasks were done by which stakeholders. STARDIT is designed to work with existing data standards. STARDIT data will be released into the public domain (CC0) and integrated into Wikidata; it works across multiple languages and is both human and machine readable. Reports can be updated throughout the lifetime of an initiative, from planning to evaluation, allowing anyone to be involved in reporting impacts and outcomes. STARDIT is the first system that enables sharing of standardised data about initiatives across disciplines. A working Beta version was publicly released in February 2021 (ScienceforAll.World/STARDIT). Subsequently, STARDIT reports have been created for peer-reviewed research in multiple journals and multiple research projects, demonstrating the usability. In addition, organisations including Cochrane and Australian Genomics have created prospective reports outlining planned initiatives. Conclusions STARDIT can help create high-quality standardised information on initiatives trying to solve complex multidisciplinary global problems

Assembly of the Biogenesis of Lysosome-related Organelles Complex-3 (BLOC-3) and Its Interaction with Rab9*

The Hermansky-Pudlak syndrome (HPS) is a genetic hypopigmentation and bleeding disorder caused by defective biogenesis of lysosome-related organelles (LROs) such as melanosomes and platelet dense bodies. HPS arises from mutations in any of 8 genes in humans and 16 genes in mice. Two of these genes, HPS1 and HPS4, encode components of the biogenesis of lysosome-related organelles complex-3 (BLOC-3). Herein we show that recombinant HPS1-HPS4 produced in insect cells can be efficiently isolated as a 1:1 heterodimer. Analytical ultracentrifugation reveals that this complex has a molecular mass of 146 kDa, equivalent to that of the native complex and to the sum of the predicted molecular masses of HPS1 and HPS4. This indicates that HPS1 and HPS4 interact directly in the absence of any other protein as part of BLOC-3. Limited proteolysis and deletion analyses show that both subunits interact with one another throughout most of their lengths with the sole exception of a long, unstructured loop in the central part of HPS4. An interaction screen reveals a specific and strong interaction of BLOC-3 with the GTP-bound form of the endosomal GTPase, Rab9. This interaction is mediated by HPS4 and the switch I and II regions of Rab9. These characteristics indicate that BLOC-3 might function as a Rab9 effector in the biogenesis of LROs