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1. Stabilization of supramolecular membrane protein–lipid bilayer assemblies through immobilization in a crystalline exoskeleton

   https://doi.org/10.1038/s41467-021-22285-y  

   Herbert, Fabian C.; Abeyrathna, Sameera S.; Abeyrathna, Nisansala S.; Wijesundara, Yalini H.; Brohlin, Olivia R.; Carraro, Francesco; Amenitsch, Heinz; Falcaro, Paolo; Luzuriaga, Michael A.; Durand-Silva, Alejandra; et al (December 2021, Nature Communications)

   null (Ed.)

   Abstract Artificial native-like lipid bilayer systems constructed from phospholipids assembling into unilamellar liposomes allow the reconstitution of detergent-solubilized transmembrane proteins into supramolecular lipid-protein assemblies called proteoliposomes, which mimic cellular membranes. Stabilization of these complexes remains challenging because of their chemical composition, the hydrophobicity and structural instability of membrane proteins, and the lability of interactions between protein, detergent, and lipids within micelles and lipid bilayers. In this work we demonstrate that metastable lipid, protein-detergent, and protein-lipid supramolecular complexes can be successfully generated and immobilized within zeolitic-imidazole framework (ZIF) to enhance their stability against chemical and physical stressors. Upon immobilization in ZIF bio-composites, blank liposomes, and model transmembrane metal transporters in detergent micelles or embedded in proteoliposomes resist elevated temperatures, exposure to chemical denaturants, aging, and mechanical stresses. Extensive morphological and functional characterization of the assemblies upon exfoliation reveal that all these complexes encapsulated within the framework maintain their native morphology, structure, and activity, which is otherwise lost rapidly without immobilization. 

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2. NME3 binds to phosphatidic acid and mediates PLD6-induced mitochondrial tethering

   https://doi.org/10.1083/jcb.202301091  

   Su, You-An; Chiu, Hsin-Yi; Chang, Yu-Chen; Sung, Chieh-Ju; Chen, Chih-Wei; Tei, Reika; Huang, Xuang-Rong; Hsu, Shao-Chun; Lin, Shan-Shan; Wang, Hsien-Chu; et al (August 2023, Journal of Cell Biology)

   Mitochondria are dynamic organelles regulated by fission and fusion processes. The fusion of membranes requires elaborative coordination of proteins and lipids and is particularly crucial for the function and quality control of mitochondria. Phosphatidic acid (PA) on the mitochondrial outer membrane generated by PLD6 facilitates the fusion of mitochondria. However, how PA promotes mitochondrial fusion remains unclear. Here, we show that a mitochondrial outer membrane protein, NME3, is required for PLD6-induced mitochondrial tethering or clustering. NME3 is enriched at the contact interface of two closely positioned mitochondria depending on PLD6, and NME3 binds directly to PA-exposed lipid packing defects via its N-terminal amphipathic helix. The PA binding function and hexamerization confer NME3 mitochondrial tethering activity. Importantly, nutrient starvation enhances the enrichment efficiency of NME3 at the mitochondrial contact interface, and the tethering ability of NME3 contributes to fusion efficiency. Together, our findings demonstrate NME3 as a tethering protein promoting selective fusion between PLD6-remodeled mitochondria for quality control. 

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3. Intra-FCY1: a novel system to identify mutations that cause protein misfolding

   https://doi.org/10.3389/fgene.2023.1198203  

   Quan, N; Eguchi, Y; Geiler-Samerotte, K (September 2023, Frontiers in Genetics)

   Protein misfolding is a common intracellular occurrence. Most mutations to coding sequences increase the propensity of the encoded protein to misfold. These misfolded molecules can have devastating effects on cells. Despite the importance of protein misfolding in human disease and protein evolution, there are fundamental questions that remain unanswered, such as, which mutations cause the most misfolding? These questions are difficult to answer partially because we lack high-throughput methods to compare the destabilizing effects of different mutations. Commonly used systems to assess the stability of mutant proteinsin vivooften rely upon essential proteins as sensors, but misfolded proteins can disrupt the function of the essential protein enough to kill the cell. This makes it difficult to identify and compare mutations that cause protein misfolding using these systems. Here, we present a novelin vivosystem named Intra-FCY1that we use to identify mutations that cause misfolding of a model protein [yellow fluorescent protein (YFP)] inSaccharomyces cerevisiae. The Intra-FCY1system utilizes two complementary fragments of the yeast cytosine deaminase Fcy1, a toxic protein, into which YFP is inserted. When YFP folds, the Fcy1 fragments associate together to reconstitute their function, conferring toxicity in media containing 5-fluorocytosine and hindering growth. But mutations that make YFP misfold abrogate Fcy1 toxicity, thus strains possessing misfolded YFP variants rise to high frequency in growth competition experiments. This makes such strains easier to study. The Intra-FCY1system cancels localization of the protein of interest, thus can be applied to study the relative stability of mutant versions of diverse cellular proteins. Here, we confirm this method can identify novel mutations that cause misfolding, highlighting the potential for Intra-FCY1to illuminate the relationship between protein sequence and stability. 

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4. Collateral fitness effects of mutations

   https://doi.org/10.1073/pnas.1918680117  

   Mehlhoff, Jacob D.; Stearns, Frank W.; Rohm, Dahlia; Wang, Buheng; Tsou, Erh-Yeh; Dutta, Nisita; Hsiao, Meng-Hsuan; Gonzalez, Courtney E.; Rubin, Alan F.; Ostermeier, Marc (May 2020, Proceedings of the National Academy of Sciences)

   The distribution of fitness effects of mutation plays a central role in constraining protein evolution. The underlying mechanisms by which mutations lead to fitness effects are typically attributed to changes in protein specific activity or abundance. Here, we reveal the importance of a mutation’s collateral fitness effects, which we define as effects that do not derive from changes in the protein’s ability to perform its physiological function. We comprehensively measured the collateral fitness effects of missense mutations in theEscherichia coli TEM-1β-lactamase antibiotic resistance gene using growth competition experiments in the absence of antibiotic. At least 42% of missense mutations inTEM-1were deleterious, indicating that for some proteins collateral fitness effects occur as frequently as effects on protein activity and abundance. Deleterious mutations caused improper posttranslational processing, incorrect disulfide-bond formation, protein aggregation, changes in gene expression, and pleiotropic effects on cell phenotype. Deleterious collateral fitness effects occurred more frequently inTEM-1than deleterious effects on antibiotic resistance in environments with low concentrations of the antibiotic. The surprising prevalence of deleterious collateral fitness effects suggests they may play a role in constraining protein evolution, particularly for highly expressed proteins, for proteins under intermittent selection for their physiological function, and for proteins whose contribution to fitness is buffered against deleterious effects on protein activity and protein abundance. 

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5. Learning the shape of protein microenvironments with a holographic convolutional neural network

   https://doi.org/10.1073/pnas.2300838121  

   Pun, Michael N; Ivanov, Andrew; Bellamy, Quinn; Montague, Zachary; LaMont, Colin; Bradley, Philip; Otwinowski, Jakub; Nourmohammad, Armita (February 2024, Proceedings of the National Academy of Sciences)

   Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure–function maps could guide design of novel proteins with desired function. 

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