OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar

Divergent folding-mediated epistasis among unstable membrane protein variants

Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally.

Experimental and computational approaches for membrane protein insertion and topology determination

Membrane proteins play pivotal roles in a wide array of cellular processes and constitute approximately a quarter of the protein-coding genes across all organisms. Despite their ubiquity and biological significance, our understanding of these proteins remains notably less comprehensive compared to their soluble counterparts. This disparity in knowledge can be attributed, in part, to the inherent challenges associated with employing specialized techniques for the investigation of membrane protein insertion and topology. This review will center on a discussion of molecular biology methodologies and computational prediction tools designed to elucidate the insertion and topology of helical membrane proteins.

Divergent Folding-Mediated Epistasis Among Unstable Membrane Protein Variants

Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Brewpitopes: a pipeline to refine B-cell epitope predictions during public health emergencies

The application of B-cell epitope identification to develop therapeutic antibodies and vaccine candidates is well established. However, the validation of epitopes is time-consuming and resource-intensive. To alleviate this, in recent years, multiple computational predictors have been developed in the immunoinformatics community. Brewpitopes is a pipeline that curates bioinformatic B-cell epitope predictions obtained by integrating different state-of-the-art tools. We used additional computational predictors to account for subcellular location, glycosylation status, and surface accessibility of the predicted epitopes. The implementation of these sets of rational filters optimizes in vivo antibody recognition properties of the candidate epitopes. To validate Brewpitopes, we performed a proteome-wide analysis of SARS-CoV-2 with a particular focus on S protein and its variants of concern. In the S protein, we obtained a fivefold enrichment in terms of predicted neutralization versus the epitopes identified by individual tools. We analyzed epitope landscape changes caused by mutations in the S protein of new viral variants that were linked to observed immune escape evidence in specific strains. In addition, we identified a set of epitopes with neutralizing potential in four SARS-CoV-2 proteins (R1AB, R1A, AP3A, and ORF9C). These epitopes and antigenic proteins are conserved targets for viral neutralization studies. In summary, Brewpitopes is a powerful pipeline that refines B-cell epitope bioinformatic predictions during public health emergencies in a high-throughput capacity to facilitate the optimization of experimental validation of therapeutic antibodies and candidate vaccines.

Genomic and proteomic characterization of vB_SauM-UFV_DC4, a novel Staphylococcus jumbo phage

Staphylococcus aureus is one of the most relevant mastitis pathogens in dairy cattle, and the acquisition of antimicrobial resistance genes presents a significant health issue in both veterinary and human fields. Among the different strategies to tackle S. aureus infection in livestock, bacteriophages have been thoroughly investigated in the last decades; however, few specimens of the so-called jumbo phages capable of infecting S. aureus have been described. Herein, we report the biological, genomic, and structural proteomic features of the jumbo phage vB_SauM-UFV_DC4 (DC4). DC4 exhibited a remarkable killing activity against S. aureus isolated from the veterinary environment and stability at alkaline conditions (pH 4 to 12). The complete genome of DC4 is 263,185 bp (GC content: 25%), encodes 263 predicted CDSs (80% without an assigned function), 1 tRNA (Phe-tRNA), multisubunit RNA polymerase, and an RNA-dependent DNA polymerase. Moreover, comparative analysis revealed that DC4 can be considered a new viral species belonging to a new genus DC4 and showed a similar set of lytic proteins and depolymerase activity with closely related jumbo phages. The characterization of a new S. aureus jumbo phage increases our understanding of the diversity of this group and provides insights into the biotechnological potential of these viruses. KEY POINTS: • vB_SauM-UFV_DC4 is a new viral species belonging to a new genus within the class Caudoviricetes. • vB_SauM-UFV_DC4 carries a set of RNA polymerase subunits and an RNA-directed DNA polymerase. • vB_SauM-UFV_DC4 and closely related jumbo phages showed a similar set of lytic proteins.

The LytS-type histidine kinase BtsS is a 7-transmembrane receptor that binds pyruvate

IMPORTANCE

Here, we studied the LytS-type histidine kinase BtsS of E. coli and identified the pyruvate binding site within the membrane-spanning domains. It is a small cavity, and pyruvate forms interactions with the side chains of Arg72, Arg99, Cys110, and Ser113 located in transmembrane helices III, IV, and V, respectively. Our results can serve as a starting point to convert BtsS into a sensor for structurally similar ligands such as lactate, which can be used as biosensor in medicine.

Elucidating Novel Targets for Ovarian Cancer Antibody-Drug Conjugate Development: Integrating In Silico Prediction and Surface Plasmon Resonance to Identify Targets with Enhanced Antibody Internalization Capacity

Antibody-drug conjugates (ADCs) constitute a rapidly expanding category of biopharmaceuticals that are reshaping the landscape of targeted chemotherapy. The meticulous process of selecting therapeutic targets, aided by specific monoclonal antibodies' high specificity for binding to designated antigenic epitopes, is pivotal in ADC research and development. Despite ADCs' intrinsic ability to differentiate between healthy and cancerous cells, developmental challenges persist. In this study, we present a rationalized pipeline encompassing the initial phases of the ADC development, including target identification and validation. Leveraging an in-house, computationally constructed ADC target database, termed ADC Target Vault, we identified a set of novel ovarian cancer targets. We effectively demonstrate the efficacy of Surface Plasmon Resonance (SPR) technology and in vitro models as predictive tools, expediting the selection and validation of targets as ADC candidates for ovarian cancer therapy. Our analysis reveals three novel robust antibody/target pairs with strong binding and favourable antibody internalization rates in both wild-type and cisplatin-resistant ovarian cancer cell lines. This approach enhances ADC development and offers a comprehensive method for assessing target/antibody combinations and pre-payload conjugation biological activity. Additionally, the strategy establishes a robust platform for high-throughput screening of potential ovarian cancer ADC targets, an approach that is equally applicable to other cancer types.

A3DyDB: exploring structural aggregation propensities in the yeast proteome

BACKGROUND

The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation.

RESULTS

In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast .

CONCLUSION

The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast.

General trends in the effects of VX-661 and VX-445 on the plasma membrane expression of clinical CFTR variants

Cystic fibrosis (CF) is caused by mutations that compromise the expression and/or function of the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel. Most people with CF harbor a common misfolded variant (ΔF508) that can be partially rescued by therapeutic "correctors" that restore its expression. Nevertheless, many other CF variants are insensitive to correctors. Using deep mutational scanning, we quantitatively compare the effects of two correctors on the plasma membrane expression of 129 CF variants. Though structural calculations suggest corrector binding provides similar stabilization to most variants, it's those with intermediate expression and mutations near corrector binding pockets that exhibit the greatest response. Deviations in sensitivity appear to depend on the degree of variant destabilization and the timing of misassembly. Combining correctors appears to rescue more variants by doubling the binding energy and stabilizing distinct cotranslational folding transitions. These results provide an overview of rare CF variant expression and establish new tools for precision pharmacology.

Phosphatidylserine-dependent structure of synaptogyrin remodels the synaptic vesicle membrane

Synaptic vesicles are small membrane-enclosed organelles that store neurotransmitters at presynaptic terminals. The uniform morphology of synaptic vesicles is important for brain function, because it enables the storage of well-defined amounts of neurotransmitters and thus reliable synaptic transmission. Here, we show that the synaptic vesicle membrane protein synaptogyrin cooperates with the lipid phosphatidylserine to remodel the synaptic vesicle membrane. Using NMR spectroscopy, we determine the high-resolution structure of synaptogyrin and identify specific binding sites for phosphatidylserine. We further show that phosphatidylserine binding changes the transmembrane structure of synaptogyrin and is critical for membrane bending and the formation of small vesicles. Cooperative binding of phosphatidylserine to both a cytoplasmic and intravesicular lysine-arginine cluster in synaptogyrin is required for the formation of small vesicles. Together with other synaptic vesicle proteins, synaptogyrin thus can sculpt the membrane of synaptic vesicles.

Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks

Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than soluble proteins. The topology of transmembrane proteins (TMPs) can determine the spatial conformation relative to the membrane, while the secondary structure helps to identify their functional domain. They are highly correlated on αTMPs sequences, and achieving a merge prediction is instructive for further understanding the structure and function of αTMPs. In this study, we implemented a hybrid model combining Deep Learning Neural Networks (DNNs) with a Class Hidden Markov Model (CHMM), namely HDNNtopss. DNNs extract rich contextual features through stacked attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs), and CHMM captures state-associative temporal features. The hybrid model not only reasonably considers the probability of the state path but also has a fitting and feature-extraction capability for deep learning, which enables flexible prediction and makes the resulting sequence more biologically meaningful. It outperforms current advanced merge-prediction methods with a Q4 of 0.779 and an MCC of 0.673 on the independent test dataset, which have practical, solid significance. In comparison to advanced prediction methods for topological and secondary structures, it achieves the highest topology prediction with a Q2 of 0.884, which has a strong comprehensive performance. At the same time, we implemented a joint training method, Co-HDNNtopss, and achieved a good performance to provide an important reference for similar hybrid-model training.

1700029I15Rik orchestrates the biosynthesis of acrosomal membrane proteins required for sperm-egg interaction

Sperm acrosomal membrane proteins, such as Izumo sperm-egg fusion 1 (IZUMO1) and sperm acrosome-associated 6 (SPACA6), play essential roles in mammalian gamete binding or fusion. How their biosynthesis is regulated during spermiogenesis has largely remained elusive. Here, we show that 1700029I15Rik knockout male mice are severely subfertile and their spermatozoa do not fuse with eggs. 1700029I15Rik is a type-II transmembrane protein expressed in early round spermatids but not in mature spermatozoa. It interacts with proteins involved in N-linked glycosylation, disulfide isomerization, and endoplasmic reticulum (ER)-Golgi trafficking, suggesting a potential role in nascent protein processing. The ablation of 1700029I15Rik destabilizes non-catalytic subunits of the oligosaccharyltransferase (OST) complex that are pivotal for N-glycosylation. The knockout testes exhibit normal expression of sperm plasma membrane proteins, but decreased abundance of multiple acrosomal membrane proteins involved in fertilization. The knockout sperm show upregulated chaperones related to ER-associated degradation (ERAD) and elevated protein ubiquitination; strikingly, SPACA6 becomes undetectable. Our results support for a specific, 1700029I15Rik-mediated pathway underpinning the biosynthesis of acrosomal membrane proteins during spermiogenesis.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC₅₀ values ranging from 0.04 to 6 μM), three OATP1B1 inhibitors (2.69 to 10 μM), and five OATP1B3 inhibitors (1.53 to 10 μM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC₅₀ values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC₅₀ = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Single amino acid residue mediates reciprocal specificity in two mosquito odorant receptors

The southern house mosquito, Culex quinquefasciatus, utilizes two odorant receptors, CquiOR10 and CquiOR2, narrowly tuned to oviposition attractants and well conserved among mosquito species. They detect skatole and indole, respectively, with reciprocal specificity. We swapped the transmembrane (TM) domains of CquiOR10 and CquiOR2 and identified TM2 as a specificity determinant. With additional mutations, we showed that CquiOR10A73L behaved like CquiOR2. Conversely, CquiOR2L74A recapitulated CquiOR10 specificity. Next, we generated structural models of CquiOR10 and CquiOR10A73L using RoseTTAFold and AlphaFold and docked skatole and indole using RosettaLigand. These modeling studies suggested space-filling constraints around A73. Consistent with this hypothesis, CquiOR10 mutants with a bulkier residue (Ile, Val) were insensitive to skatole and indole, whereas CquiOR10A73G retained the specificity to skatole and showed a more robust response than the wildtype receptor CquiOR10. On the other hand, Leu to Gly mutation of the indole receptor CquiOR2 reverted the specificity to skatole. Lastly, CquiOR10A73L, CquiOR2, and CquiOR2L74I were insensitive to 3-ethylindole, whereas CquiOR2L74A and CquiOR2L74G gained activity. Additionally, CquiOR10A73G gave more robust responses to 3-ethylindole than CquiOR10. Thus, we suggest the specificity of these receptors is mediated by a single amino acid substitution, leading to finely tuned volumetric space to accommodate specific oviposition attractants.

Predicting the Assembly of the Transmembrane Domains of Viral Channel Forming Proteins and Peptide Drug Screening Using a Docking Approach

A de novo assembly algorithm is provided to propose the assembly of bitopic transmembrane domains (TMDs) of membrane proteins. The algorithm is probed using, in particular, viral channel forming proteins (VCPs) such as M2 of influenza A virus, E protein of severe acute respiratory syndrome corona virus (SARS-CoV), 6K of Chikungunya virus (CHIKV), SH of human respiratory syncytial virus (hRSV), and Vpu of human immunodeficiency virus type 2 (HIV-2). The generation of the structures is based on screening a 7-dimensional space. Assembly of the TMDs can be achieved either by simultaneously docking the individual TMDs or via a sequential docking. Scoring based on estimated binding energies (EBEs) of the oligomeric structures is obtained by the tilt to decipher the handedness of the bundles. The bundles match especially well for all-atom models of M2 referring to an experimentally reported tetrameric bundle. Docking of helical poly-peptides to experimental structures of M2 and E protein identifies improving EBEs for positively charged (K,R,H) and aromatic amino acids (F,Y,W). Data are improved when using polypeptides for which the coordinates of the amino acids are adapted to the Cα coordinates of the respective experimentally derived structures of the TMDs of the target proteins.

Insights into the structure and function of the histidine kinase ComP from Bacillus amyloliquefaciens based on molecular modeling

The ComPA two-component signal transduction system (TCS) is essential in Bacillus spp. However, the molecular mechanism of the histidine kinase ComP remains unclear. Here, we predicted the structure of ComP from Bacillus amyloliquefaciens Q-426 (BaComP) using an artificial intelligence approach, analyzed the structural characteristics based on the molecular docking results and compared homologous proteins, and then investigated the biochemical properties of BaComP. We obtained a truncated ComPS protein with high purity and correct folding in solution based on the predicted structures. The expression and purification of BaComP proteins suggested that the subdomains in the cytoplasmic region influenced the expression and stability of the recombinant proteins. ComPS is a bifunctional enzyme that exhibits the activity of both histidine kinase and phosphotransferase. We found that His571 played an obligatory role in the autophosphorylation of BaComP based on the analysis of the structures and mutagenesis studies. The molecular docking results suggested that the HATPase_c domain contained an ATP-binding pocket, and the ATP molecule was coordinated by eight conserved residues from the N, G1, and G2 boxes. Our study provides novel insight into the histidine kinase BaComP and its homologous proteins.

Structural modeling of the hERG potassium channel and associated drug interactions

The voltage-gated potassium channel, KV11.1, encoded by the human Ether-à-go-go-Related Gene (hERG), is expressed in cardiac myocytes, where it is crucial for the membrane repolarization of the action potential. Gating of the hERG channel is characterized by rapid, voltage-dependent, C-type inactivation, which blocks ion conduction and is suggested to involve constriction of the selectivity filter. Mutations S620T and S641A/T within the selectivity filter region of hERG have been shown to alter the voltage dependence of channel inactivation. Because hERG channel blockade is implicated in drug-induced arrhythmias associated with both the open and inactivated states, we used Rosetta to simulate the effects of hERG S620T and S641A/T mutations to elucidate conformational changes associated with hERG channel inactivation and differences in drug binding between the two states. Rosetta modeling of the S641A fast-inactivating mutation revealed a lateral shift of the F627 side chain in the selectivity filter into the central channel axis along the ion conduction pathway and the formation of four lateral fenestrations in the pore. Rosetta modeling of the non-inactivating mutations S620T and S641T suggested a potential molecular mechanism preventing F627 side chain from shifting into the ion conduction pathway during the proposed inactivation process. Furthermore, we used Rosetta docking to explore the binding mechanism of highly selective and potent hERG blockers - dofetilide, terfenadine, and E4031. Our structural modeling correlates well with much, but not all, existing experimental evidence involving interactions of hERG blockers with key residues in hERG pore and reveals potential molecular mechanisms of ligand interactions with hERG in an inactivated state.

Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter

The Amino Acid-Polyamine-Organocation (APC) transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular γ-aminobutyric acid (GABA). Its structure, determined in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold with a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin transporter SERT and the glucose-sodium symporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follow the blueprint of these or other LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced modification of AlphaFold2 . Our experimental results reveal acid-induced conformational changes that dislodge the Cterminus from the permeation pathway coupled with rearrangement of helices that enables isomerization between inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminates the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different families in the LeuT-fold.

TMbed: transmembrane proteins predicted through language model embeddings

BACKGROUND

Despite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today's top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions.

RESULTS

Here, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94 ± 8% of the beta barrel TMPs (53 of 57) and 98 ± 1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060).

CONCLUSIONS

Based on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2.

Modeling of protein conformational changes with Rosetta guided by limited experimental data

Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated Cα-Cα distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.

Conformational selection guides β-arrestin recruitment at a biased G protein-coupled receptor

G protein-coupled receptors (GPCRs) recruit β-arrestins to coordinate diverse cellular processes, but the structural dynamics driving this process are poorly understood. Atypical chemokine receptors (ACKRs) are intrinsically biased GPCRs that engage β-arrestins but not G proteins, making them a model system for investigating the structural basis of β-arrestin recruitment. Here, we performed nuclear magnetic resonance (NMR) experiments on ¹³CH₃-ε-methionine-labeled ACKR3, revealing that β-arrestin recruitment is associated with conformational exchange at key regions of the extracellular ligand-binding pocket and intracellular β-arrestin-coupling region. NMR studies of ACKR3 mutants defective in β-arrestin recruitment identified an allosteric hub in the receptor core that coordinates transitions among heterogeneously populated and selected conformational states. Our data suggest that conformational selection guides β-arrestin recruitment by tuning receptor dynamics at intracellular and extracellular regions.

High-throughput strategy for identification of Mycobacterium tuberculosis membrane protein expression conditions using folding reporter GFP

Mycobacterium tuberculosis membrane protein biochemistry and structural biology studies are often hampered by challenges in protein expression and selection for well-expressing protein candidates, suitable for further investigation. Here we present a folding reporter GFP (frGFP) assay, adapted for M. tuberculosis membrane protein screening in Escherichia coli Rosetta 2 (DE3) and Mycobacterium smegmatis mc [2]4517. This method allows protein expression condition screening for multiple protein targets simultaneously by monitoring frGFP fluorescence in growing cells. We discuss the impact of common protein expression conditions on 42 essential M. tuberculosis H37Rv helical transmembrane proteins and establish the grounds for their further analysis. We have found that the basal expression of the lac operon in the T7-promoter expression system generally leads to high recombinant protein yield in M. smegmatis, and we suggest that a screening condition without the inducer is included in routine protein expression tests. In addition to the general observations, we describe conditions allowing high-level expression of more than 25 essential M. tuberculosis membrane proteins, containing 2 to 13 transmembrane helices. We hope that these findings will stimulate M. tuberculosis membrane protein research and aid the efforts in drug development against tuberculosis.

AlignMe: an update of the web server for alignment of membrane protein sequences

The AlignMe web server is dedicated to accurately aligning sequences of membrane proteins, a particularly challenging task due to the strong evolutionary divergence and the low compositional complexity of hydrophobic membrane-spanning proteins. AlignMe can create pairwise alignments of either two primary amino acid sequences or two hydropathy profiles. The web server for AlignMe has been continuously available for >10 years, supporting 1000s of users per year. Recent improvements include anchoring, multiple submissions, and structure visualization. Anchoring is the ability to constrain a position in an alignment, which allows expert information about related residues in proteins to be incorporated into an alignment without manual modification. The original web interface to the server limited the user to one alignment per submission, hindering larger scale studies. Now, batches of alignments can be initiated with a single submission. Finally, to provide structural context for the relationship between proteins, sequence similarity can now be mapped onto one or more structures (or structural models) of the proteins being aligned, by links to MutationExplorer, a web-based visualization tool. Together with a refreshed user interface, these features further enhance an important resource in the membrane protein community. The AlignMe web server is freely available at https://www.bioinfo.mpg.de/AlignMe/.

Structural Comparative Modeling of Multi-Domain F508del CFTR

Cystic fibrosis (CF) is a rare genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs-which increases the risk of infection and eventually causes death. CFTR is an ATP-binding cassette (ABC) transporter family protein composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (F508del), making F508del CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain F508del CFTR structure has been determined and few studies have modeled F508del using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a F508del model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and F508del CFTR structural models. These models demonstrate the destabilizing effects of F508del on NBD1 and the NBD1/TMD interface in both the inactive and active conformation of CFTR. Furthermore, we modeled F508del/R1070W and F508del bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of VX-809 on multi-domain models of F508del CFTR and pave the way for rational design of additional drugs that target F508del CFTR for treatment of CF.

Pathogenic variants in MDFIC cause recessive central conducting lymphatic anomaly with lymphedema

Central conducting lymphatic anomaly (CCLA), characterized by the dysfunction of core collecting lymphatic vessels including the thoracic duct and cisterna chyli, and presenting as chylothorax, pleural effusions, chylous ascites, and lymphedema, is a severe disorder often resulting in fetal or perinatal demise. Although pathogenic variants in RAS/mitogen activated protein kinase (MAPK) signaling pathway components have been documented in some patients with CCLA, the genetic etiology of the disorder remains uncharacterized in most cases. Here, we identified biallelic pathogenic variants in MDFIC, encoding the MyoD family inhibitor domain containing protein, in seven individuals with CCLA from six independent families. Clinical manifestations of affected fetuses and children included nonimmune hydrops fetalis (NIHF), pleural and pericardial effusions, and lymphedema. Generation of a mouse model of human MDFIC truncation variants revealed that homozygous mutant mice died perinatally exhibiting chylothorax. The lymphatic vasculature of homozygous Mdfic mutant mice was profoundly mispatterned and exhibited major defects in lymphatic vessel valve development. Mechanistically, we determined that MDFIC controls collective cell migration, an important early event during the formation of lymphatic vessel valves, by regulating integrin β₁ activation and the interaction between lymphatic endothelial cells and their surrounding extracellular matrix. Our work identifies MDFIC variants underlying human lymphatic disease and reveals a crucial, previously unrecognized role for MDFIC in the lymphatic vasculature. Ultimately, understanding the genetic and mechanistic basis of CCLA will facilitate the development and implementation of new therapeutic approaches to effectively treat this complex disease.

Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks

Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has been discovered for a long time, few ab initio machine learning-based prediction models have been reported, due to the limited amount of training data. In this study, we report a new deep learning-based prediction model, which is composed of a residual neural network and the uneven-thresholds decision algorithm. It is constructed on 121 membrane proteins, in total 51640 residue samples, which are curated from an up-to-date membrane protein structure database. Through a rigid 10-fold nested cross-validation experiment, we demonstrate that our model can achieve promising predictions and exceed current state-of-the-art approaches in this field. This presents a new avenue for accurately predicting AHs. Analysis on the contribution of the input residues and some cases further reveals the high interpretability and the generalization of our model.

Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins

One of the unique traits of membrane proteins is that a significant fraction of their hydrophobic amino acids is exposed to the hydrophobic core of lipid bilayers rather than being embedded in the protein interior, which is often not explicitly considered in the protein structure and function predictions. Here, we propose a characteristic and predictive quantity, the membrane contact probability (MCP), to describe the likelihood of the amino acids of a given sequence being in direct contact with the acyl chains of lipid molecules. We show that MCP is complementary to solvent accessibility in characterizing the outer surface of membrane proteins, and it can be predicted for any given sequence with a machine learning-based method by utilizing a training dataset extracted from MemProtMD, a database generated from molecular dynamics simulations for the membrane proteins with a known structure. As the first of many potential applications, we demonstrate that MCP can be used to systematically improve the prediction precision of the protein contact maps and structures.

The distribution of residues on protein surfaces is largely determined by the surrounding environment. For soluble proteins, most of the residues on the outer surface are hydrophilic, and people use the quantity “solvent accessibility” to describe and predict these surface residues. In contrast, for membrane proteins that are embedded in a lipid bilayer, many of their surface residues are hydrophobic and membrane-contacting, but there is yet a widely-accepted quantity for the description or prediction of this characteristic property. Here, we propose a new quantity termed “membrane contact probability (MCP)”, which can be used to describe and predict the membrane-contacting surface residues of proteins. We also propose a machine learning-based method to predict MCP from protein sequences, utilizing the dataset generated by physics-based computer simulations. We demonstrate that a quantity such as MCP is helpful for protein structure prediction, and we believe that it will find broad applications in the structure and function studies of membrane proteins.

Novel compound heterozygous variants in XYLT1 gene caused Desbuquois dysplasia type 2 in an aborted fetus: a case report

Background

Desbuquois dysplasia type 2 (DBQD2) is an infrequent dysplasia with a wide range of symptoms, including facial deformities, growth retardation and short long bones. It is an autosomal recessive disorder caused by mutations in the XYLT1 gene that encodes xylosyltransferase-1.

Case presentation

We studied an aborted fetus from Iranian non-consanguineous parents who was therapeutically aborted at 19 weeks of gestation. Ultrasound examinations at 18 weeks of gestation revealed growth retardation in her long bones and some facial problems. Whole-exome sequencing was performed on the aborted fetus which revealed compound heterozygous XYLT1 mutations: c.742G>A; p.(Glu248Lys) and c.1537 C>A; p.(Leu513Met). Sanger sequencing and segregation analysis confirmed the compound heterozygosity of these variants in XYLT1.

Conclusion

The c.1537 C>A; p.(Leu513Met) variant has not been reported in any databases so far and therefore is novel. This is the third compound heterozygote report in XYLT1 and further supports the high heterogeneity of this disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03132-5.

Mapping the binding sites of UDP and prostaglandin E2 glyceryl ester in the nucleotide receptor P2Y6

Cyclooxygenase‐2 catalyzes the biosynthesis of prostaglandins from arachidonic acid and the biosynthesis of prostaglandin glycerol esters (PG‐Gs) from 2‐arachidonoylglycerol. PG‐Gs are mediators of several biological actions such as macrophage activation, hyperalgesia, synaptic plasticity, and intraocular pressure. Recently, the human UDP receptor P2Y₆ was identified as a target for the prostaglandin E2 glycerol ester (PGE₂‐G). Here, we show that UDP and PGE₂‐G are evolutionary conserved endogenous agonists at vertebrate P2Y₆ orthologs. Using sequence comparison of P2Y₆ orthologs, homology modeling, and ligand docking studies, we proposed several receptor positions participating in agonist binding. Site‐directed mutagenesis and functional analysis of these P2Y₆ mutants revealed that both UDP and PGE₂‐G share in parts one ligand‐binding site. Thus, the convergent signaling of these two chemically very different agonists has already been manifested in the evolutionary design of the ligand‐binding pocket.

An Improved Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network

Alpha-helical proteins ( αTMPs) are essential in various biological processes. Despite their tertiary structures are crucial for revealing complex functions, experimental structure determination remains challenging and costly. In the past decades, various sequence-based topology prediction methods have been developed to bridge the gap between the sequences and structures by characterizing the structural features, but significant improvements are still required. Deep learning brings a great opportunity for its powerful representation learning capability from limited original data. In this work, we improved our αTMP topology prediction method DMCTOP using deep learning, which composed of two deep convolutional blocks to simultaneously extract local and global contextual features. Consequently, the inputs were simplified to reflect the original features of the sequence, including a protein sequence feature and an evolutionary conservation feature. DMCTOP can efficiently and accurately identify all topological types and the N-terminal orientation for an αTMP sequence. To validate the effectiveness of our method, we benchmarked DMCTOP against 13 peer methods according to the whole sequence, the transmembrane segment and the traditional criterion in testing experiments. All the results reveal that our method achieved the highest prediction accuracy and outperformed all the previous methods. The method is available at https://icdtools.nenu.edu.cn/dmctop.

Molecular basis for the evolved instability of a human G-protein coupled receptor

Membrane proteins are prone to misfolding and degradation. This is particularly true for mammalian forms of the gonadotropin-releasing hormone receptor (GnRHR). Although they function at the plasma membrane, mammalian GnRHRs accumulate within the secretory pathway. Their apparent instability is believed to have evolved through selection for attenuated GnRHR activity. Nevertheless, the molecular basis of this adaptation remains unclear. We show that adaptation coincides with a C-terminal truncation that compromises the translocon-mediated membrane integration of its seventh transmembrane domain (TM7). We also identify a series of polar residues in mammalian GnRHRs that compromise the membrane integration of TM2 and TM6. Reverting a lipid-exposed polar residue in TM6 to an ancestral hydrophobic residue restores expression with no impact on function. Evolutionary trends suggest variations in the polarity of this residue track with reproductive phenotypes. Our findings suggest that the marginal energetics of cotranslational folding can be exploited to tune membrane protein fitness.

Integral membrane proteins are prone to misfolding, especially mammalian gonadotropin-releasing hormone receptors (GnRHRs). Chamness et al. show that the evolved instability of mammalian GnRHRs stems from adaptive modifications that disrupt translocon-mediated membrane integration, suggesting that membrane protein misfolding can be exploited to tune fitness.

TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks

Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Prediction of (per-residue) features such as transmembrane topology, membrane exposure, secondary structure, and solvent accessibility can be a useful starting point for experimental design or protein structure prediction but often requires different computational tools for different features or types of proteins. We present TopProperty, a metapredictor that predicts all of these features for TMPs or globular proteins. TopProperty is trained on datasets without bias toward a high number of sequence homologs, and the predictions are significantly better than the evaluated state-of-the-art primary predictors on all quality metrics. TopProperty eliminates the need for protein type- or feature-tailored tools, specifically for TMPs. TopProperty is freely available as a web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/.

Hidden neural networks for transmembrane protein topology prediction

Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.

Topological analyses of the L-lysine exporter LysO reveal a critical role for a conserved pair of intramembrane solvent-exposed acidic residues

LysO, a prototypical member of the LysO family, mediates export of L-lysine (Lys) and resistance to the toxic Lys antimetabolite, L-thialysine (Thl) in Escherichia coli. Here, we have addressed unknown aspects of LysO function pertaining to its membrane topology and the mechanism by which it mediates Lys/Thl export. Using substituted cysteine (Cys) accessibility, here we delineated the membrane topology of LysO. Our studies support a model in which both the N- and C-termini of LysO are present at the periplasmic face of the membrane with a transmembrane (TM) domain comprising eight TM segments (TMSs) between them. In addition, a feature of intramembrane solvent exposure in LysO is inferred with the identification of membrane-located solvent-exposed Cys residues. Isosteric substitutions of a pair of conserved acidic residues, one E233, located in the solvent-exposed TMS7 and the other D261, in a solvent-exposed intramembrane segment located between TMS7 and TMS8, abolished LysO function in vivo. Thl, but not Lys, elicited proton release in inside-out membrane vesicles, a process requiring the presence of both E233 and D261. We postulate that Thl may be exported in antiport with H+ and that Lys may be a low-affinity export substrate. Our findings are compatible with a physiological scenario wherein in vivo LysO exports the naturally occurring antimetabolite Thl with higher affinity over the essential cellular metabolite Lys, thus affording protection from Thl toxicity and limiting wasteful export of Lys.

Tmc proteins are essential for zebrafish hearing where Tmc1 is not obligatory

Perception of sound is initiated by mechanically gated ion channels at the tips of stereocilia. Mature mammalian auditory hair cells require transmembrane channel-like 1 (TMC1) for mechanotransduction, and mutations of the cognate genetic sequences result in dominant or recessive heritable deafness forms in humans and mice. In contrast, zebrafish lateral line hair cells, which detect water motion, require Tmc2a and Tmc2b. Here, we use standard and multiplex genome editing in conjunction with functional and behavioral assays to determine the reliance of zebrafish hearing and vestibular organs on Tmc proteins. Surprisingly, our approach using multiple mutant alleles demonstrates that hearing in zebrafish is not dependent on Tmc1, nor is it fully dependent on Tmc2a and Tmc2b. Hearing however is absent in triple-mutant zebrafish that lack Tmc1, Tmc2a and Tmc2b. These outcomes reveal a striking resemblance of Tmc protein reliance in the vestibular sensory epithelia of mammals to the maculae of zebrafish. Moreover, our findings disclose a logic of Tmc use where hearing depends on a complement of Tmc proteins beyond those employed to sense water motion.

HutT functions as the major L-histidine transporter in Pseudomonas putida KT2440

Histidine is an important carbon and nitrogen source of γ-proteobacteria and can affect bacteria-host interactions. The mechanisms of histidine uptake are only partly understood. Here, we analyze functional properties of the putative histidine transporter HutT of the soil bacterium Pseudomonas putida. The hutT gene is part of the histidine utilization operon, and the gene product belongs to the amino acid-polyamine-organocation (APC) family of secondary transporters. Deletion of hutT severely impairs growth of P. putida on histidine, suggesting that the encoded transporter is the major histidine uptake system of P. putida. Transport experiments with cells and purified and reconstituted protein indicate that HutT functions as a high-affinity histidine : proton symporter with high specificity for the amino acid. Substitution analyses identified amino acids crucial for HutT function.

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework. Our comparison shows that MASSP performs equally well or better than the state-of-the-art methods in predicting residue-level secondary structures, boundaries of transmembrane segments, and topology. Furthermore, it achieves outstanding accuracy in predicting protein-level structural classes. MASSP automatically distinguishes the structural classes of input sequences and identifies transmembrane segments and topologies if present, making it broadly applicable to different classes of proteins. In summary, MASSP's good performance and broad applicability make it well suited for annotating residue-level attributes and protein-level structural classes at the proteome scale.

Determination of the membrane topology of PORCN, an O-acyl transferase that modifies Wnt signalling proteins

Wnt gradients elicit distinct cellular responses, such as proliferation, specification, differentiation and survival in a dose-dependent manner. Porcupine (PORCN), a membrane-bound O-acyl transferase (MBOAT) that resides in the endoplasmic reticulum, catalyses the addition of monounsaturated palmitate to Wnt proteins and is required for Wnt gradient formation and signalling. In humans, PORCN mutations are causal for focal dermal hypoplasia (FDH), an X-linked dominant syndrome characterized by defects in mesodermal and endodermal tissues. PORCN is also an emerging target for cancer therapeutics. Despite the importance of this enzyme, its structure remains poorly understood. Recently, the crystal structure of DltB, an MBOAT family member from bacteria, was solved. In this report, we use experimental data along with homology modelling to DltB to determine the membrane topology of PORCN. Our studies reveal that PORCN has 11 membrane domains, comprising nine transmembrane spanning domains and two reentrant domains. The N-terminus is oriented towards the lumen while the C-terminus is oriented towards the cytosol. Like DltB, PORCN has a funnel-like structure that is encapsulated by multiple membrane-spanning helices. This new model for PORCN topology allows us to map residues that are important for biological activity (and implicated in FDH) onto its three-dimensional structure.

Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors

The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly matched by a standard pairwise sequence alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the aligned regions either side of the newly matched positions. Previous studies have introduced restraints as a means to impose the matching of positions during sequence alignments, originally in the context of genome assembly. Here we introduce position restraints, or "anchors" as a feature in our alignment tool AlignMe, providing an aid to pairwise global sequence alignment of alpha-helical membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of anchors can be used to modify alignments, while still maintaining the reproducibility and rigor of the rest of the alignment. Anchored alignments can be generated using the online version of AlignMe available at www.bioinfo.mpg.de/AlignMe/.

Structural Perspective on Ancient Neuropeptide Y-like System reveals Hallmark Features for Peptide Recognition and Receptor Activation

The neuropeptide Y (NPY) family is a peptide-activated G protein-coupled receptor system conserved across all bilaterians, and is involved in food intake, learning, and behavior. We hypothesized that comparing the NPY system in evolutionarily ancient organisms can reveal structural determinants of peptide recognition and receptor activation conserved in evolution. To test this hypothesis, we investigated the homologous FLP/NPR system of the protostome C.elegans. For three prototypic peptide-receptor complexes representing different ligand types, we integrate extensive functional data into structural models of the receptors. Common features include acidic patches in the extracellular loops (ECLs) of the receptors that cooperatively 'draw' the peptide into the binding pocket, which was functionally validated in vivo. A structurally conserved glutamate in the ECL2 anchors the peptides by a conserved salt bridge to the arginine of the RFamide motif. Beyond this conserved interaction, peptide binding show variability enabled by receptor-specific interactions. The family-conserved residue Q^3.32 is a key player for peptide binding and receptor activation. Altered interaction patterns at Q^3.32 may drastically increase the efficacy to activate the receptor.

Identification and classification of innexin gene transcripts in the central nervous system of the terrestrial slug Limax valentianus

Intercellular gap junction channels and single-membrane channels have been reported to regulate electrical synapse and the brain function. Innexin is known as a gap junction-related protein in invertebrates and is involved in the formation of intercellular gap junction channels and single-cell membrane channels. Multiple isoforms of innexin protein in each species enable the precise regulation of channel function. In molluscan species, sequence information of innexins is still limited and the sequences of multiple innexin isoforms have not been classified. This study examined the innexin transcripts expressed in the central nervous system of the terrestrial slug Limax valentianus and identified 16 transcripts of 12 innexin isoforms, including the splicing variants. We performed phylogenetic analysis and classified the isoforms with other molluscan innexin sequences. Next, the phosphorylation, N-glycosylation, and S-nitrosylation sites were predicted to characterize the innexin isoforms. Further, we identified 16 circular RNA sequences of nine innexin isoforms in the central nervous system of Limax. The identification and classification of molluscan innexin isoforms provided novel insights for understanding the regulatory mechanism of innexin in this phylum.

TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

Alpha transmembrane proteins (αTMPs) profoundly affect many critical biological processes and are major drug targets due to their pivotal protein functions. At present, even though the non-transmembrane secondary structures are highly relevant to the biological functions of αTMPs along with their transmembrane structures, they have not been unified to be studied yet. In this study, we present a novel computational method, TMPSS, to predict the secondary structures in non-transmembrane parts and the topology structures in transmembrane parts of αTMPs. TMPSS applied a Convolutional Neural Network (CNN), combined with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network, to extract the local contexts and long-distance interdependencies from primary sequences. In addition, a multi-task learning strategy was used to predict the secondary structures and the transmembrane helixes. TMPSS was thoroughly trained and tested against a non-redundant independent dataset, where the Q3 secondary structure prediction accuracy achieved 78% in the non-transmembrane region, and the accuracy of the transmembrane region prediction achieved 90%. In sum, our method showcased a unified model for predicting the secondary structure and topology structure of αTMPs by only utilizing features generated from primary sequences and provided a steady and fast prediction, which promisingly improves the structural studies on αTMPs.

Prediction of Transmembrane Regions, Cholesterol, and Ganglioside Binding Sites in Amyloid-Forming Proteins Indicate Potential for Amyloid Pore Formation

Besides amyloid fibrils, amyloid pores (APs) represent another mechanism of amyloid induced toxicity. Since hypothesis put forward by Arispe and collegues in 1993 that amyloid-beta makes ion-conducting channels and that Alzheimer's disease may be due to the toxic effect of these channels, many studies have confirmed that APs are formed by prefibrillar oligomers of amyloidogenic proteins and are a common source of cytotoxicity. The mechanism of pore formation is still not well-understood and the structure and imaging of APs in living cells remains an open issue. To get closer to understand AP formation we used predictive methods to assess the propensity of a set of 30 amyloid-forming proteins (AFPs) to form transmembrane channels. A range of amino-acid sequence tools were applied to predict AP domains of AFPs, and provided context on future experiments that are needed in order to contribute toward a deeper understanding of amyloid toxicity. In a set of 30 AFPs we predicted their amyloidogenic propensity, presence of transmembrane (TM) regions, and cholesterol (CBM) and ganglioside binding motifs (GBM), to which the oligomers likely bind. Noteworthy, all pathological AFPs share the presence of TM, CBM, and GBM regions, whereas the functional amyloids seem to show just one of these regions. For comparative purposes, we also analyzed a few examples of amyloid proteins that behave as biologically non-relevant AFPs. Based on the known experimental data on the β-amyloid and α-synuclein pore formation, we suggest that many AFPs have the potential for pore formation. Oligomerization and α-TM helix to β-TM strands transition on lipid rafts seem to be the common key events.

Integrative approach for detecting membrane proteins

Background

Membrane proteins are key gates that control various vital cellular functions. Membrane proteins are often detected using transmembrane topology prediction tools. While transmembrane topology prediction tools can detect integral membrane proteins, they do not address surface-bound proteins. In this study, we focused on finding the best techniques for distinguishing all types of membrane proteins.

Results

This research first demonstrates the shortcomings of merely using transmembrane topology prediction tools to detect all types of membrane proteins. Then, the performance of various feature extraction techniques in combination with different machine learning algorithms was explored. The experimental results obtained by cross-validation and independent testing suggest that applying an integrative approach that combines the results of transmembrane topology prediction and position-specific scoring matrix (Pse-PSSM) optimized evidence-theoretic k nearest neighbor (OET-KNN) predictors yields the best performance.

Conclusion

The integrative approach outperforms the state-of-the-art methods in terms of accuracy and MCC, where the accuracy reached a 92.51% in independent testing, compared to the 89.53% and 79.42% accuracies achieved by the state-of-the-art methods.

Unconventional Secretion of Nigerolysins A from Aspergillus Species

Aegerolysins are small lipid-binding proteins particularly abundant in fungi. Aegerolysins from oyster mushrooms interact with an insect-specific membrane lipid and, together with MACPF proteins produced by the same organism, form pesticidal pore-forming complexes. The specific interaction with the same membrane lipid was recently demonstrated for nigerolysin A2 (NigA2), an aegerolysin from Aspergillus niger. In Aspergillus species, the aegerolysins were frequently found as secreted proteins, indicating their function in fungal defense. Using immunocytochemistry and live-cell imaging we investigated the subcellular localization of the nigerolysins A in A. niger, while their secretion was addressed by secretion prediction and Western blotting. We show that both nigerolysins A are leaderless proteins that reach the cell exterior by an unconventional protein secretion. NigA proteins are evenly distributed in the cytoplasm of fungal hyphae. A detailed bioinformatics analysis of Aspergillus aegerolysins suggests that the same function occurs only in a limited number of aegerolysins. From alignment, analysis of chromosomal loci, orthology, synteny, and phylogeny it follows that the same or a similar function described for pairs of pesticidal proteins of Pleurotus sp. can be expected in species of the subgenus Circumdati, section Nigri, series Nigri, and some other species with adjacent pairs of putative pesticidal proteins.