Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.     

SARS‐CoV‐2 structural coverage map reveals viral protein assembly,mimicry, and hijacking mechanisms

We modeled 3D structures of all SARS‐CoV‐2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post‐translational modifications, block host translation, and disable host defenses; a further ˜29% self‐assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is—and is not—known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria‐COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark.     

PDBsum extras: SARS‐CoV‐2 and AlphaFold models

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS‐CoV‐2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.     

A hierarchical deep learning based approach for multi‐functional enzyme classification

Enzymes are critical proteins in every organism. They speed up essential chemical reactions, help fight diseases, and have a wide use in the pharmaceutical and manufacturing industries. Wet lab experiments to figure out an enzyme''s function are time consuming and expensive. Therefore, the need for computational approaches to address this problem are becoming necessary. Usually, an enzyme is extremely specific in performing its function. However, there exist enzymes that can perform multiple functions. A multi‐functional enzyme has vast potential as it reduces the need to discover/use different enzymes for different functions. We propose an approach to predict a multi‐functional enzyme''s function up to the most specific fourth level of the hierarchy of the Enzyme Commission (EC) number. Previous studies can only predict the function of the enzyme till level 1. Using a dataset of 2,583 multi‐functional enzymes, we achieved a hierarchical subset accuracy of 71.4% and a Macro F₁ Score of 96.1% at the fourth level. The robustness of the network was further tested on a multi‐functional isoforms dataset. Our method is broadly applicable and may be used to discover better enzymes. The web‐server can be freely accessed at http://hecnet.cbrlab.org/.     

Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP     

FastD: Fast detection of insecticide target‐site mutations and overexpressed detoxification genes in insect populations from RNA‐Seq data

Target‐site mutations and detoxification gene overexpression are two major mechanisms conferring insecticide resistance. Molecular assays applied to detect these resistance genetic markers are time‐consuming and with high false‐positive rates. RNA‐Seq data contains information on the variations within expressed genomic regions and expression of detoxification genes. However, there is no corresponding method to detect resistance markers at present. Here, we collected 66 reported resistance mutations of four insecticide targets (AChE, VGSC, RyR, and nAChR) from 82 insect species. Next, we obtained 403 sequences of the four target genes and 12,665 sequences of three kinds of detoxification genes including P450s, GSTs, and CCEs. Then, we developed a Perl program, FastD, to detect target‐site mutations and overexpressed detoxification genes from RNA‐Seq data and constructed a web server for FastD (http://www.insect-genome.com/fastd). The estimation of FastD on simulated RNA‐Seq data showed high sensitivity and specificity. We applied FastD to detect resistant markers in 15 populations of six insects, Plutella xylostella, Aphis gossypii, Anopheles arabiensis, Musca domestica, Leptinotarsa decemlineata and Apis mellifera. Results showed that 11 RyR mutations in P. xylostella, one nAChR mutation in A. gossypii, one VGSC mutation in A. arabiensis and five VGSC mutations in M. domestica were found to be with frequency difference >40% between resistant and susceptible populations including previously confirmed mutations G4946E in RyR, R81T in nAChR and L1014F in VGSC. And 49 detoxification genes were found to be overexpressed in resistant populations compared with susceptible populations including previously confirmed detoxification genes CYP6BG1, CYP6CY22, CYP6CY13, CYP6P3, CYP6M2, CYP6P4 and CYP4G16. The candidate target‐site mutations and detoxification genes were worth further validation. Resistance estimates according to confirmed markers were consistent with population phenotypes, confirming the reliability of this program in predicting population resistance at omics‐level.     

B‐cell capacity for differentiation changes with age

BackgroundAge‐related immune deficiencies are thought to be responsible for increased susceptibility to infection in older adults, with alterations in lymphocyte populations becoming more prevalent over time. The loss of humoral immunity in ageing was attributed to the diminished numbers of B cells and the reduced ability to generate immunoglobulin.AimsTo compare the intrinsic B‐cell capacity for differentiation into mature plasma cells (PCs), between young and old donors, using in vitro assays, providing either effective T‐cell help or activation via TLR engagement.MethodsB cells were isolated from healthy individuals, in younger (30–38 years) and older (60–64 years) donors. An in vitro model system of B‐cell differentiation was used, analysing 5 differentiation markers by flow cytometry, under T‐dependent (TD: CD40/BCR stimulation) or T‐independent (TI: TLR7/BCR activation) conditions. Antibody secretion was measured by ELISA and gene expression using qPCR.ResultsTI and TD differentiation resulted in effective proliferation of B cells followed by their differentiation into PC. B‐cell‐executed TI differentiation was faster, all differentiation marker and genes being expressed earlier than under TD differentiation (day 6), although generating less viable cells and lower antibody levels (day 13). Age‐related differences in B‐cell capacity for differentiation were minimal in TD differentiation. In contrast, in TI differentiation age significantly affected proliferation, viability, differentiation, antibody secretion and gene expression, older donors being more efficient.ConclusionAltogether, B‐cell differentiation into PC appeared similar between age groups when provided with T‐cell help, in contrast to TI differentiation, where multiple age‐related changes suggest better capacities in older donors. These new findings may help explain the emergence of autoantibodies in ageing.     

Construction of miRNA‐lncRNA‐mRNA co‐expression network affecting EMT‐mediated cisplatin resistance in ovarian cancer

Platinum resistance is one of the major concerns in ovarian cancer treatment. Recent evidence shows the critical role of epithelial–mesenchymal transition (EMT) in this resistance. Epithelial‐like ovarian cancer cells show decreased sensitivity to cisplatin after cisplatin treatment. Our study prospected the association between epithelial phenotype and response to cisplatin in ovarian cancer. Microarray dataset {"type":"entrez-geo","attrs":{"text":"GSE47856","term_id":"47856"}}GSE47856 was acquired from the GEO database. After identifying differentially expressed genes (DEGs) between epithelial‐like and mesenchymal‐like cells, the module identification analysis was performed using weighted gene co‐expression network analysis (WGCNA). The gene ontology (GO) and pathway analyses of the most considerable modules were performed. The protein–protein interaction network was also constructed. The hub genes were specified using Cytoscape plugins MCODE and cytoHubba, followed by the survival analysis and data validation. Finally, the co‐expression of miRNA‐lncRNA‐TF with the hub genes was reconstructed. The co‐expression network analysis suggests 20 modules relating to the Epithelial phenotype. The antiquewhite4, brown and darkmagenta modules are the most significant non‐preserved modules in the Epithelial phenotype and contain the most differentially expressed genes. GO, and KEGG pathway enrichment analyses on these modules divulge that these genes were primarily enriched in the focal adhesion, DNA replication pathways and stress response processes. ROC curve and overall survival rate analysis show that the co‐expression pattern of the brown module''s hub genes could be a potential prognostic biomarker for ovarian cancer cisplatin resistance.     

RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D

Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open‐access global archive housing three‐dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB‐designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro‐electron diffraction). The RCSB PDB research‐focused web portal (RCSB.org) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal (PDB101.RCSB.org) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB‐designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org; and (v) supports training, outreach, and education via PDB101.RCSB.org. New tools and features at RCSB.org are presented using examples drawn from high‐resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.     

Engineering defensin α‐helix to produce high‐affinity SARS‐CoV‐2 spike protein binding ligands

The binding of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) spike protein to the angiotensin‐converting enzyme 2 (ACE2) receptor expressed on the host cells is a critical initial step for viral infection. This interaction is blocked through competitive inhibition by soluble ACE2 protein. Therefore, developing high‐affinity and cost‐effective ACE2 mimetic ligands that disrupt this protein–protein interaction is a promising strategy for viral diagnostics and therapy. We employed human and plant defensins, a class of small (2–5 kDa) and highly stable proteins containing solvent‐exposed alpha‐helix, conformationally constrained by two disulfide bonds. Therefore, we engineered the amino acid residues on the constrained alpha‐helix of defensins to mimic the critical residues on the ACE2 helix 1 that interact with the SARS‐CoV‐2 spike protein. The engineered proteins (h‐deface2, p‐deface2, and p‐deface2‐MUT) were soluble and purified to homogeneity with a high yield from a bacterial expression system. The proteins demonstrated exceptional thermostability (Tm 70.7°C), high‐affinity binding to the spike protein with apparent K _d values of 54.4 ± 11.3, 33.5 ± 8.2, and 14.4 ± 3.5 nM for h‐deface2, p‐deface2, and p‐deface2‐MUT, respectively, and were used in a diagnostic assay that detected SARS‐CoV‐2 neutralizing antibodies. This work addresses the challenge of developing helical ACE2 mimetics by demonstrating that defensins provide promising scaffolds to engineer alpha‐helices in a constrained form for designing of high‐affinity ligands.     

HIV‐1 infection of CD4 T cells impairs antigen‐specific B cell function

Failures to produce neutralizing antibodies upon HIV‐1 infection result in part from B‐cell dysfunction due to unspecific B‐cell activation. How HIV‐1 affects antigen‐specific B‐cell functions remains elusive. Using an adoptive transfer mouse model and ex vivo HIV infection of human tonsil tissue, we found that expression of the HIV‐1 pathogenesis factor NEF in CD4 T cells undermines their helper function and impairs cognate B‐cell functions including mounting of efficient specific IgG responses. NEF interfered with T cell help via a specific protein interaction motif that prevents polarized cytokine secretion at the T‐cell–B‐cell immune synapse. This interference reduced B‐cell activation and proliferation and thus disrupted germinal center formation and affinity maturation. These results identify NEF as a key component for HIV‐mediated dysfunction of antigen‐specific B cells. Therapeutic targeting of the identified molecular surface in NEF will facilitate host control of HIV infection.     

Ascochyta rabiei: A threat to global chickpea production

The necrotrophic fungus Ascochyta rabiei causes Ascochyta blight (AB) disease in chickpea. A. rabiei infects all aerial parts of the plant, which results in severe yield loss. At present, AB disease occurs in most chickpea‐growing countries. Globally increased incidences of A. rabiei infection and the emergence of new aggressive isolates directed the interest of researchers toward understanding the evolution of pathogenic determinants in this fungus. In this review, we summarize the molecular and genetic studies of the pathogen along with approaches that are helping in combating the disease. Possible areas of future research are also suggested.Taxonomykingdom Mycota, phylum Ascomycota, class Dothideomycetes, subclass Coelomycetes, order Pleosporales, family Didymellaceae, genus Ascochyta, species rabiei. Primary host A. rabiei survives primarily on Cicer species.Disease symptoms A. rabiei infects aboveground parts of the plant including leaves, petioles, stems, pods, and seeds. The disease symptoms first appear as watersoaked lesions on the leaves and stems, which turn brown or dark brown. Early symptoms include small circular necrotic lesions visible on the leaves and oval brown lesions on the stem. At later stages of infection, the lesions may girdle the stem and the region above the girdle falls off. The disease severity increases at the reproductive stage and rounded lesions with concentric rings, due to asexual structures called pycnidia, appear on leaves, stems, and pods. The infected pod becomes blighted and often results in shrivelled and infected seeds.Disease management strategiesCrop failures may be avoided by judicious practices of integrated disease management based on the use of resistant or tolerant cultivars and growing chickpea in areas where conditions are least favourable for AB disease development. Use of healthy seeds free of A. rabiei, seed treatments with fungicides, and proper destruction of diseased stubbles can also reduce the fungal inoculum load. Crop rotation with nonhost crops is critical for controlling the disease. Planting moderately resistant cultivars and prudent application of fungicides is also a way to combat AB disease. However, the scarcity of AB‐resistant accessions and the continuous evolution of the pathogen challenges the disease management process.Useful websites https://www.ndsu.edu/pubweb/pulse‐info/resourcespdf/Ascochyta%20blight%20of%20chickpea.pdf https://saskpulse.com/files/newsletters/180531_ascochyta_in_chickpeas‐compressed.pdf http://www.pulseaus.com.au/growing‐pulses/bmp/chickpea/ascochyta‐blight http://agriculture.vic.gov.au/agriculture/pests‐diseases‐and‐weeds/plant‐diseases/grains‐pulses‐and‐cereals/ascochyta‐blight‐of‐chickpea http://www.croppro.com.au/crop_disease_manual/ch05s02.php https://www.northernpulse.com/uploads/resources/722/handout‐chickpeaascochyta‐nov13‐2011.pdf http://oar.icrisat.org/184/1/24_2010_IB_no_82_Host_Plant https://www.crop.bayer.com.au/find‐crop‐solutions/by‐pest/diseases/ascochyta‐blight     

DichroWeb,a website for calculating protein secondary structure from circular dichroism spectroscopic data

Circular dichroism (CD) spectroscopy is a widely‐used method for characterizing the secondary structures of proteins. The well‐established and highly used analysis website, DichroWeb (located at: http://dichroweb.cryst.bbk.ac.uk/html/home.shtml) enables the facile quantitative determination of helix, sheet, and other secondary structure contents of proteins based on their CD spectra. DichroWeb includes a range of reference datasets and algorithms, plus graphical and quantitative methods for determining the quality of the analyses produced. This article describes the current website content, usage and accessibility, as well as the many upgraded features now present in this highly popular tool that was originally created nearly two decades ago.     

NMR hawk‐eyed view of AlphaFold2 structures

The prediction of the three‐dimensional (3D) structure of proteins from the amino acid sequence made a stunning breakthrough reaching atomic accuracy. Using the neural network‐based method AlphaFold2, 3D structures of almost the entire human proteome have been predicted and made available (https://www.alphafold.ebi.ac.uk). To gain insight into how well AlphaFold2 structures represent the conformation of proteins in solution, I here compare the AlphaFold2 structures of selected small proteins with their 3D structures that were determined by nuclear magnetic resonance (NMR) spectroscopy. Proteins were selected for which the 3D solution structures were determined on the basis of a very large number of distance restraints and residual dipolar couplings and are thus some of the best‐resolved solution structures of proteins to date. The quality of the backbone conformation of the AlphaFold2 structures is assessed by fitting a large set of experimental residual dipolar couplings (RDCs). The analysis shows that experimental RDCs fit extremely well to the AlphaFold2 structures predicted for GB3, DinI, and ubiquitin. In the case of GB3, the accuracy of the AlphaFold2 structure even surpasses that of a 1.1 Å crystal structure. Fitting of experimental RDCs furthermore allows identification of AlphaFold2 structures that are best representative of the protein''s conformation in solution as seen for the EF hands of the N‐terminal domain of Ca²⁺‐ligated calmodulin. Taken together, the analysis shows that structures predicted by AlphaFold2 can be highly representative of the solution conformation of proteins. The combination of AlphaFold2 structures with RDCs promises to be a powerful approach to study structural changes in proteins.     

PDBsum1 : A standalone program for generating PDBsum analyses

PDBsum1 is a standalone set of programs to perform the same structural analyses as provided by the PDBsum web server (https://www.ebi.ac.uk/pdbsum). The server has pages for every entry in the Protein Data Bank (PDB) and can also process user‐uploaded PDB files, returning a password‐protected set of pages that are retained for around 3 months. The standalone version described here allows for in‐house processing and indefinite retention of the results. All data files and images are pre‐generated, rather than on‐the‐fly as in the web version, so can be easily accessed. The program runs on Linux, Windows, and mac operating systems and is freely available for academic use at https://www.ebi.ac.uk/thornton-srv/software/PDBsum1.     

Disease‐linked TDP‐43 hyperphosphorylation suppresses TDP‐43 condensation and aggregation

Post‐translational modifications (PTMs) have emerged as key modulators of protein phase separation and have been linked to protein aggregation in neurodegenerative disorders. The major aggregating protein in amyotrophic lateral sclerosis and frontotemporal dementia, the RNA‐binding protein TAR DNA‐binding protein (TDP‐43), is hyperphosphorylated in disease on several C‐terminal serine residues, a process generally believed to promote TDP‐43 aggregation. Here, we however find that Casein kinase 1δ‐mediated TDP‐43 hyperphosphorylation or C‐terminal phosphomimetic mutations reduce TDP‐43 phase separation and aggregation, and instead render TDP‐43 condensates more liquid‐like and dynamic. Multi‐scale molecular dynamics simulations reveal reduced homotypic interactions of TDP‐43 low‐complexity domains through enhanced solvation of phosphomimetic residues. Cellular experiments show that phosphomimetic substitutions do not affect nuclear import or RNA regulatory functions of TDP‐43, but suppress accumulation of TDP‐43 in membrane‐less organelles and promote its solubility in neurons. We speculate that TDP‐43 hyperphosphorylation may be a protective cellular response to counteract TDP‐43 aggregation.     

Molecular basis of cross‐species ACE2 interactions with SARS‐CoV‐2‐like viruses of pangolin origin

Correction to: The EMBO Journal (2021) 40: e107786. DOI 10.15252/embj.2021107786 | Published online 8 June 2021The authors would like to add three references to the paper: Starr et al and Zahradník et al also reported that the Q498H or Q498R mutation has enhanced binding affinity to ACE2; and Liu et al reported on the binding of bat coronavirus to ACE2.Starr et al and Zahradník et al have now been cited in the Discussion section, and the following sentence has been corrected from:“According to our data, the SARS‐CoV‐2 RBD with Q498H increases the binding strength to hACE2 by 5‐fold, suggesting the Q498H mutant is more ready to interact with human receptor than the wildtype and highlighting the necessity for more strict control of virus and virus‐infected animals”.to“Here, according to our data and two recently published papers, the SARS‐CoV‐2 RBD with Q498H or Q498R increases the binding strength to hACE2 (Starr et al, 2020; Zahradník et al, 2021), suggesting the mutant with Q498H or Q498R is more ready to interact with human receptor than the wild type and highlighting the necessity for more strict control of virus and virus‐infected animals”.The Liu et al citation has been added to the following sentence:“In another paper published by our group recently, RaTG13 RBD was found to bind to hACE2 with much lower binding affinity than SARS‐CoV‐2 though RaTG13 displays the highest whole‐genome sequence identity (96.2%) with the SARS‐CoV‐2 (Liu et al, 2021)”.Additionally, the authors have added the GISAID accession IDs to the sequence names of the SARS‐CoV‐2 in two human samples (Discussion section). To make identification unambiguous, the sequence names have been updated from “SA‐lsf‐27 and SA‐lsf‐37” to “GISAID accession ID: EPI_ISL_672581 and EPI_ISL_672589”.Lastly, the authors declare in the Materials and Methods section that all experiments employed SARS‐CoV‐2 pseudovirus in cultured cells. These experiments were performed in a BSL‐2‐level laboratory and approved by Science and Technology Conditions Platform Office, Institute of Microbiology, Chinese Academy of Sciences.These changes are herewith incorporated into the paper.