Mechanisms of readthrough mitigation reveal principles of GCN1-mediated translational quality control.

Readthrough into the 3' untranslated region (3' UTR) of the mRNA results in the production of aberrant proteins. Metazoans efficiently clear readthrough proteins, but the underlying mechanisms remain unknown. Here, we show in Caenorhabditis elegans and mammalian cells that readthrough proteins are targeted by a coupled, two-level quality control pathway involving the BAG6 chaperone complex and the ribosome-collision-sensing protein GCN1. Readthrough proteins with hydrophobic C-terminal extensions (CTEs) are recognized by SGTA-BAG6 and ubiquitylated by RNF126 for proteasomal degradation. Additionally, cotranslational mRNA decay initiated by GCN1 and CCR4/NOT limits the accumulation of readthrough products. Unexpectedly, selective ribosome profiling uncovered a general role of GCN1 in regulating translation dynamics when ribosomes collide at nonoptimal codons, enriched in 3' UTRs, transmembrane proteins, and collagens. GCN1 dysfunction increasingly perturbs these protein classes during aging, resulting in mRNA and proteome imbalance. Our results define GCN1 as a key factor acting during translation in maintaining protein homeostasis.

ATP13A1 prevents ERAD of folding-competent mislocalized and misoriented proteins.

The biosynthesis of thousands of proteins requires targeting a signal sequence or transmembrane segment (TM) to the endoplasmic reticulum (ER). These hydrophobic ɑ helices must localize to the appropriate cellular membrane and integrate in the correct topology to maintain a high-fidelity proteome. Here, we show that the P5A-ATPase ATP13A1 prevents the accumulation of mislocalized and misoriented proteins, which are eliminated by different ER-associated degradation (ERAD) pathways in mammalian cells. Without ATP13A1, mitochondrial tail-anchored proteins mislocalize to the ER through the ER membrane protein complex and are cleaved by signal peptide peptidase for ERAD. ATP13A1 also facilitates the topogenesis of a subset of proteins with an N-terminal TM or signal sequence that should insert into the ER membrane with a cytosolic N terminus. Without ATP13A1, such proteins accumulate in the wrong orientation and are targeted for ERAD by distinct ubiquitin ligases. Thus, ATP13A1 prevents ERAD of diverse proteins capable of proper folding.

Teneurin4 dimer structures reveal a calcium-stabilized compact conformation supporting homomeric trans-interactions.

Establishment of correct synaptic connections is a crucial step during neural circuitry formation. The Teneurin family of neuronal transmembrane proteins promotes cell-cell adhesion via homophilic and heterophilic interactions, and is required for synaptic partner matching in the visual and hippocampal systems in vertebrates. It remains unclear how individual Teneurins form macromolecular cis- and trans-synaptic protein complexes. Here, we present a 2.7 Å cryo-EM structure of the dimeric ectodomain of human Teneurin4. The structure reveals a compact conformation of the dimer, stabilized by interactions mediated by the C-rich, YD-shell, and ABD domains. A 1.5 Å crystal structure of the C-rich domain shows three conserved calcium binding sites, and thermal unfolding assays and SAXS-based rigid-body modeling demonstrate that the compactness and stability of Teneurin4 dimers are calcium-dependent. Teneurin4 dimers form a more extended conformation in conditions that lack calcium. Cellular assays reveal that the compact cis-dimer is compatible with homomeric trans-interactions. Together, these findings support a role for teneurins as a scaffold for macromolecular complex assembly and the establishment of cis- and trans-synaptic interactions to construct functional neuronal circuits.

Generation of a Deep Mouse Brain Spectral Library for Transmembrane Proteome Profiling in Mental Disease Models.

Transmembrane (TM) proteins constitute over 30% of the mammalian proteome and play essential roles in mediating cell-cell communication, synaptic transmission, and plasticity in the central nervous system. Many of these proteins, especially the G protein-coupled receptors (GPCRs), are validated or candidate drug targets for therapeutic development for mental diseases, yet their expression profiles are underrepresented in most global proteomic studies. Herein, we establish a brain TM protein-enriched spectral library based on 136 data-dependent acquisition runs acquired from various brain regions of both naïve mice and mental disease models. This spectral library comprises 3043 TM proteins including 171 GPCRs, 231 ion channels, and 598 transporters. Leveraging this library, we analyzed the data-independent acquisition data from different brain regions of two mouse models exhibiting depression- or anxiety-like behaviors. By integrating multiple informatics workflows and library sources, our study significantly expanded the mental stress-perturbed TM proteome landscape, from which a new GPCR regulator of depression was verified by in vivo pharmacological testing. In summary, we provide a high-quality mouse brain TM protein spectral library to largely increase the TM proteome coverage in specific brain regions, which would catalyze the discovery of new potential drug targets for the treatment of mental disorders.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

Transmembrane proteins-Different anchoring systems.

Transmembrane proteins are active in amphipathic environments. To stabilize the protein in such surrounding the exposure of hydrophobic residues on the protein surface is required. Transmembrane proteins are responsible for the transport of various molecules. Therefore, they often represent structures in the form of channels. This analysis focused on the stability and local flexibility of transmembrane proteins, particularly those related to their biological activity. Different forms of anchorage were identified using the fuzzy oil-drop model (FOD) and its modified form, FOD-M. The mainly helical as well as ß-barrel structural forms are compared with respect to the mechanism of stabilization in the cell membrane. The different anchoring system was found to stabilize protein molecules with possible local fluctuation.

Fusogenic vesicular stomatitis virus combined with natural killer T cell immunotherapy controls metastatic breast cancer.

BACKGROUND: Metastatic breast cancer is a leading cause of cancer death in woman. Current treatment options are often associated with adverse side effects and poor outcomes, demonstrating the need for effective new treatments. Immunotherapies can provide durable outcomes in many cancers; however, limited success has been achieved in metastatic triple negative breast cancer. We tested whether combining different immunotherapies can target metastatic triple negative breast cancer in pre-clinical models. METHODS: Using primary and metastatic 4T1 triple negative mammary carcinoma models, we examined the therapeutic effects of oncolytic vesicular stomatitis virus (VSVΔM51) engineered to express reovirus-derived fusion associated small transmembrane proteins p14 (VSV-p14) or p15 (VSV-p15). These viruses were delivered alone or in combination with natural killer T (NKT) cell activation therapy mediated by adoptive transfer of α-galactosylceramide-loaded dendritic cells. RESULTS: Treatment of primary 4T1 tumors with VSV-p14 or VSV-p15 alone increased immunogenic tumor cell death, attenuated tumor growth, and enhanced immune cell infiltration and activation compared to control oncolytic virus (VSV-GFP) treatments and untreated mice. When combined with NKT cell activation therapy, oncolytic VSV-p14 and VSV-p15 reduced metastatic lung burden to undetectable levels in all mice and generated immune memory as evidenced by enhanced in vitro recall responses (tumor killing and cytokine production) and impaired tumor growth upon rechallenge. CONCLUSION: Combining NKT cell immunotherapy with enhanced oncolytic virotherapy increased anti-tumor immune targeting of lung metastasis and presents a promising treatment strategy for metastatic breast cancer.

Recent insights into the control of mitochondrial fission.

Mitochondria are the powerhouse of the cell. They undergo fission and fusion to maintain cellular homeostasis. In this review, we explore the intricate regulation of mitochondrial fission at various levels, including the protein level, the post-translational modification level, and the organelle level. Malfunctions in mitochondrial fission can have detrimental effects on cells. Therefore, we also examine the association between mitochondrial fission with diseases such as breast cancer and cardiovascular disorders. We anticipate that a comprehensive investigation into the control of mitochondrial fission will pave the way for the development of innovative therapeutic strategies.

Computational design of de novo bioenergetic membrane proteins.

The major energy-producing reactions of biochemistry occur at biological membranes. Computational protein design now provides the opportunity to elucidate the underlying principles of these processes and to construct bioenergetic pathways on our own terms. Here, we review recent achievements in this endeavour of 'synthetic bioenergetics', with a particular focus on new enabling tools that facilitate the computational design of biocompatible de novo integral membrane proteins. We use recent examples to showcase some of the key computational approaches in current use and highlight that the overall philosophy of 'surface-swapping' - the replacement of solvent-facing residues with amino acids bearing lipid-soluble hydrophobic sidechains - is a promising avenue in membrane protein design. We conclude by highlighting outstanding design challenges and the emerging role of AI in sequence design and structure ideation.

From bottom-up to cell surface proteomics: detergents or no detergents, that is the question.

Measuring the expression levels of membrane proteins (MPs) is crucial for understanding cell differentiation and tissue specificity, defining disease characteristics, identifying biomarkers, and developing therapeutics. While bottom-up proteomics addresses the need for accurately surveying the membrane proteome, the lower abundance and hydrophobic nature of MPs pose challenges in sample preparation. As MPs normally reside in the lipid bilayer, conventional extraction methods rely on detergents, introducing here a paradox - detergents prevent aggregation and facilitate protein processing, but themselves become contaminants that interfere with downstream analytical applications. Various detergent removal methods exist to mitigate this issue, including filter-aided sample preparation, SP3, suspension trapping, and membrane mimetics. This review delves into the fundamentals of each strategy, applications, merits, and limitations, providing insights into their effectiveness in MP research.

Indirect interactions involving the PsbM or PsbT subunits and the PsbO, PsbU and PsbV proteins stabilize assembly and activity of Photosystem II in Synechocystis sp. PCC 6803.

The low-molecular-weight PsbM and PsbT proteins of Photosystem II (PS II) are both located at the monomer-monomer interface of the mature PS II dimer. Since the extrinsic proteins are associated with the final step of assembly of an active PS II monomer and, in the case of PsbO, are known to impact the stability of the PS II dimer, we have investigated the potential cooperativity between the PsbM and PsbT subunits and the PsbO, PsbU and PsbV extrinsic proteins. Blue-native polyacrylamide electrophoresis and western blotting detected stable PS II monomers in the ∆PsbM:∆PsbO and ∆PsbT:∆PsbO mutants that retained sufficient oxygen-evolving activity to support reduced photoautotrophic growth. In contrast, the ∆PsbM:∆PsbU and ∆PsbT:∆PsbU mutants assembled dimeric PS II at levels comparable to wild type and supported photoautotrophic growth at rates similar to those obtained with the corresponding ∆PsbM and ∆PsbT cells. Removal of PsbV was more detrimental than removal of PsbO. Only limited levels of dimeric PS II were observed in the ∆PsbM:∆PsbV mutant and the overall reduced level of assembled PS II in this mutant resulted in diminished rates of photoautotrophic growth and PS II activity below those obtained in the ∆PsbM:∆PsbO and ∆PsbT:∆PsbO strains. In addition, the ∆PsbT:∆PsbV mutant did not assemble active PS II centers although inactive monomers could be detected. The inability of the ∆PsbT:∆PsbV mutant to grow photoautotrophically, or to evolve oxygen, suggested a stable oxygen-evolving complex could not assemble in this mutant.

Lhs1 dependent ERAD is determined by transmembrane domain context.

Transmembrane proteins have unique requirements to fold and integrate into the endoplasmic reticulum (ER) membrane. Most notably, transmembrane proteins must fold in three separate environments: extracellular domains fold in the oxidizing environment of the ER lumen, transmembrane domains (TMDs) fold within the lipid bilayer, and cytosolic domains fold in the reducing environment of the cytosol. Moreover, each region is acted upon by a unique set of chaperones and monitored by components of the ER associated quality control machinery that identify misfolded domains in each compartment. One factor is the ER lumenal Hsp70-like chaperone, Lhs1. Our previous work established that Lhs1 is required for the degradation of the unassembled α-subunit of the epithelial sodium channel (αENaC), but not the homologous ß- and γENaC subunits. However, assembly of the ENaC heterotrimer blocked the Lhs1-dependent ER associated degradation (ERAD) of the α-subunit, yet the characteristics that dictate the specificity of Lhs1-dependent ERAD substrates remained unclear. We now report that Lhs1-dependent substrates share a unique set of features. First, all Lhs1 substrates appear to be unglycosylated, and second they contain two TMDs. Each substrate also contains orphaned or unassembled TMDs. Additionally, interfering with inter-subunit assembly of the ENaC trimer results in Lhs1-dependent degradation of the entire complex. Finally, our work suggests that Lhs1 is required for a subset of ERAD substrates that also require the Hrd1 ubiquitin ligase. Together, these data provide hints as to the identities of as-yet unconfirmed substrates of Lhs1 and potentially of the Lhs1 homolog in mammals, GRP170.

TMEM44 as a Novel Prognostic Marker for Kidney Renal Clear Cell Carcinoma is Associated with Tumor Invasion, Migration and Immune Infiltration.

Transmembrane (TMEM) proteins are integral membrane proteins that traverse biological membranes. Several members of the TMEM family have been linked to the development and progression of various tumors. However, the specific role and mechanism of TMEM44 in tumor biology remain largely unexplored. In this study, we initially conducted an extensive analysis using the TCGA database to investigate the expression patterns and survival associations of TMEM44 across various human tumors. Subsequently, we focused on KIRC and found a significant correlation between TMEM44 expression and this particular cancer type. To validate our findings, we performed western blot and quantitative polymerase chain reaction (qPCR) assays to confirm the expression levels of TMEM44 in KIRC. Following this, we employed a series of functional assays, including CCK8 viability assay, EDU incorporation assay, wound healing assay, and transwell migration assay, to investigate the biological role of TMEM44 in KIRC. We observed a significant upregulation of TMEM44 expression in KIRC, indicating its potential involvement in the pathogenesis of this cancer. We intervened in the expression of TMEM44 in KIRC cells and found significant inhibitory effects on cell proliferation, migration, and invasion in KIRC cells. Furthermore, our findings indicated that TMEM44 could serve as an independent prognostic factor in KIRC, highlighting its potential clinical significance. Consequently, TMEM44 holds promise as both a prognostic biomarker and a prospective therapeutic target for KIRC.

Engineered Chimera Protein Constructs to Facilitate the Production of Heterologous Transmembrane Proteins in E. coli.

To delve into the structure-function relationship of transmembrane proteins (TMPs), robust protocols are needed to produce them in a pure, stable, and functional state. Among all hosts that express heterologous TMPs, E. coli has the lowest cost and fastest turnover. However, many of the TMPs expressed in E. coli are misfolded. Several strategies have been developed to either direct the foreign TMPs to E. coli's membrane or retain them in a cytosolic soluble form to overcome this deficiency. Here, we summarize protein engineering methods to produce chimera constructs of the desired TMPs fused to either a signal peptide or precursor maltose binding protein (pMBP) to direct the entire construct to the periplasm, therefore depositing the fused TMP in the plasma membrane. We further describe strategies to produce TMPs in soluble form by utilizing N-terminally fused MBP without a signal peptide. Depending on its N- or C-terminus location, a fusion to apolipoprotein AI can either direct the TMP to the membrane or shield the hydrophobic regions of the TMP, maintaining the soluble form. Strategies to produce G-protein-coupled receptors, TMPs of Mycobacterium tuberculosis, HIV-1 Vpu, and other TMPs are discussed. This knowledge could increase the scope of TMPs' expression in E. coli.

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features.

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2's predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

SARS-CoV-2 Variants of Concern Hijack IFITM2 for Efficient Replication in Human Lung Cells.

It has recently been shown that an early SARS-CoV-2 isolate (NL-02-2020) hijacks interferon-induced transmembrane proteins (IFITMs) for efficient replication in human lung cells, cardiomyocytes, and gut organoids. To date, several "variants of concern" (VOCs) showing increased infectivity and resistance to neutralization have emerged and globally replaced the early viral strains. Here, we determined whether the five current SARS-CoV-2 VOCs (Alpha, Beta, Gamma, Delta, and Omicron) maintained the dependency on IFITM proteins for efficient replication. We found that depletion of IFITM2 strongly reduces viral RNA production by all VOCs in the human epithelial lung cancer cell line Calu-3. Silencing of IFITM1 had modest effects, while knockdown of IFITM3 resulted in an intermediate phenotype. Strikingly, depletion of IFITM2 generally reduced infectious virus production by more than 4 orders of magnitude. In addition, an antibody directed against the N terminus of IFITM2 inhibited SARS-CoV-2 VOC replication in induced pluripotent stem cell (iPSC)-derived alveolar epithelial type II cells, thought to represent major viral target cells in the lung. In conclusion, endogenously expressed IFITM proteins (especially IFITM2) are critical cofactors for efficient replication of genuine SARS-CoV-2 VOCs, including the currently dominant Omicron variant. IMPORTANCE Recent data indicate that SARS-CoV-2 requires endogenously expressed IFITM proteins for efficient infection. However, the results were obtained with an early SARS-CoV-2 isolate. Thus, it remained to be determined whether IFITMs are also important cofactors for infection of emerging SARS-CoV-2 VOCs that outcompeted the original strains in the meantime. This includes the Omicron VOC, which currently dominates the pandemic. Here, we show that depletion of endogenous IFITM2 expression almost entirely prevents productive infection of Alpha, Beta, Gamma, Delta, and Omicron SARS-CoV-2 VOCs in human lung cells. In addition, an antibody targeting the N terminus of IFITM2 inhibited SARS-CoV-2 VOC replication in iPSC-derived alveolar epithelial type II cells. Our results show that SARS-CoV-2 VOCs, including the currently dominant Omicron variant, are strongly dependent on IFITM2 for efficient replication, suggesting a key proviral role of IFITMs in viral transmission and pathogenicity.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins.

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

Ins and outs of AlphaFold2 transmembrane protein structure predictions.

Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind's AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.

Importance of accessibility to the extracellular juxtamembrane stalk region of membrane protein for substrate recognition by viral ubiquitin ligase K5.

Kaposi's sarcoma-associated herpesvirus (KSHV) is a carcinogenic virus that latently infects B cells and causes malignant tumors in immunocompromised patients. KSHV utilizes two viral E3 ubiquitin ligases, K3 and K5, in KSHV-infected cells to mediate the polyubiquitination-dependent down-regulation of several host membrane proteins involved in the immune system. Although K3 and K5 are members of the same family and have similar structural topologies, K3 and K5 have different substrate specificities. Hence, K5 may have a different substrate recognition mode than K3; however, the molecular basis of substrate recognition remains unclear. Here, we investigated the reason why human CD8α, which is known not to be a substrate for both K3 and K5, is not recognized by them, to obtain an understanding for molecular basis of substrate specificity. CD8α forms a disulfide-linked homodimer under experimental conditions to evaluate the viral ligase-mediated down-regulation. It is known that two interchain disulfide linkages in the stalk region between each CD8α monomer (Cys164-Cys164 and Cys181-Cys181) mediate homodimerization. When the interchain disulfide linkage of Cys181-Cys181 was eliminated, CD8α was down-regulated by K5 with a functional RING variant (RINGv) domain via polyubiquitination at the cytoplasmic tail. Aspartic acid, located at the stalk/transmembrane interface of CD8α, was essential for K5-mediated down-regulation of the CD8α mutant without a Cys181-Cys181 linkage. These results suggest that disulfide linkage near the stalk/transmembrane interface critically inhibits substrate targeting by K5. Accessibility to the extracellular juxtamembrane stalk region of membrane proteins may be important for substrate recognition by the viral ubiquitin ligase K5.

The First Quarter Century of the Dense Alignment Surface Transmembrane Prediction Method.

The dense alignment surface (DAS) transmembrane (TM) prediction method was first published more than 25 years ago. DAS was the one of the earliest tools to discriminate TM proteins from globular ones and to predict the sequence positions of TM helices in proteins with high accuracy from their amino acid sequence alone. The algorithmic improvements that followed in 2002 (DAS-TMfilter) made it one of the best performing tools among those relying on local sequence information for TM prediction. Since then, many more experimental data about membrane proteins (including thousands of 3D structures of membrane proteins) have accumulated but there has been no significant improvement concerning performance in the area of TM helix prediction tools. Here, we report a new implementation of the DAS-TMfilter prediction web server. We reevaluated the performance of the method using a five-times-larger, updated test dataset. We found that the method performs at essentially the same accuracy as the original even without any change to the parametrization of the program despite the much larger dataset. Thus, the approach captures the physico-chemistry of TM helices well, essentially solving this scientific problem.