N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding

Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures

Posttranslational modifications (PTMs) play a crucial role in modulating the structure, function, localization, and interactions of proteins, with many PTMs being localized within supersecondary structures, such as helical pairs. These modifications can significantly influence the conformation and stability of these structures. For instance, phosphorylation introduces negative charges that alter electrostatic interactions, while acetylation or methylation of lysine residues affects the stability and interactions of alpha helices or beta strands. Given the pivotal role of supersecondary structures in the overall protein architecture, their modulation by PTMs is essential for protein functionality. This chapter explores the latest advancements in predicting sites for the five PTMs (phosphorylation, acetylation, glycosylation, methylation, and ubiquitination) known to be localized within supersecondary structures. The chapter highlights the recent advances in the prediction of these PTM sites, including the use of global contextualized embeddings from protein language models, integration of structural information, utilization of reliable positive and negative sites, and application of contrastive learning. These methodologies and emerging trends offer a roadmap for novel innovations in addressing PTM prediction challenges, particularly those linked to supersecondary structures.

Sitetack: a deep learning model that improves PTM prediction by using known PTMs

MOTIVATION

Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success.

RESULTS

We evaluated the use of known PTM sites in prediction via sequence-based deep learning algorithms. For each PTM, known locations of that PTM were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of that PTM at a given site. Without labeling known PTMs, our models are on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms.

AVAILABILITY AND IMPLEMENTATION

Sitetack is available as a web tool at https://sitetack.net; the source code, representative datasets, instructions for local use, and select models are available at https://github.com/clair-gutierrez/sitetack.

Positions of cysteine residues reveal local clusters and hidden relationships to Sequons and Transmembrane domains in Human proteins

Membrane proteins often possess critical structural features, such as transmembrane domains (TMs), N-glycosylation, and disulfide bonds (SS bonds), which are essential to their structure and function. Here, we extend the study of the motifs carrying N-glycosylation, i.e. the sequons, and the Cys residues supporting the SS bonds, to the whole human proteome with a particular focus on the Cys positions in human proteins with respect to those of sequons and TMs. As the least abundant amino acid residue in protein sequences, the positions of Cys residues in proteins are not random but rather selected through evolution. We discovered that the frequency of Cys residues in proteins is length dependent, and the frequency of CC gaps formed between adjacent Cys residues can be used as a classifier to distinguish proteins with special structures and functions, such as keratin-associated proteins (KAPs), extracellular proteins with EGF-like domains, and nuclear proteins with zinc finger C2H2 domains. Most importantly, by comparing the positions of Cys residues to those of sequons and TMs, we discovered that these structural features can form dense clusters in highly repeated and mutually exclusive modalities in protein sequences. The evolutionary advantages of such complementarity among the three structural features are discussed, particularly in light of structural dynamics in proteins that are lacking from computational predictions. The discoveries made here highlight the sequence-structure-function axis in biological organisms that can be utilized in future protein engineering toward synthetic biology.

Boltzmann Model Predicts Glycan Structures from Lectin Binding

Glycans are complex oligosaccharides that are involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese hamster ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network

Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated glycosylation in Enhanced Aromatic Sequons, SARS-CoV-2 spike, and IgG3 variants, thus demonstrating the ability of the algorithm to predict both glycan structure and abundance. Thus, protein structure constrains glycosylation, and the neural network enables predictive in silico glycosylation of uncharacterized or novel protein sequences and genetic variants.

Protein feature engineering framework for AMPylation site prediction

AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model's capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations.

A quest for cytosolic sequons and their functions

Evolution shapes protein sequences for their functions. Here, we studied the moonlighting functions of the N-linked sequon NXS/T, where X is not P, in human nucleocytosolic proteins. By comparing membrane and secreted proteins in which sequons are well known for N-glycosylation, we discovered that cyto-sequons can participate in nucleic acid binding, particularly in zinc finger proteins. Our global studies further discovered that sequon occurrence is largely proportional to protein length. The contribution of sequons to protein functions, including both N-glycosylation and nucleic acid binding, can be regulated through their density as well as the biased usage between NXS and NXT. In proteins where other PTMs or structural features are rich, such as phosphorylation, transmembrane ɑ-helices, and disulfide bridges, sequon occurrence is scarce. The information acquired here should help understand the relationship between protein sequence and function and assist future protein design and engineering.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Mechanisms of thyrotropin receptor-mediated phenotype variability deciphered by gene mutations and M453T-knockin model

The clinical spectrum of thyrotropin receptor-mediated (TSHR-mediated) diseases varies from loss-of-function mutations causing congenital hypothyroidism to constitutively active mutations (CAMs) leading to nonautoimmune hyperthyroidism (NAH). Variation at the TSHR locus has also been associated with altered lipid and bone metabolism and autoimmune thyroid diseases. However, the extrathyroidal roles of TSHR and the mechanisms underlying phenotypic variability among TSHR-mediated diseases remain unclear. Here we identified and characterized TSHR variants and factors involved in phenotypic variability in different patient cohorts, the FinnGen database, and a mouse model. TSHR CAMs were found in all 16 patients with NAH, with 1 CAM in an unexpected location in the extracellular leucine-rich repeat domain (p.S237N) and another in the transmembrane domain (p.I640V) in 2 families with distinct hyperthyroid phenotypes. In addition, screening of the FinnGen database revealed rare functional variants as well as distinct common noncoding TSHR SNPs significantly associated with thyroid phenotypes, but there was no other significant association between TSHR variants and more than 2,000 nonthyroid disease endpoints. Finally, our TSHR M453T-knockin model revealed that the phenotype was dependent on the mutation's signaling properties and was ameliorated by increased iodine intake. In summary, our data show that TSHR-mediated disease risk can be modified by variants at the TSHR locus both inside and outside the coding region as well as by altered TSHR-signaling and dietary iodine, supporting the need for personalized treatment strategies.

Importance of N-Glycosylation for the Expression and Function of Human Organic Anion Transporting Polypeptide 2B1

Human organic anion transporting polypeptide 2B1 (OATP2B1) is a membrane transporter widely expressed in organs crucial for drug absorption and disposition such as the intestine, liver, and kidney. Evidence indicates that OATP2B1 is a glycoprotein. However, the sites of glycosylation and their contribution to the function and expression of OATP2B1 are largely unknown. In this study, by site-directed mutagenesis, we determined that two of four potential N-glycosylation sites in OATP2B1, N176 and N538, are indeed glycosylated. Functional studies revealed that the transport activities of mutants N176Q and N538Q were greatly reduced as compared to that of wild-type OATP2B1. However, the reduced activity was not due to the impairment of transport function per se but due to the decreased surface expression as the Km and normalized Vmax values of N176Q and N538Q were comparable to those of OATP2B1. Quantitative polymerase chain reaction (PCR) revealed that N176Q and N538Q mutations did not affect the expression of OATP2B1 at a transcriptional level. Immunofluorescence analysis showed that deglycosylated OATP2B1 was largely retained in the endoplasmic reticulum, which may activate the endoplasmic reticulum-associated degradation pathway, and the ubiquitin-proteasome system played a major role in the degradation of OATP2B1. Taken together, OATP2B1 is N-glycosylated, and N-glycosylation is essential for the surface expression of OATP2B1 but not critical for the transport function of OATP2B1 per se.

Proteomics as a tool to study the osteoimmunomodulatory role of metallic ions in a sol-gel coating

The success of bone implants depends on the osteoimmunomodulatory (OIM) activity of the biomaterials in the interactions with the periimplantary tissues. Many in vitro tests have been conducted to evaluate the osteoimmunology effects of biomaterials. However, results of these tests have often been inconclusive. This study examines the properties of newly developed sol-gel coatings doped with two metal ions associated with bone regeneration, Ca and Zn. The study uses both proteomic methods and traditional in vitro assays. The results demonstrate that proteomics is an effective tool to scrutinize the OIM properties of the materials. Moreover, sol-gel coatings offer excellent base materials to evaluate the effects of metal ions on these properties. The obtained data highlight the highly tunable nature of sol-gel materials; studying the materials with different doping levels supplies valuable information on the interactions between the immune and bone-forming processes.

Evolutionary conservation of sequence motifs at sites of protein modification

Gene duplications are common in biology and are likely to be an important source of functional diversification and specialization. The yeast Saccharomyces cerevisiae underwent a whole-genome duplication event early in evolution, and a substantial number of duplicated genes have been retained. We identified more than 3500 instances where only one of two paralogous proteins undergoes posttranslational modification despite having retained the same amino acid residue in both. We also developed a web-based search algorithm (CoSMoS.c.) that scores conservation of amino acid sequences based on 1011 wild and domesticated yeast isolates and used it to compare differentially modified pairs of paralogous proteins. We found that the most common modifications-phosphorylation, ubiquitylation, and acylation but not N-glycosylation-occur in regions of high sequence conservation. Such conservation is evident even for ubiquitylation and succinylation, where there is no established 'consensus site' for modification. Differences in phosphorylation were not associated with predicted secondary structure or solvent accessibility but did mirror known differences in kinase-substrate interactions. Thus, differences in posttranslational modification likely result from differences in adjoining amino acids and their interactions with modifying enzymes. By integrating data from large-scale proteomics and genomics analysis, in a system with such substantial genetic diversity, we obtained a more comprehensive understanding of the functional basis for genetic redundancies that have persisted for 100 million years.

Engineering receptors in the secretory pathway for orthogonal signalling control

Synthetic receptors targeted to the secretory pathway often fail to exhibit the expected activity due to post-translational modifications (PTMs) and/or improper folding. Here, we engineered synthetic receptors that reside in the cytoplasm, inside the endoplasmic reticulum (ER), or on the plasma membrane through orientation adjustment of the receptor parts and by elimination of dysfunctional PTMs sites. The cytoplasmic receptors consist of split-TEVp domains that reconstitute an active protease through chemically-induced dimerization (CID) that is triggered by rapamycin, abscisic acid, or gibberellin. Inside the ER, however, some of these receptors were non-functional, but their activity was restored by mutagenesis of cysteine and asparagine, residues that are typically associated with PTMs. Finally, we engineered orthogonal chemically activated cell-surface receptors (OCARs) consisting of the Notch1 transmembrane domain fused to cytoplasmic tTA and extracellular CID domains. Mutagenesis of cysteine residues in CID domains afforded functional OCARs which enabled fine-tuning of orthogonal signalling in mammalian cells.

Insilico prediction and functional analysis of nonsynonymous SNPs in human CTLA4 gene

The CTLA4 receptor is an immune checkpoint involved in the downregulation of T cells. Polymorphisms in this gene have been found to be associated with different diseases like rheumatoid arthritis, autosomal dominant immune dysregulation syndrome, juvenile idiopathic arthritis and autoimmune Addison's disease. Therefore, the identification of polymorphisms that have an effect on the structure and function of CTLA4 gene is important. Here we identified the most damaging missense or non-synonymous SNPs (nsSNPs) that might be crucial for the structure and function of CTLA4 using different bioinformatics tools. These in silico tools included SIFT, PROVEAN, PhD-SNP, PolyPhen-2 followed by MutPred2, I-Mutant 2.0 and ConSurf. The protein structures were predicted using Phyre2 and I-TASSER, while the gene-gene interactions were predicted by GeneMANIA and STRING. Our study identified three damaging missense SNPs rs1553657429, rs1559591863 and rs778534474 in coding region of CTLA4 gene. Among these SNPs the rs1553657429 showed a loss of potential phosphorylation site and was found to be highly conserved. The prediction of gene-gene interaction showed the interaction of CTlA4 with other genes and its importance in different pathways. This investigation of damaging nsSNPs can be considered in future while studying CTLA4 related diseases and can be of great importance in precision medicine.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

Biophysical differences in IgG1 Fc-based therapeutics relate to their cellular handling, interaction with FcRn and plasma half-life

Antibody-based therapeutics (ABTs) are used to treat a range of diseases. Most ABTs are either full-length IgG1 antibodies or fusions between for instance antigen (Ag)-binding receptor domains and the IgG1 Fc fragment. Interestingly, their plasma half-life varies considerably, which may relate to how they engage the neonatal Fc receptor (FcRn). As such, there is a need for an in-depth understanding of how different features of ABTs affect FcRn-binding and transport behavior. Here, we report on how FcRn-engagement of the IgG1 Fc fragment compare to clinically relevant IgGs and receptor domain Fc fusions, binding to VEGF or TNF-α. The results reveal FcRn-dependent intracellular accumulation of the Fc, which is in line with shorter plasma half-life than that of full-length IgG1 in human FcRn-expressing mice. Receptor domain fusion to the Fc increases its half-life, but not to the extent of IgG1. This is mirrored by a reduced cellular recycling capacity of the Fc-fusions. In addition, binding of cognate Ag to ABTs show that complexes of similar size undergo cellular transport at different rates, which could be explained by the biophysical properties of each ABT. Thus, the study provides knowledge that should guide tailoring of ABTs regarding optimal cellular sorting and plasma half-life.

Analysis of clinically approved antibody-based therapeutics reveals different structural designs, such as full-length IgG1 or Fc-fusions, entail distinct biophysical properties that affect FcRn binding, intracellular transport and plasma half-life.

EpiBuilder: A Tool for Assembling, Searching, and Classifying B-Cell Epitopes

Epitopes are portions of a protein that are recognized by antibodies. These small amino acid sequences represent a significant breakthrough in a branch of bioinformatics called immunoinformatics. Various software are available for linear B-cell epitope (BCE) prediction such as ABCPred, SVMTrip, EpiDope, and EpitopeVec; a well-known BCE predictor is BepiPred-2.0. However, despite the prediction, there are several essential steps, such as epitope assembly, evaluation, and searching for epitopes in other proteomes. Here, we present EpiBuilder (https://epibuilder.sourceforge.io), a user friendly software that assists in epitope assembly, classifying and searching using input results of BepiPred-2.0. EpiBuilder generates several output results from these data and supports a proteome-wide processing approach. In addition, this software provides the following features: Chou & Fasman beta-turn prediction, Emini surface accessibility prediction, Karplus and Schulz flexibility prediction, Kolaskar and Tongaonkar antigenicity, Parker hydrophilicity prediction, N-glycosylation domains, and hydropathy. These information generate a unique topology for each epitope, visually demonstrating its characteristics. The software can search the entire epitope sequence in various FASTA files, and it allows to use BLASTP to identify epitopes that eventually have sequence variations. As an EpiBuilder application, we developed a epitope dataset from the protozoan Trypanosoma brucei gambiense, the gram-positive bacterium Clostridioides difficile, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.

Connexins evolved after early chordates lost innexin diversity

Gap junction channels are formed by two unrelated protein families. Non-chordates use the primordial innexins, while chordates use connexins that superseded the gap junction function of innexins. Chordates retained innexin-homologs, but N-glycosylation prevents them from forming gap junctions. It is puzzling why chordates seem to exclusively use the new gap junction protein and why no chordates should exist that use non-glycosylated innexins to form gap junctions. Here, we identified glycosylation sites of 2388 innexins from 174 non-chordate and 276 chordate species. Among all chordates, we found not a single innexin without glycosylation sites. Surprisingly, the glycosylation motif is also widespread among non-chordate innexins indicating that glycosylated innexins are not a novelty of chordates. In addition, we discovered a loss of innexin diversity during early chordate evolution. Most importantly, lancelets, which lack connexins, exclusively possess only one highly conserved innexin with one glycosylation site. A bottleneck effect might thus explain why connexins have become the only protein used to form chordate gap junctions.

Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins

Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. Hence, there is a demand to build fast and efficient computational methods to address this problem. Here, we present the SPRINT-Gly framework containing the largest dataset and a prediction model of glycosylation sites for a given protein sequence. In this framework, we construct a large dataset containing N- and O-linked glycosylation sites of human and mouse proteins, collected from different sources. We then introduce the SPRINT-Gly method to predict putative N- and O-linked sites. SPRINT-Gly is a machine learning-based approach consisting of a number of trained predictive models for glycosylation sites in both human and mouse proteins, separately. The method is built by incorporating sequence-based, predicted structural, and physicochemical information of the neighboring residues of each N- and O-linked glycosylation site and by training deep learning neural network and support vector machine as classifiers. SPRINT-Gly outperformed other existing methods by achieving 18% and 50% higher Matthew's correlation coefficient for N- and O-linked glycosylation site prediction, respectively. SPRINT-Gly is publicly available as an online and stand-alone predictor at https://sparks-lab.org/server/sprint-gly/ .

Computational Saturation Mutagenesis of SARS-CoV-1 Spike Glycoprotein: Stability, Binding Affinity, and Comparison With SARS-CoV-2

Severe Acute respiratory syndrome coronavirus (SARS-CoV-1) attaches to the host cell surface to initiate the interaction between the receptor-binding domain (RBD) of its spike glycoprotein (S) and the human Angiotensin-converting enzyme (hACE2) receptor. SARS-CoV-1 mutates frequently because of its RNA genome, which challenges the antiviral development. Here, we per-formed computational saturation mutagenesis of the S protein of SARS-CoV-1 to identify the residues crucial for its functions. We used the structure-based energy calculations to analyze the effects of the missense mutations on the SARS-CoV-1 S stability and the binding affinity with hACE2. The sequence and structure alignment showed similarities between the S proteins of SARS-CoV-1 and SARS-CoV-2. Interestingly, we found that target mutations of S protein amino acids generate similar effects on their stabilities between SARS-CoV-1 and SARS-CoV-2. For example, G839W of SARS-CoV-1 corresponds to G857W of SARS-CoV-2, which decrease the stability of their S glycoproteins. The viral mutation analysis of the two different SARS-CoV-1 isolates showed that mutations, T487S and L472P, weakened the S-hACE2 binding of the 2003–2004 SARS-CoV-1 isolate. In addition, the mutations of L472P and F360S destabilized the 2003–2004 viral isolate. We further predicted that many mutations on N-linked glycosylation sites would increase the stability of the S glycoprotein. Our results can be of therapeutic importance in the design of antivirals or vaccines against SARS-CoV-1 and SARS-CoV-2.

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.

Genome-wide identification, evolution, and transcript profiling of Aldehyde dehydrogenase superfamily in potato during development stages and stress conditions

The Aldehyde dehydrogenase (ALDH) superfamily comprises a group of enzymes involved in the scavenging of toxic aldehyde molecules by converting them into their corresponding non-toxic carboxylic acids. A genome-wide study in potato identified a total of 22 ALDH genes grouped into ten families that are presented unevenly throughout all the 12 chromosomes. Based on the evolutionary analysis of ALDH proteins from different plant species, ALDH2 and ALDH3 were found to be the most abundant families in the plant, while ALDH18 was found to be the most distantly related one. Gene expression analysis revealed that the expression of StALDH genes is highly tissue-specific and divergent in various abiotic, biotic, and hormonal treatments. Structural modelling and functional analysis of selected StALDH members revealed conservancy in their secondary structures and cofactor binding sites. Taken together, our findings provide comprehensive information on the ALDH gene family in potato that will help in developing a framework for further functional studies.

Biopharmaceutics 4.0, Advanced Pre-Clinical Development of mRNA-Encoded Monoclonal Antibodies to Immunosuppressed Murine Models

Administration of mRNA against SARS-CoV-2 has demonstrated sufficient efficacy, tolerability and clinical potential to disrupt the vaccination field. A multiple-arm, cohort randomized, mixed blind, placebo-controlled study was designed to investigate the in vivo expression of mRNA antibodies to immunosuppressed murine models to conduct efficacy, safety and bioavailability evaluation. Enabling 4.0 tools we reduced animal sacrifice, while interventions were designed compliant to HARRP and SPIRIT engagement: (a) Randomization, blinding; (b) pharmaceutical grade formulation, monitoring; (c) biochemical and histological analysis; and (d) theoretic, statistical analysis. Risk assessment molded the study orientations, according to the ARRIVE guidelines. The primary target of this protocol is the validation of the research hypothesis that autologous translation of Trastuzumab by in vitro transcribed mRNA-encoded antibodies to immunosuppressed animal models, is non-inferior to classical treatments. The secondary target is the comparative pharmacokinetic assessment of the novel scheme, between immunodeficient and healthy subjects. Herein, the debut clinical protocol, investigating the pharmacokinetic/pharmacodynamic impact of mRNA vaccination to immunodeficient organisms. Our design, contributes novel methodology to guide the preclinical development of RNA antibody modalities by resolving efficacy, tolerability and dose regime adjustment for special populations that are incapable of humoral defense.

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.

Characterization of Five Transmembrane Proteins: With Focus on the Tweety, Sideroflexin, and YIP1 Domain Families

Transmembrane proteins are involved in many essential cell processes such as signal transduction, transport, and protein trafficking, and hence many are implicated in different disease pathways. Further, as the structure and function of proteins are correlated, investigating a group of proteins with the same tertiary structure, i.e., the same number of transmembrane regions, may give understanding about their functional roles and potential as therapeutic targets. This analysis investigates the previously unstudied group of proteins with five transmembrane-spanning regions (5TM). More than half of the 58 proteins identified with the 5TM architecture belong to 12 families with two or more members. Interestingly, more than half the proteins in the dataset function in localization activities through movement or tethering of cell components and more than one-third are involved in transport activities, particularly in the mitochondria. Surprisingly, no receptor activity was identified within this dataset in large contrast with other TM groups. The three major 5TM families, which comprise nearly 30% of the dataset, include the tweety family, the sideroflexin family and the Yip1 domain (YIPF) family. We also analyzed the evolutionary origin of these three families. The YIPF family appears to be the most ancient with presence in bacteria and archaea, while the tweety and sideroflexin families are first found in eukaryotes. We found no evidence of common decent for these three families. About 30% of the 5TM proteins have prominent expression in the brain, liver, or testis. Importantly, 60% of these proteins are identified as cancer prognostic markers, where they are associated with clinical outcomes of various tumor types. Nearly 10% of the 5TMs are still not fully characterized and further investigation of their functional activities and expression is warranted. This study provides the first comprehensive analysis of proteins with the 5TM architecture, providing details of their unique characteristics.

Genome sequence of the cardiopulmonary canid nematode Angiostrongylus vasorum reveals species-specific genes with potential involvement in coagulopathy

Angiostrongylus vasorum is an emerging parasitic nematode of canids and causes respiratory distress, bleeding, and other signs in dogs. Despite its clinical importance, the molecular toolbox allowing the study of the parasite is incomplete. To address this gap, we have sequenced its nuclear genome using Oxford nanopore sequencing, polished with Illumina reads. The size of the final genome is 280 Mb comprising 468 contigs, with an N50 value of 1.68 Mb and a BUSCO score of 93.5%. Ninety-three percent of 13,766 predicted genes were assigned to putative functions. Three folate carriers were found exclusively in A. vasorum, with potential involvement in host coagulopathy. A screen for previously identified vaccine candidates, the aminopeptidase H11 and the somatic protein rHc23, revealed homologs in A. vasorum. The genome sequence will provide a foundation for the development of new tools against canine angiostrongylosis, supporting the identification of potential drug and vaccine targets.

N-Glycosylation Network Construction and Analysis to Modify Glycans on the Spike (S) Glycoprotein of SARS-CoV-2

Background: The N-glycan structure and composition of the spike (S) protein of SARS-CoV-2 are pertinent to vaccine development and efficacy.

Methods: We reconstructed the glycosylation network based on previously published mass spectrometry data using GNAT, a glycosylation network analysis tool. Our compilation of the network tool had 26 glycosyltransferase and glucosidase enzymes and could infer the pathway of glycosylation machinery based on glycans in the virus spike protein. Once the glycan biosynthesis pathway was generated, we simulated the effect of blocking specific enzymes—swainsonine or deoxynojirimycin for blocking mannosidase-II and indolizidine for blocking alpha-1,6-fucosyltransferase—to see how they would affect the biosynthesis network and the glycans that were synthesized.

Results: The N-glycan biosynthesis network of SARS-CoV-2 spike protein shows an elaborate enzymatic pathway with several intermediate glycans, along with the ones identified by mass spectrometric studies. Of the 26 enzymes, the following were involved—Man-Ia, MGAT1, MGAT2, MGAT4, MGAT5, B3GalT, B4GalT, Man-II, SiaT, ST3GalI, ST3GalVI, and FucT8. Blocking specific enzymes resulted in a substantially modified glycan profile of SARS-CoV-2.

Conclusion: Variations in the final N-glycan profile of the virus, given its site-specific microheterogeneity, are factors in the host response to the infection, vaccines, and antibodies. Heterogeneity in the N-glycan profile of the spike (S) protein and its potential effect on vaccine efficacy or adverse reactions to the vaccines remain unexplored. Here, we provide all the resources we generated—the glycans in the glycoCT xml format and the biosynthesis network for future work.

A Glycoside Hydrolase Family 99-Like Domain-Containing Protein Modifies Outer Membrane Proteins to Maintain Xanthomonas Pathogenicity and Viability in Stressful Environments

Protein glycosylation is an essential process that plays an important role in proteome stability, protein structure, and protein function modulation in eukaryotes. However, in bacteria, especially plant pathogenic bacteria, similar studies are lacking. Here, we investigated the relationship between protein glycosylation and pathogenicity by using Xanthomonas oryzae pv. oryzae, the causal agent of bacterial leaf blight in rice, as a well-defined example. In our previous work, we identified a virulence-related hypothetical protein, PXO_03177, but how this protein regulates X. oryzae pv. oryzae virulence has remained unclear. BLAST analysis showed that most homologous proteins of PXO_03177 are glycoside hydrolase family 99-like domain-containing proteins. In the current study, we found that the outer membrane integrity of ΔPXO_03177 appeared to be disrupted. Extracting the outer membrane proteins (OMPs) and performing comparative proteomics and sodium dodecyl sulphate-polyacrylamide gel electrophoresis gel staining analyses revealed that PXO_03177 deletion altered the protein levels of 13 OMPs. Western blot analyses showed that the protein level and glycosylation modification of PXO_02523, a related OmpA family-like protein, was changed in the ΔPXO_03177 mutant background strain. Additionally, the interaction between PXO_03177 and PXO_02523 was confirmed by coimmunoprecipitation. Both PXO_03177 and PXO_02523 play important roles in regulating pathogen virulence and viability in stressful environments. This work provides the first evidence that protein glycosylation is necessary for the virulence of plant pathogenic bacteria.

Transmission of Respiratory Syncytial Virus genotypes in Cali, Colombia

Background

Colombia's climatological variety, added to pathogen diversity, creates local niches for infectious diseases. In Bogotá, respiratory syncytial virus causes 30%‐52% of the cases of respiratory infections. In coastal or inter‐Andean cities with higher temperature and longer dry seasons, frequency of this virus is 7%‐13%. By 2017, increased hospitalizations due to airway infections occurred in regions whose weather is differently influenced by “El Niño Southern Oscillation” than in Bogotá, although microbial diversity might have also been involved.

Methods

For Cali, an inter‐Andean city with warm tropical weather, records of respiratory syncytial virus from 2014 to 2018, in children two years old or younger, were analyzed, and genotypes transmitted during 2016‐2017 were identified based on partial sequences of glycoprotein G.

Results

Most cases of respiratory syncytial virus in Cali occur in the first semesters, with peaks expressed around March‐April, without a clear association with pluviosity. Unlike the biannual rotating pattern of Bogotá, co‐circulation of types A and B was detected. As years pass, transmission seasons are becoming longer and frequencies of the virus augment. The viral genotypes identified follow international trends with dominance of Ontario and Buenos Aires clades. Similar to other isolates in these clades, viruses from Cali exhibit glycosylation variability that may account for their fitness.

Conclusions

The pattern of respiratory syncytial virus transmission in Cali differs from that in Bogotá. Its epidemiology is shifting and will remain so with the advent of novel respiratory diseases. This may impact the introduction of vaccination schemes for these or other respiratory viruses.

Diet affects glycosylation of serum proteins in women at risk for cardiometabolic disease

Background

Glycoproteomics deals with glycoproteins that are formed by post-translational modification when sugars (like fucose and sialic acid) are attached to protein. Glycosylation of proteins influences function, but whether glycosylation is altered by diet is unknown.

Objective

To evaluate the effect of consuming a diet based on the Dietary Guidelines for Americans on circulating glycoproteins that have previously been associated with cardiometabolic diseases.

Design

Forty-four women, with one or more metabolic syndrome characteristics, completed an 8-week randomized controlled feeding intervention (n = 22) consuming a diet based on the Dietary Guidelines for Americans (DGA 2010); the remaining consumed a ‘typical American diet’ (TAD, n = 22). Fasting serum samples were obtained at week0 (baseline) and week8 (post-intervention); 17 serum proteins were chosen for targeted analyses. Protein standards and serum samples were analyzed in a UHPLC-MS protocol to determine peptide concentration and their glycan (fucosylation or sialylation) profiles. Data at baseline were used in correlational analyses; change in proteins and glycans following intervention were used in non-parametric analyses.

Results

At baseline, women with more metabolic syndrome characteristics had more fucosylation (total di-fucosylated proteins: p = 0.045) compared to women with a lesser number of metabolic syndrome characteristics. Dietary refined grain intake was associated with increased total fucosylation (ρ = − 0.530, p < 0.001) and reduced total sialylation (ρ = 0.311, p = 0.042). After the 8-week intervention, there was higher sialylation following the DGA diet (Total di-sialylated protein p = 0.018, poly-sialylated orosomucoid p = 0.012) compared to the TAD diet.

Conclusions

Based on this study, glycosylation of proteins is likely affected by dietary patterns; higher sialylation was associated with a healthier diet pattern. Altered glycosylation is associated with several diseases, particularly cancer and type 2 diabetes, and this study raises the possibility that diet may influence disease state by altering glycosylation.

Clinical trial registration

NCT02298725 at clinicaltrials.gov; https://clinicaltrials.gov/ct2/show/NCT02298725.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00394-021-02539-7.

Structural insight into the novel iron-coordination and domain interactions of transferrin-1 from a model insect, Manduca sexta

Transferrins function in iron sequestration and iron transport by binding iron tightly and reversibly. Vertebrate transferrins coordinate iron through interactions with two tyrosines, an aspartate, a histidine, and a carbonate anion, and conformational changes that occur upon iron binding and release have been described. Much less is known about the structure and functions of insect transferrin-1 (Tsf1), which is present in hemolymph and influences iron homeostasis mostly by unknown mechanisms. Amino acid sequence and biochemical analyses have suggested that iron coordination by Tsf1 differs from that of the vertebrate transferrins. Here we report the first crystal structure (2.05 Å resolution) of an insect transferrin. Manduca sexta (MsTsf1) in the holo form exhibits a bilobal fold similar to that of vertebrate transferrins, but its carboxyl-lobe adopts a novel orientation and contacts with the amino-lobe. The structure revealed coordination of a single Fe3+ ion in the amino-lobe through Tyr90, Tyr204, and two carbonate anions. One carbonate anion is buried near the ferric ion and is coordinated by four residues, whereas the other carbonate anion is solvent exposed and coordinated by Asn121. Notably, these residues are highly conserved in Tsf1 orthologs. Docking analysis suggested that the solvent exposed carbonate position is capable of binding alternative anions. These findings provide a structural basis for understanding Tsf1 function in iron sequestration and transport in insects as well as insight into the similarities and differences in iron homeostasis between insects and humans.

Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels

Glycosylation is a dynamic enzymatic process that attaches glycan to proteins or other organic molecules such as lipoproteins. Research has shown that such a process in ion channel proteins plays a fundamental role in modulating ion channel functions. This study used a computational method to predict N-linked glycosylation sites, the most common type, in ion channel proteins. From segments of ion channel proteins centered around N-linked glycosylation sites, the amino acid embedding vectors of each residue were concatenated to create features for prediction. We experimented with two different models for converting amino acids to their corresponding embeddings: one was fed with ion channel sequences and the other with a large dataset composed of more than one million protein sequences. The latter model stemmed from the idea of transfer learning technique and emerged as a more efficient feature extractor. Our best model was obtained from this transfer learning approach and a hyperparameter tuning process with a random search on 5-fold cross-validation data. It achieved an accuracy, specificity, sensitivity, and Matthews correlation coefficient of 93.4%, 92.8%, 98.6%, and 0.726, respectively. Corresponding scores on an independent test were 92.9%, 92.2%, 99%, and 0.717. These results outperform the position-specific scoring matrix features that are predominantly employed in post-translational modification site predictions. Furthermore, compared to N-GlyDE, GlycoEP, SPRINT-Gly, the most recent N-linked glycosylation site predictors, our model yields higher scores on the above 4 metrics, thus further demonstrating the efficiency of our approach.

Prediction of bio-sequence modifications and the associations with diseases

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

Identifying the Zoonotic Origin of SARS-CoV-2 by Modeling the Binding Affinity between the Spike Receptor-Binding Domain and Host ACE2

Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that binding to ACE2 proteins is the first step for the coronaviruses to invade host cells, we propose a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated for the modeled Spike-RBD/ACE2 complex structures correlated closely with the effectiveness of animal infection as determined by multiple experimental data sets. Built on the optimized binding affinity cutoff, we suggest a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggests a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation, but also, more importantly, it proposes a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.

Substrate Specificity of LACCASE8 Facilitates Polymerization of Caffeyl Alcohol for C-Lignin Biosynthesis in the Seed Coat of Cleome hassleriana

Catechyl lignin (C-lignin) is a linear homopolymer of caffeyl alcohol found in the seed coats of diverse plant species. Its properties make it a natural source of carbon fibers and high-value chemicals, but the mechanism of in planta polymerization of caffeyl alcohol remains unclear. In the ornamental plant Cleome hassleriana, lignin biosynthesis in the seed coat switches from guaiacyl lignin to C-lignin at ∼12 d after pollination. Here we found that the transcript profile of the laccase gene ChLAC8 parallels the accumulation of C-lignin during seed coat development. Recombinant ChLAC8 oxidizes caffeyl and sinapyl alcohols, generating their corresponding dimers or trimers in vitro, but cannot oxidize coniferyl alcohol. We propose a basis for this substrate preference based on molecular modeling/docking experiments. Suppression of ChLAC8 expression led to significantly reduced C-lignin content in the seed coats of transgenic Cleome plants. Feeding of ¹³C-caffeyl alcohol to the Arabidopsis (Arabidopsis thaliana) caffeic acid o-methyltransferase mutant resulted in no incorporation of ¹³C into C-lignin, but expressing ChLAC8 in this genetic background led to appearance of C-lignin with >40% label incorporation. These results indicate that ChLAC8 is required for C-lignin polymerization and determines lignin composition when caffeyl alcohol is available.

The genomes of Mourilyan virus and Wēnzhōu shrimp virus 1 of prawns comprise 4 RNA segments

Reported here is the complete genome sequence of Mourilyan virus (MoV) that infects giant tiger (Penaeus monodon) and kuruma prawns (P. japonicas) in Australia. Its genome was determined using various PCR strategies based on the sequences of 3 randomly-amplified cDNA clones to its L and M RNA segments discovered in a library generated to determine the genome sequence of gill-associated ronivirus. The sequences of PCR products and clones obtained showed the MoV genome to comprise 4 ssRNA segments (L, M, S1 and S2), as confirmed by Northern blotting using RNA from naïve and MoV-infected prawns, and by Illumina sequence analysis of semi-purified MoV. BLASTn searches identified the MoV L, M and S1 RNA segments to be homologous to Wēnzhōu shrimp virus 1 (WzSV1) segments discovered recently in a P. monodon RNA-Seq library (SRR1745808). Mapping this read library to the MoV S2 RNA segment identified WzSV1 to also possess an equivalent segment. BLASTp searches identified the putative non-structural protein (NSs2; 393-394 aa) encoded in their S2 RNA segments to have no homologs in GenBank. Possibly due to NSs2 being encoded in a discrete RNA segment rather than in ambisense relative to the N protein as in the S RNA segments of other phenuiviruses, each of 6 MoV S1 RNA segment clones sequenced possessed a variable-length (≤ 645 nt) imperfect GA-repeat extending from the N protein stop codon to the more variable ∼90 nt segment terminal sequence. Read mapping of RNA-Seq library SRR1745808 showed the WzSV1 S1 RNA segment to possess a similar GA-repeat. However, paired-read variations hindered definitive assembly of a consensus sequence. All 4 MoV and WzSV1 RNA segments terminated with a 10 nt inverted repeat sequence (5'-ACACAAAGAC.) identical to the RNA segment termini of uukuviruses. Phylogenetic analyses of MoV/WzSV1 RNA-dependant RNA polymerase (L RNA), G1G2 precursor glycoprotein (M RNA) and nucleocapsid (N) protein (S1 RNA) sequences generally clustered them with as yet unassigned crustacean/diptera bunya-like viruses on branches positioned closely to others containing tick-transmitted phenuiviruses. As genome sequences of most phenuiviruses discovered recently have originated from meta-transcriptomics studies, the data presented here showing the MoV and WzSV1 genomes to comprise more than 3 RNA segments, like the plant tenuiviruses, suggests a need to investigate the genomes of these unassigned viruses more closely.

Invertases in Phytophthora infestans Localize to Haustoria and Are Programmed for Infection-Specific Expression

The oomycete Phytophthora infestans, the causal agent of potato and tomato blight, expresses two extracellular invertases. Unlike typical fungal invertases, the P. infestans genes are not sucrose induced or glucose repressed but instead appear to be under developmental control. Transcript levels of both genes were very low in mycelia harvested from artificial medium but high in preinfection stages (sporangia, zoospores, and germinated cysts), high during biotrophic growth in leaves and tubers, and low during necrotrophy. Genome-wide analyses of metabolic enzymes and effectors indicated that this expression profile was fairly unusual, matched only by a few other enzymes, such as carbonic anhydrases and a few RXLR effectors. Genes for other metabolic enzymes were typically downregulated in the preinfection stages. Overall metabolic gene expression during the necrotrophic stage of infection clustered with artificial medium, while the biotrophic phase formed a separate cluster. Confocal microscopy of transformants expressing green fluorescent protein (GFP) fusions indicated that invertase protein resided primarily in haustoria during infection. This localization was not attributable to haustorium-specific promoter activity. Instead, the N-terminal regions of proteins containing signal peptides were sufficient to deliver proteins to haustoria. Invertase expression during leaf infection was linked to a decline in apoplastic sucrose, consistent with a role of the enzymes in plant pathogenesis. This was also suggested by the discovery that invertase genes occur across multiple orders of oomycetes but not in most animal pathogens or a mycoparasite.IMPORTANCE Oomycetes cause hundreds of diseases in economically and environmentally significant plants. How these microbes acquire host nutrients is not well understood. Many oomycetes insert specialized hyphae called haustoria into plant cells, but unlike their fungal counterparts, a role in nutrition has remained unproven. The discovery that Phytophthora invertases localize to haustoria provides the first strong evidence that these structures participate in feeding. Since regions of proteins containing signal peptides targeted proteins to the haustorium-plant interface, haustoria appear to be the primary machinery for secreting proteins during biotrophic pathogenesis. Although oomycete invertases were acquired laterally from fungi, their expression patterns have adapted to the Phytophthora lifestyle by abandoning substrate-level regulation in favor of developmental control, allowing the enzymes to be produced in anticipation of plant colonization. This study highlights how a widely distributed hydrolytic enzyme has evolved new behaviors in oomycetes.

Identifying zoonotic origin of SARS-CoV-2 by modeling the binding affinity between Spike receptor-binding domain and host ACE2

Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that the recognition of the binding ACE2 proteins is the first step for the coronaviruses to invade host cells, we proposed a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated on the modeled Spike-RBD/ACE2 complex structures correlate closely with the effectiveness of animal infections as determined by multiple experimental datasets. Built on the optimized binding affinity cutoff, we suggested a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggested a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation; but more importantly, it proposed a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.

In silico functional and structural characterization of hepatitis B virus PreS/S-gene in Iranian patients infected with chronic hepatitis B virus genotype D

Objective

Chronic hepatitis B (CHB) virus infection is the most prevalent chronic liver disease and has become a serious threat to human health. In this study, we attempted to specify and predict several properties including physicochemical, mutation sites, B-cell epitopes, phosphorylation sites, N-link, O-link glycosylation sites, and protein structures of S protein isolated from Ahvaz.

Materials and methods

Initially, hepatitis B virus DNA (HBV DNA) was extracted from five sera samples of untreated chronic hepatitis B patients. The full-length HBV genomes were amplified and then cloned in pTZ57 R/T vector. The full sequences of HBV were registered in the GenBank with accessions numbers (MK355500), (MK355501) and (MK693107-9). PROTSCALE, Expasy's ProtParam, immuneepitope, ABCpred, BcePred, Bepipred, Algpred, VaxiJen, SCRATCH, DiANNA, plus a number of online analytical processing tools were used to analyse and predict the preS/S gene of genotype D sequences. The present study is the first analytical research on samples obtained from Ahvaz.

Results

We found major hydrophilic region (MHR) mutations at "a" determining region that included K122R, N131T, F134Y, P142L, and T126N mutations. Moreover, Ahvaz sequences revealed four sites (4, 112, 166, and 309) in the preS/S gene for N-glycosylation that could possibly be a potential target for anti-HBV therapy.

Conclusion

In the present study, mutations were identified at positions T113S and N131T within the MHR region of S protein; these mutations can potentially decrease the effect of hepatitis B vaccination in vaccine recipients.

Interleukin 34 Serves as a Novel Molecular Adjuvant against Nocardia Seriolae Infection in Largemouth Bass (Micropterus Salmoides)

DNA vaccines have been widely employed in controlling viral and bacterial infections in mammals and teleost fish. Co-injection of molecular adjuvants, including chemokines, cytokines, and immune co-stimulatory molecules, is one of the potential strategies used to improve DNA vaccine efficacy. In mammals and teleost fish, interleukin-34 (IL-34) had been described as a multifunctional cytokine and its immunological role had been confirmed; however, the adjuvant capacity of IL-34 remains to be elucidated. In this study, IL-34 was identified in largemouth bass. A recombinant plasmid of IL-34 (pcIL-34) was constructed and co-administered with a DNA vaccine encoding hypoxic response protein 1 (Hrp1; pcHrp1) to evaluate the adjuvant capacity of pcIL-34 against Nocardia seriolae infection. Our results indicated that pcIL-34 co-injected with pcHrp1 not only triggered innate immunity and a specific antibody response, but also enhanced the mRNA expression level of immune-related genes encoding for cytokines, chemokines, and humoral and cell-mediated immunity. Moreover, pcIL-34 enhanced the protection of pcHrp1 against N. seriolae challenge and conferred the relative percent survival of 82.14%. Collectively, IL-34 is a promising adjuvant in a DNA vaccine against nocardiosis in fish.

Structural, glycosylation and antigenic variation between 2019 novel coronavirus (2019-nCoV) and SARS coronavirus (SARS-CoV)

The emergence of 2019 novel coronavirus (2019-nCoV) is of global concern and might have emerged from RNA recombination among existing coronaviruses. CoV spike (S) protein which is crucial for receptor binding, membrane fusion via conformational changes, internalization of the virus, host tissue tropism and comprises crucial targets for vaccine development, remain largely uncharacterized. Therefore, the present study has been planned to determine the sequence variation, structural and antigenic divergence of S glycoprotein which may be helpful for the management of 2019-nCoV infection. The sequences of spike glycoprotein of 2019-nCoV and SARS coronavirus (SARS-CoV) were used for the comparison. The sequence variations were determined using EMBOSS Needle pairwise sequence alignment tools. The variation in glycosylation sites was predicted by NetNGlyc 1.0 and validated by N-GlyDE server. Antigenicity was predicted by NetCTL 1.2 and validated by IEDB Analysis Resource server. The structural divergence was determined by using SuperPose Version 1.0 based on cryo-EM structure of the SARS coronavirus spike glycoprotein. Our data suggests that 2019-nCoV is newly spilled coronavirus into humans in China is closely related to SARS-CoV, which has only 12.8% of difference with SARS-CoV in S protein and has 83.9% similarity in minimal receptor-binding domain with SARS-CoV. Addition of a novel glycosylation sites were observed in 2019-nCoV. In addition, antigenic analysis proposes that great antigenic differences exist between both the viral strains, but some of the epitopes were found to be similar between both the S proteins. In spite of the variation in S protein amino acid composition, we found no significant difference in their structures. Collectively, for the first time our results exhibit the emergence of human 2019-nCoV is closely related to predecessor SARS-CoV and provide the evidence that 2019-nCoV uses various novel glycosylation sites as SARS-CoV and may have a potential to become pandemic owing its antigenic discrepancy. Further, demonstration of novel Cytotoxic T lymphocyte epitopes may impart opportunities for the development of peptide based vaccine for the prevention of 2019-nCoV.