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Abstract The organization of membrane proteins between and within membrane-bound compartments is critical to cellular function. Yet we lack approaches to regulate this organization in a range of membrane-based materials, such as engineered cells, exosomes, and liposomes. Uncovering and leveraging biophysical drivers of membrane protein organization to design membrane systems could greatly enhance the functionality of these materials. Towards this goal, we use de novo protein design, molecular dynamic simulations, and cell-free systems to explore how membrane-protein hydrophobic mismatch could be used to tune protein cotranslational integration and organization in synthetic lipid membranes. We find that membranes must deform to accommodate membrane-protein hydrophobic mismatch, which reduces the expression and co-translational insertion of membrane proteins into synthetic membranes. We use this principle to sort proteins both between and within membranes, thereby achieving one-pot assembly of vesicles with distinct functions and controlled split-protein assembly, respectively. Our results shed light on protein organization in biological membranes and provide a framework to design self-organizing membrane-based materials with applications such as artificial cells, biosensors, and therapeutic nanoparticles. 

Abstract Naturally generated lipid nanoparticles termed extracellular vesicles (EVs) hold significant promise as engineerable therapeutic delivery vehicles. However, active loading of protein cargo into EVs in a manner that is useful for delivery remains a challenge. Here, we demonstrate that by rationally designing proteins to traffic to the plasma membrane and associate with lipid rafts, we can enhance loading of protein cargo into EVs for a set of structurally diverse transmembrane and peripheral membrane proteins. We then demonstrate the capacity of select lipid tags to mediate increased EV loading and functional delivery of an engineered transcription factor to modulate gene expression in target cells. We envision that this technology could be leveraged to develop new EV-based therapeutics that deliver a wide array of macromolecular cargo. 

Abstract De novo design of complex protein folds using solely computational means remains a substantial challenge¹. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors², are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space. 

La Niña climate anomalies have historically been associated with substantial reductions in the atmospheric CO₂growth rate. However, the 2021 La Niña exhibited a unique near-neutral impact on the CO₂growth rate. In this study, we investigate the underlying mechanisms by using an ensemble of net CO₂fluxes constrained by CO₂observations from the Orbiting Carbon Observatory-2 in conjunction with estimates of gross primary production and fire carbon emissions. Our analysis reveals that the close-to-normal atmospheric CO₂growth rate in 2021 was the result of the compensation between increased net carbon uptake over the tropics and reduced net carbon uptake over the Northern Hemisphere mid-latitudes. Specifically, we identify that the extreme drought and warm anomalies in Europe and Asia reduced the net carbon uptake and offset 72% of the increased net carbon uptake over the tropics in 2021. This study contributes to our broader understanding of how regional processes can shape the trajectory of atmospheric CO₂concentration under climate change. 

Abstract Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models. 

Abstract We investigated the biogeography of benthic foraminifera in a highly urbanized tropical seascape, i.e. Hong Kong, in order to assess their utility as bioindicators relative to other marine fauna. Hong Kong is one of the largest coastal cities on the planet and studies of other benthic fauna in the region are available for comparison. We found that: (1) turbid, muddy habitats host a unique foraminiferal fauna; (2) areas with intermediate levels of eutrophication have the highest foraminiferal species diversity; (3) semi-enclosed and heavily polluted environments host a distinct foraminiferal fauna, characterized by low taxonomic diversity and/or high dominance, and that is acclimated to stressful marine conditions. Biodiversity patterns of foraminifera in Hong Kong are generally consistent with those of other soft-sediment macro- and meio-fauna (e.g. polychaetes, molluscs and ostracods); however, foraminifera may be more sensitive than these other groups to eutrophication and associated changes in coastal food webs. The tolerance of some, but not other, species to eutrophic and hypoxic conditions means that foraminiferal faunas can serve as bioindicators across a wide array of environmental conditions, in contrast with corals whose sensitivity to eutrophication results in their absence from eutrophied settings. The well-known autoecology of foraminifera taxa can help to characterize environmental conditions of different habitats and regional environmental gradients. Although the use of fauna as bioindicators may be most robust when data are compared for multiple taxonomic groups, when such broad sampling is not available, benthic foraminifera are particularly well suited for environmental assessments due to their ubiquity, interspecific environmental breadth, and the well-understood environmental preference of individual taxa. 

Abstract Many peptide hormones form an α-helix on binding their receptors^1–4, and sensitive methods for their detection could contribute to better clinical management of disease⁵. De novo protein design can now generate binders with high affinity and specificity to structured proteins^6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion⁸to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful. 

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of “hinge” proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.