In silico platform for prediction of N-, O- and C-glycosites in eukaryotic protein sequences

Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data

Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of only the unlabeled proteins from the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.

Chromosome level assemblies of Nakaseomyces (Candida) bracarensis uncover two distinct clades and define its adhesin repertoire

BACKGROUND

The Nakaseomyces clade is formed by at least nine described species among which three can be pathogenic to humans, namely Nakaseomyces glabratus (Candida glabrata), the second most-common cause of candidiasis worldwide, and two rarer emerging pathogens: Nakaseomyces (Candida) nivarensis and Nakaseomyces (Candida) bracarensis. Early comparative genomics analyses identified parallel expansions of subtelomeric adhesin genes in N. glabratus and N. nivarensis/bracarensis, and suggested possible links with the emergence of the virulence potential in these species. However, as shown for N. glabratus, the proper assessment of subtelomeric genes is hindered by the use of incomplete assemblies and reliance on a single isolate.

RESULTS

Here we sequenced seven N. bracarensis isolates and reconstructed chromosome level assemblies of two divergent strains. We show that N. bracarensis isolates belong to two diverging clades that have slightly different genomic structures. We identified the set of encoded adhesins in the two complete assemblies, and uncovered the presence of a novel adhesin motif, found mainly in N. bracarensis. Our analysis revealed a larger adhesin content in N. bracarensis than previously reported, and similar in size to that of N. glabratus. We confirm the independent adhesin expansion in these two species, which could relate to their different levels of virulence.

CONCLUSION

N. bracarensis clinical isolates belong to at least two differentiated clades. We describe a novel repeat motif found in N. bracarensis adhesins, which helps in their identification. Adhesins underwent independent expansions in N. glabratus and N. bracarensis, leading to repertoires that are qualitatively different but quantitatively similar. Given that adhesins are considered virulence factors, some of the observed differences could contribute to variations in virulence capabilities between N. glabratus and N. bracarensis.

Human organic anion transporting polypeptide 1B3 (OATP1B3) is more heavily N-glycosylated than OATP1B1 in extracellular loops 2 and 5

Human organic anion transporting polypeptide 1B3 (OATP1B3) and 1B1 are two liver-specific and highly homologous uptake transporters, whose structures consist of 12 transmembrane domains. The present study showed that OATP1B3 is more heavily N-glycosylated than OATP1B1 in extracellular loop 2 (EL2) and EL5. OATP1B3 has six N-glycosylation sites, namely N134, N145, N151, N445, N503, and N516, which is twice of that of OATP1B1. Single removal of individual N-glycans seems to have minimal influence on the surface expression and function of OATP1B3. However, simultaneous removal of all N-glycans will lead to OATP1B3's large retention in the endoplasmic reticulum and cellular degradation and thus significantly disrupts its surface expression. While N-glycosylation plays a crucial role in the surface expression of OATP1B3, it also has some effect on the transport function of OATP1B3 per se, which is not due to a decrease of substrate binding affinity but due to a reduced transporter's turnover number. Taken together, N-glycosylation is essential for normal surface expression and function of OATP1B3. Its disruption by some liver diseases such as NASH might alter the pharmacokinetic/pharmacodynamic properties of OATP1B3's substrate drugs.

Towards a seamless product and process development workflow for recombinant proteins produced by plant molecular farming

Plant molecular farming (PMF) has been promoted as a fast, efficient and cost-effective alternative to bacteria and animal cells for the production of biopharmaceutical proteins. Numerous plant species have been tested to produce a wide range of drug candidates. However, PMF generally lacks a systematic, streamlined and seamless workflow to continuously fill the product pipeline. Therefore, it is currently unable to compete with established platforms in terms of routine, throughput and horizontal integration (the rapid translation of product candidates to preclinical and clinical development). Individual management decisions, limited funding and a lack of qualified production capacity can hinder the execution of such projects, but we also lack suitable technologies for sample handling and data management. This perspectives article will highlight current bottlenecks in PMF and offer potential solutions that combine PMF with existing technologies to build an integrated facility of the future for product development, testing, manufacturing and clinical translation. Ten major bottlenecks have been identified and are discussed in turn: automated cloning and simplified transformation options, reproducibility of bacterial cultivation, bioreactor integration with automated cell handling, options for rapid mid-scale candidate and product manufacturing, interconnection with (group-specific or personalized) clinical trials, diversity of (post-)infiltration conditions, development of downstream processing platforms, continuous process operation, compliance of manufacturing conditions with biosafety regulations, scaling requirements for cascading biomass.

HOTGpred: Enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach

O-linked glycosylation is a complex post-translational modification (PTM) in human proteins that plays a critical role in regulating various cellular metabolic and signaling pathways. In contrast to N-linked glycosylation, O-linked glycosylation lacks specific sequence features and maintains an unstable core structure. Identifying O-linked threonine glycosylation sites (OTGs) remains challenging, requiring extensive experimental tests. While bioinformatics tools have emerged for predicting OTGs, their reliance on limited conventional features and absence of well-defined feature selection strategies limit their effectiveness. To address these limitations, we introduced HOTGpred (Human O-linked Threonine Glycosylation predictor), employing a multi-stage feature selection process to identify the optimal feature set for accurately identifying OTGs. Initially, we assessed 25 different feature sets derived from various pretrained protein language model (PLM)-based embeddings and conventional feature descriptors using nine classifiers. Subsequently, we integrated the top five embeddings linearly and determined the most effective scoring function for ranking hybrid features, identifying the optimal feature set through a process of sequential forward search. Among the classifiers, the extreme gradient boosting (XGBT)-based model, using the optimal feature set (HOTGpred), achieved 92.03 % accuracy on the training dataset and 88.25 % on the balanced independent dataset. Notably, HOTGpred significantly outperformed the current state-of-the-art methods on both the balanced and imbalanced independent datasets, demonstrating its superior prediction capabilities. Additionally, SHapley Additive exPlanations (SHAP) and ablation analyses were conducted to identify the features contributing most significantly to HOTGpred. Finally, we developed an easy-to-navigate web server, accessible at https://balalab-skku.org/HOTGpred/, to support glycobiologists in their research on glycosylation structure and function.

DOTAD: A Database of Therapeutic Antibody Developability

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites

Studies have shown that protein glycosylation in cells reflects the real-time dynamics of biological processes, and the occurrence and development of many diseases are closely related to protein glycosylation. Abnormal protein glycosylation can be used as a potential diagnostic and prognostic marker of a disease, as well as a therapeutic target and a new breakthrough point for exploring pathogenesis. To address the issue of significant differences in the prediction results of previous models for different species, we constructed a hybrid deep learning model N-GlycoPred on the basis of dual-layer convolution, a paired attention mechanism and BiLSTM for accurate identification of N-glycosylation sites. By adopting one-hot encoding or the AAindex, we specifically selected the optimum combination of features and deep learning frameworks for human and mouse to refine the models. Based on six independent test datasets, our N-GlycoPred model achieved an average AUC of 0.9553, which is 0.23% higher than MusiteDeep. The comparison results indicate that our model can serve as a powerful tool for N-glycosylation site prescreening for biological researchers.

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.

Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum

Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.

Use of in silico approaches, synthesis and profiling of Pan-filovirus GP-1,2 preprotein specific antibodies

Intermolecular interactions of protein-protein complexes play a principal role in the process of discovering new substances used in the diagnosis and treatment of many diseases. Among such complexes of proteins, we have to mention antibodies; they interact with specific antigens of two genera of single-stranded RNA viruses belonging to the family Filoviridae-Ebolavirus and Marburgvirus; both cause rare but fatal viral hemorrhagic fever in Africa, with pandemic potential. In this research, we conduct studies aimed at the design and evaluation of antibodies targeting the filovirus glycoprotein precursor GP-1,2 to develop potential targets for the pan-filovirus easy-to-use rapid diagnostic tests. The in silico research using the available 3D structure of the natural antibody-antigen complex was carried out to determine the stability of individual protein segments in the process of its formation and maintenance. The computed free binding energy of the complex and its decomposition for all amino acids allowed us to define the residues that play an essential role in the structure and indicated the spots where potential antibodies can be improved. Following that, the study involved targeting six epitopes of the filovirus GP1,2 with two polyclonal antibodies (pABs) and 14 monoclonal antibodies (mABs). The evaluation conducted using Enzyme Immunoassays tested 62 different sandwich combinations of monoclonal antibodies (mAbs), identifying 10 combinations that successfully captured the recombinant GP1,2 (rGP). Among these combinations, the sandwich option (3G2G12* - (rGP) - 2D8F11) exhibited the highest propensity for capturing the rGP antigen.

Structural analysis and functional evaluation of the disordered ß-hexosyltransferase region from Hamamotoa (Sporobolomyces) singularis

Hamamotoa (Sporobolomyces) singularis codes for an industrially important membrane bound ß-hexosyltransferase (BHT), (BglA, UniprotKB: Q564N5) that has applications in the production of natural fibers such as galacto-oligosaccharides (GOS) and natural sugars found in human milk. When heterologously expressed by Komagataella phaffii GS115, BHT is found both membrane bound and soluble secreted into the culture medium. In silico structural predictions and crystal structures support a glycosylated homodimeric enzyme and the presence of an intrinsically disordered region (IDR) with membrane binding potential within its novel N-terminal region (1-110 amino acids). Additional in silico analysis showed that the IDR may not be essential for stable homodimerization. Thus, we performed progressive deletion analyses targeting segments within the suspected disordered region, to determine the N-terminal disorder region's impact on the ratio of membrane-bound to secreted soluble enzyme and its contribution to enzyme activity. The ratio of the soluble secreted to membrane-bound enzyme shifted from 40% to 53% after the disordered N-terminal region was completely removed, while the specific activity was unaffected. Furthermore, functional analysis of each glycosylation site found within the C-terminal domain revealed reduced total secreted protein activity by 58%-97% in both the presence and absence of the IDR, indicating that glycosylation at all four locations is required by the host for the secretion of active enzyme and independent of the removed disordered N-terminal region. Overall, the data provides evidence that the disordered region only partially influences the secretion and membrane localization of BHT.

Enzymatic hydrolysis of single-use bioplastic items by improved recombinant yeast strains

Single-use bioplastic items pose new challenges for a circular plastics economy as they require different processing than petroleum-based plastics items. Microbial and enzymatic recycling approaches could address some of the pitfalls created by the influx of bioplastic waste. In this study, the recombinant expression of a cutinase-like-enzyme (CLE1) was improved in the yeast Saccharomyces cerevisiae to efficiently hydrolyse several commercial single-use bioplastic items constituting blends of poly(lactic acid), poly(1,4-butylene adipate-co-terephthalate), poly(butylene succinate) and mineral fillers. The hydrolysis process was optimised in controlled bioreactor configurations to deliver substantial monomer concentrations and, ultimately, 29 to 78% weight loss. Product inhibition studies and molecular docking provided insights into potential bottlenecks of the enzymatic hydrolysis process, while FT-IR analysis showed the preferential breakdown of specific polymers in blended commercial bioplastic items. This work constitutes a step towards implementing enzymatic hydrolysis as a circular economy approach for the valorisation of end-of-life single-use bioplastic items.

EMNGly: predicting N-linked glycosylation sites using the language models for feature extraction

MOTIVATION

N-linked glycosylation is a frequently occurring post-translational protein modification that serves critical functions in protein folding, stability, trafficking, and recognition. Its involvement spans across multiple biological processes and alterations to this process can result in various diseases. Therefore, identifying N-linked glycosylation sites is imperative for comprehending the mechanisms and systems underlying glycosylation. Due to the inherent experimental complexities, machine learning and deep learning have become indispensable tools for predicting these sites.

RESULTS

In this context, a new approach called EMNGly has been proposed. The EMNGly approach utilizes pretrained protein language model (Evolutionary Scale Modeling) and pretrained protein structure model (Inverse Folding Model) for features extraction and support vector machine for classification. Ten-fold cross-validation and independent tests show that this approach has outperformed existing techniques. And it achieves Matthews Correlation Coefficient, sensitivity, specificity, and accuracy of 0.8282, 0.9343, 0.8934, and 0.9143, respectively on a benchmark independent test set.

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.

Effects of PmDOME and PmSTAT knockdown on white spot syndrome virus infection in Penaeus monodon

Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway plays an important role in antiviral immunity. This research reports the full-length DOME receptor gene in Penaeus monodon (PmDOME) and examines the effects of PmDOME and PmSTAT silencing on immune-related gene expressions in shrimp hemocytes during white spot syndrome virus (WSSV) infection. PmDOME and PmSTAT were up-regulated in shrimp hemocytes upon WSSV infection. Suppression of PmDOME and PmSTAT showed significant impacts on the expression levels of ProPO2 (melanization), Vago5 (interferon-like protein) and several antimicrobial peptides, including ALFPm3, Penaeidin3, CrustinPm1 and CrustinPm7. Silencing of PmDOME and PmSTAT reduced WSSV copy numbers and delayed the cumulative mortality caused by WSSV. We postulated that suppression of the JAK/STAT signaling pathway may activate the proPO, IFN-like antiviral cytokine and AMP production, resulting in a delay of WSSV-related mortality.

Genetic control of N-glycosylation of human blood plasma proteins

Glycosylation is an important protein modification, which influences the physical and chemical properties as well as biological function of these proteins. Large-scale population studies have shown that the levels of various plasma protein N-glycans are associated with many multifactorial human diseases. Observed associations between protein glycosylation levels and human diseases have led to the conclusion that N-glycans can be considered a potential source of biomarkers and therapeutic targets. Although biochemical pathways of glycosylation are well studied, the understanding of the mechanisms underlying general and tissue-specific regulation of these biochemical reactions in vivo is limited. This complicates both the interpretation of the observed associations between protein glycosylation levels and human diseases, and the development of glycan-based biomarkers and therapeutics. By the beginning of the 2010s, high-throughput methods of N-glycome profiling had become available, allowing research into the genetic control of N-glycosylation using quantitative genetics methods, including genome-wide association studies (GWAS). Application of these methods has made it possible to find previously unknown regulators of N-glycosylation and expanded the understanding of the role of N-glycans in the control of multifactorial diseases and human complex traits. The present review considers the current knowledge of the genetic control of variability in the levels of N-glycosylation of plasma proteins in human populations. It briefly describes the most popular physical-chemical methods of N-glycome profiling and the databases that contain genes involved in the biosynthesis of N-glycans. It also reviews the results of studies of environmental and genetic factors contributing to the variability of N-glycans as well as the mapping results of the genomic loci of N-glycans by GWAS. The results of functional in vitro and in silico studies are described. The review summarizes the current progress in human glycogenomics and suggests possible directions for further research.

Re-mining serum proteomics data reveals extensive post-translational modifications upon Zika and dengue infection

Zika virus (ZIKV) and dengue virus (DENV) are two closely related flaviviruses with similar symptoms. However, due to the implications of ZIKV infections for pregnancy outcomes, understanding differences in their molecular impact on the host is of high interest. Viral infections change the host proteome, including post-translational modifications. As modifications are diverse and of low abundance, they typically require additional sample processing which is not feasible for large cohort studies. Therefore, we tested the potential of next-generation proteomics data in its ability to prioritize specific modifications for later analysis. We re-mined published mass spectra from 122 serum samples from ZIKV and DENV patients for the presence of phosphorylated, methylated, oxidized, glycosylated/glycated, sulfated, and carboxylated peptides. We identified 246 modified peptides with significantly differential abundance in ZIKV and DENV patients. Amongst these, methionine-oxidized peptides from apolipoproteins and glycosylated peptides from immunoglobulin proteins were more abundant in ZIKV patient serum and generate hypotheses on the potential roles of the modification in the infection. The results demonstrate how data-independent acquisition techniques can help prioritize future analyses of peptide modifications.

O-GlyThr: Prediction of human O-linked threonine glycosites using multi-feature fusion

O-linked glycosylation is one of the most complex post-translational modifications (PTM) of human proteins modulating various cellular metabolic and signaling pathways. Unlike N-glycosylation, the O-glycosylation has nonspecific sequence features and nonstable glycan core structure, which makes identification of O-glycosites more challenging either by experimental or computational methods. Biochemical experiments to identify O-glycosites in batches are technically and economically demanding. Therefore, development of computation-based methods is greatly warranted. This study constructed a prediction model based on feature fusion for O-glycosites linked to the threonine residues in Homo sapiens. In the training model, we collected and sorted out high-quality human protein data with O-linked threonine glycosites. Seven feature coding methods were fused to represent the sample sequence. By comparison of different algorithms, random forest was selected as the final classifier to construct the classification model. Through 5-fold cross-validation, the proposed model, namely O-GlyThr, performed satisfactorily on both training set (AUC: 0.9308) and independent validation dataset (AUC: 0.9323). Compared with previously published predictors, O-GlyThr achieved the highest ACC of 0.8475 on the independent test dataset. These results demonstrated the high competency of our predictor in identifying O-glycosites on threonine residues. Furthermore, a user-friendly webserver named O-GlyThr (http://cbcb.cdutcm.edu.cn/O-GlyThr/) was developed to assist glycobiologists in the research associated with glycosylation structure and function.

MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases

We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enable simultaneous prediction of multiple PTMs with high performance and computation efficiency. MIND-S also features an interpretation module, which provides the relevance of each amino acid for making the predictions and is validated with known motifs. The interpretation module also captures PTM patterns without any supervision. Furthermore, MIND-S enables examination of mutation effects on PTMs. We document a workflow, its applications to 26 types of PTMs of two datasets consisting of ∼50,000 proteins, and an example of MIND-S identifying a PTM-interrupting SNP with validation from biological data. We also include use case analyses of targeted proteins. Taken together, we have demonstrated that MIND-S is accurate, interpretable, and efficient to elucidate PTM-relevant biological processes in health and diseases.

Exploring the Potential of Iminosugars as Antivirals for Crimean-Congo Haemorrhagic Fever Virus, Using the Surrogate Hazara Virus: Liquid-Chromatography-Based Mapping of Viral N-Glycosylation and In Vitro Antiviral Assays

Crimean-Congo haemorrhagic fever virus (CCHFV) is a pathogen of increasing public health concern, being a widely distributed arbovirus and the causative agent of the potentially fatal Crimean-Congo haemorrhagic fever. Hazara virus (HAZV) is a genetically and serologically related virus that has been proposed as a surrogate for antiviral and vaccine testing for CCHFV. Glycosylation analysis of HAZV has been limited; first, we confirmed for the first time the occupation of two N-glycosylation sites in the HAZV glycoprotein. Despite this, there was no apparent antiviral efficacy of a panel of iminosugars against HAZV, as determined by quantification of the total secretion and infectious virus titres produced following infection of SW13 and Vero cells. This lack of efficacy was not due to an inability of deoxynojirimycin (DNJ)-derivative iminosugars to access and inhibit endoplasmic reticulum α-glucosidases, as demonstrated by free oligosaccharide analysis in uninfected and infected SW13 and uninfected Vero cells. Even so, iminosugars may yet have potential as antivirals for CCHFV since the positions and importance of N-linked glycans may differ between the viruses, a hypothesis requiring further evaluation.

Bioinformatics and computational analyses of kidney stone modulatory proteins lead to solid experimental evidence and therapeutic potential

In recent biomedical research, bioinformatics and computational analyses have played essential roles for examining experimental findings and database information. Several bioinformatic tools have been developed and made publicly available for analyzing protein sequence, structure, functional motif/domain, and interactions network. Such properties are very helpful to define biochemical and functional roles of the protein(s) of interest. During the past few decades, bioinformatics and computational biotechnology have been widely applied to kidney stone research. This review summarizes commonly used tools and evidence of bioinformatics and computational biotechnology applied to kidney stone disease (KSD) with special emphasis on analyses of the stone modulatory proteins that play critical roles in kidney stone formation. Such analyses lead to solid experimental evidence to demonstrate mechanisms underlying their stone modulatory activities. The findings obtained from such analyses may also lead to better understanding of KSD pathogenesis and to further development of new therapeutic and preventive strategies.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Aglycosylated extracellular loop of inwardly rectifying potassium channel 4.1 (KCNJ10) provides a target for autoimmune neuroinflammation

Multiple sclerosis is an autoimmune disease of the central nervous system. Yet, the autoimmune targets are still undefined. The extracellular e1 sequence of KCNJ10, the inwardly rectifying potassium channel 4.1, has been subject to fierce debate for its role as a candidate autoantigen in multiple sclerosis. Inwardly rectifying potassium channel 4.1 is expressed in the central nervous system but also in peripheral tissues, raising concerns about the central nervous system-specificity of such autoreactivity. Immunization of C57Bl6/J female mice with the e1 peptide (amino acids 83-120 of Kir4.1) induced anti-e1 immunoglobulin G- and T-cell responses and promoted demyelinating encephalomyelitis with B cell central nervous system enrichment in leptomeninges and T cells/macrophages in central nervous system parenchyma from forebrain to spinal cord, mostly in the white matter. Within our cohort of multiple sclerosis patients (n = 252), 6% exhibited high anti-e1 immunoglobulin G levels in serum as compared to 0.7% in the control cohort (n = 127; P = 0.015). Immunolabelling of inwardly rectifying potassium channel 4.1-expressing white matter glia with the anti-e1 serum from immunized mice increased during murine autoimmune neuroinflammation and in multiple sclerosis white matter as compared with controls. Strikingly, the mouse and human anti-e1 sera labelled astrocytoma cells when N-glycosylation was blocked with tunicamycin. Western blot confirmed that neuroinflammation induces Kir4.1 expression, including its shorter aglycosylated form in murine experimental autoencephalomyelitis and multiple sclerosis. In addition, recognition of inwardly rectifying potassium channel 4.1 using mouse anti-e1 serum in Western blot experiments under unreduced conditions or in cells transfected with the N-glycosylation defective N104Q mutant as compared to the wild type further suggests that autoantibodies target an e1 conformational epitope in its aglycosylated form. These data highlight the e1 sequence of inwardly rectifying potassium channel 4.1 as a valid central nervous system autoantigen with a disease/tissue-specific post-translational antigen modification as potential contributor to autoimmunity in some multiple sclerosis patients.

Species-specific differences in NPC1 protein trafficking govern therapeutic response in Niemann-Pick type C disease

The folding and trafficking of transmembrane glycoproteins are essential for cellular homeostasis and are compromised in many diseases. In Niemann-Pick type C disease, a lysosomal disorder characterized by impaired intracellular cholesterol trafficking, the transmembrane glycoprotein NPC1 misfolds due to disease-causing missense mutations. While mutant NPC1 has emerged as a robust target for proteostasis modulators, drug development efforts have been unsuccessful in mouse models. Here, we demonstrated unexpected differences in trafficking through the medial Golgi between mouse and human I1061T-NPC1, a common disease-causing mutant. We established that these distinctions are governed by differences in the NPC1 protein sequence rather than by variations in the endoplasmic reticulum-folding environment. Moreover, we demonstrated direct effects of mutant protein trafficking on the response to small molecules that modulate the endoplasmic reticulum-folding environment by affecting Ca++ concentration. Finally, we developed a panel of isogenic human NPC1 iNeurons expressing WT, I1061T-, and R934L-NPC1 and demonstrated their utility in testing these candidate therapeutics. Our findings identify important rules governing mutant NPC1's response to proteostatic modulators and highlight the importance of species- and mutation-specific responses for therapy development.

Analysis of amplification and association polymorphisms in the bovine beta-defensin 129 (BBD129) gene revealed its function in bull fertility

β-defensins are adsorbable on the sperm surface in the male reproductive tract (MRT) and enhance sperm functional characteristics. The beta-defensin 129 (DEFB129) antimicrobial peptide is involved in sperm maturation, motility, and fertilization. However, its role in bovine fertility has not been well investigated. This study examines the relationship between the bovine BBD129 gene and Bos indicus x Bos taurus bull fertility. The complete coding sequence of BBD129 mRNA was identified by RNA Ligase Mediated-Rapid Amplification of cDNA End (RLM-RACE) and Sanger sequencing methodologies. It consisted of 582 nucleotides (nts) including 5' untranslated region (UTR) (46nts) and 3'UTR (23nts). It conserves all beta-defensin-like features. The expression level of BBD129 was checked by RT-qPCR and maximal expression was detected in the corpus-epididymis region compared to other parts of MRT. Polymorphism in BBD129 was also confirmed by Sanger sequencing of 254 clones from 5 high fertile (HF) and 6 low fertile (LF) bulls at two positions, 169 T > G and 329A > G, which change the S57A and N110S in the protein sequence respectively. These two mutations give rise to four types of BBD129 haplotypes. The non-mutated TA-BBD129 (169 T/329A) haplotype was substantially more prevalent among high-fertile bulls (P < 0.005), while the double-site mutated GG-BBD129 (169 T > G/329A > G) haplotype was significantly more prevalent among low-fertile bulls (P < 0.005). The in silico analysis confirmed that the polymorphism in BBD129 results in changes in mRNA secondary structure, protein conformations, protein stability, extracellular-surface availability, post-translational modifications (O-glycosylation and phosphorylation), and affects antibacterial and immunomodulatory capabilities. In conclusion, the mRNA expression of BBD129 in the MRT indicates its region-specific dynamics in sperm maturation. BBD129 polymorphisms were identified as the deciding elements accountable for the changed proteins with impaired functionality, contributing to cross-bred bulls' poor fertility.

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.

MMP7 cleavage of amino-terminal CD95 death receptor switches signaling toward non-apoptotic pathways

CD95 is a death receptor that can promote oncogenesis through molecular mechanisms that are not fully elucidated. Although the mature CD95 membrane receptor is considered to start with the arginine at position 17 after elimination of the signal peptide, this receptor can also be cleaved by MMP7 upstream of its leucine at position 37. This post-translational modification occurs in cancer cells but also in normal cells such as peripheral blood leukocytes. The non-cleaved CD95 amino-terminal region consists in a disordered domain and its in silico reconstitution suggests that it might contribute to receptor aggregation and thereby, regulate the downstream death signaling pathways. In agreement with this molecular modeling analysis, the comparison of CD95-deficient cells reconstituted with full-length or N-terminally truncated CD95 reveals that the loss of the amino-terminal region of CD95 impairs the initial steps of the apoptotic signal while favoring the induction of pro-survival signals, including the PI3K and MAPK pathways.

Targeting the YXXΦ Motifs of the SARS Coronaviruses 1 and 2 ORF3a Peptides by In Silico Analysis to Predict Novel Virus-Host Interactions

The emerging SARS-CoV and SARS-CoV-2 belong to the family of "common cold" RNA coronaviruses, and they are responsible for the 2003 epidemic and the current pandemic with over 6.3 M deaths worldwide. The ORF3a gene is conserved in both viruses and codes for the accessory protein ORF3a, with unclear functions, possibly related to viral virulence and pathogenesis. The tyrosine-based YXXΦ motif (Φ: bulky hydrophobic residue-L/I/M/V/F) was originally discovered to mediate clathrin-dependent endocytosis of membrane-spanning proteins. Many viruses employ the YXXΦ motif to achieve efficient receptor-guided internalisation in host cells, maintain the structural integrity of their capsids and enhance viral replication. Importantly, this motif has been recently identified on the ORF3a proteins of SARS-CoV and SARS-CoV-2. Given that the ORF3a aa sequence is not fully conserved between the two SARS viruses, we aimed to map in silico structural differences and putative sequence-driven alterations of regulatory elements within and adjacently to the YXXΦ motifs that could predict variations in ORF3a functions. Using robust bioinformatics tools, we investigated the presence of relevant post-translational modifications and the YXXΦ motif involvement in protein-protein interactions. Our study suggests that the predicted YXXΦ-related features may confer specific-yet to be discovered-functions to ORF3a proteins, significant to the new virus and related to enhanced propagation, host immune regulation and virulence.

Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models

Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, machine learning models largely enriched the prediction of PTMs. However, most machine learning models only rely on protein sequence and little structural information. The lack of the systematic dynamics analysis underlying PTMs largely limits the PTM functional predictions. In this research, we present two dynamics-centric deep learning models, namely, cDL-PAU and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based features to elucidate the molecular basis and underlying functional landscape of PTMs. cDL-PAU achieved satisfactory area under the curve (AUC) scores of 0.804-0.888 for predicting phosphorylation, acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos) sites, displaying reliable improvements. Through a feature selection, the dynamics-based coupling and commute ability show large contributions in discovering PAU sites and FuncPhos sites, suggesting the allosteric propensity for important PTMs. The application of cDL-FuncPhos in three oncoproteins not only corroborates its strong performance in FuncPhos prioritization but also gains insight into the physical basis for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos are available at https://github.com/ComputeSuda/PTM_ML.

Advanced strategies in glycosylation prediction and control during biopharmaceutical development: Avenues toward Industry 4.0

Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multi-variate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm. Nonetheless, the development of smart algorithms and omics-enabled complete cell characterization have made it possible to predict glycan profiles beforehand, and manipulate process variables accordingly. This review thus discusses the various approaches available for control and prediction of glycosylation in biopharmaceuticals. Further, the futuristic goal of integrating such technologies is discussed in order to attain an automated and digitized continuous bioprocess for control of glycosylation. Given, control of a process as complex as glycosylation requires intense monitoring intervention, we examine the current technologies that enable automation. Finally, we discuss the challenges and the technological gap that currently limits incorporation of an automated process in routine bio-manufacturing, with a glimpse into the economic bearing. This article is protected by copyright. All rights reserved.

Insights into polyamine metabolism: homospermidine is double-oxidized in two discrete steps by a single copper-containing amine oxidase in pyrrolizidine alkaloid biosynthesis

Polyamines are important metabolites in plant development and abiotic and biotic stress responses. Copper-containing amine oxidases (CuAOs) are involved in the regulation of polyamine levels in the cell. CuAOs oxidize primary amines to their respective aldehydes and hydrogen peroxide. In plants, aldehydes are intermediates in various biosynthetic pathways of alkaloids. CuAOs are thought to oxidize polyamines at only one of the primary amino groups, a process frequently resulting in monocyclic structures. These oxidases have been postulated to be involved in pyrrolizidine alkaloid (PA) biosynthesis. Here, we describe the identification and characterization of homospermidine oxidase (HSO), a CuAO of Heliotropium indicum (Indian heliotrope), involved in PA biosynthesis. Virus-induced gene silencing of HSO in H. indicum leads to significantly reduced PA levels. By in vitro enzyme assays after transient in planta expression, we show that this enzyme prefers Hspd over other amines. Nuclear magnetic resonance spectroscopy and mass spectrometry analyses of the reaction products demonstrate that HSO oxidizes both primary amino groups of homospermidine (Hspd) to form a bicyclic structure, 1-formylpyrrolizidine. Using tracer feeding, we have further revealed that 1-formylpyrrolizidine is an intermediate in the biosynthesis of PAs. Our study therefore establishes that HSO, a canonical CuAO, catalyzes the second step of PA biosynthesis and provides evidence for an undescribed and unusual mechanism involving two discrete steps of oxidation that might also be involved in the biosynthesis of complex structures in other alkaloidal pathways.

Identification of a New Endo-β-1,4-xylanase Prospected from the Microbiota of the Termite Heterotermes tenuis

Xylanases are hemicellulases that break down xylan to soluble pentoses. They are used for industrial purposes, such as paper whitening, beverage clarification, and biofuel production. The second-generation bioethanol production is hindered by the enzymatic hydrolysis step of the lignocellulosic biomass, due to the complex arrangement established among its constituents. Xylanases can potentially increase the production yield by improving the action of the cellulolytic enzyme complex. We prospected endo-β-1,4-xylanases from meta-transcriptomes of the termite Heterotermes tenuis. In silico structural characterization and functional analysis of an endo-β-1,4-xylanase from a symbiotic protist of H. tenuis indicate two active sites and a substrate-binding groove needed for the catalytic activity. No N-glycosylation sites were found. This endo-β-1,4-xylanase was recombinantly expressed in Pichia pastoris and Escherichia coli cells, presenting a molecular mass of approximately 20 kDa. Enzymatic activity assay using recombinant endo-β-1,4-xylanase was also performed on 1% xylan agar stained with Congo red at 30 °C and 40 °C. The enzyme expressed in both systems was able to hydrolyze the substrate xylan, becoming a promising candidate for further analysis aiming to determine its potential for application in industrial xylan degradation processes.

Development of Viral-Vectored Vaccines and Virus Replicon Particle-Based Neutralisation Assay against Mayaro Virus

Mayaro virus (MAYV) is an emerging alphavirus causing acute febrile illness associated with chronic polyarthralgia. Although MAYV is currently restricted to tropical regions in South America around the Amazon basin, it has the potential to spread globally by Aedes species mosquitoes. In addition, there are currently no specific therapeutics or licenced vaccines against MAYV infection. We have previously shown that an adenovirus based Mayaro vaccine (ChAdOx1 May) was able to provide full protection against MAYV challenge in vaccinated A129 mice and induced high neutralising antibody titres. In this study, we have constructed a replication deficient simian adenovirus (ChAdOx2) and a Modified Ankara Virus (MVA) based vaccine expressing the MAYV structural cassette (sMAYV) similar to ChAdOx1 May, and characterised recombinant MAYV E2 glycoprotein expressed in a mammalian system for immune monitoring. We demonstrate that ChAdOx2 May was able to induce high antibody titres similar to ChAdOx1 May, and MVA May was shown to be an effective boosting strategy following prime vaccination with ChAdOx1 or ChAdOx2 May. In order to measure MAYV neutralising ability, we have developed a virus replicon particle-based neutralisation assay which effectively detected neutralising antibodies against MAYV. In summary, our study indicates the potential for further clinical development of the viral vectored MAYV vaccines against MAYV infections.

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/ .

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

Functions of Glycosylation and Related Web Resources for Its Prediction

Glycosylation involves the attachment of carbohydrate sugar chains, or glycans, onto an amino acid residue of a protein. These glycans are often branched structures and serve to modulate the function of proteins. Glycans are synthesized through a complex process of enzymatic reactions that occur in the Golgi apparatus in mammalian systems. Because there is currently no sequencer for glycans, technologies such as mass spectrometry is used to characterize glycans in a biological sample to ascertain its glycome. This is a tedious process that requires high levels of expertise and equipment. Thus, the enzymes that work on glycans, called glycogenes or glycoenzymes, have been studied to better understand glycan function. With the development of glycan-related databases and a glycan repository, bioinformatics approaches have attempted to predict the glycosylation pathway and the glycosylation sites on proteins. This chapter introduces these methods and related Web resources for understanding glycan function.

A novel CD34-derived hinge for rapid and efficient detection and enrichment of CAR T cells

Immunotherapy including chimeric antigen receptor (CAR) T cell therapy has revolutionized modern cancer therapy and has achieved remarkable remission and survival rates for several malignancies with historically dismal outcomes. The hinge of the CAR connects the antigen binding to the transmembrane domain and can be exploited to confer features to CAR T cells including additional stimulation, targeted elimination or detection and enrichment of the genetically modified cells. For establishing a novel hinge derived from human CD34, we systematically tested CD34 fragments of different lengths, all containing the binding site of the QBend-10 monoclonal antibody, in a FMC63-based CD19 CAR lentiviral construct. A final construct of 99 amino acids called C6 proved to be the best candidate for flow cytometry-based detection of CAR T cells and >95% enrichment of genetically modified T cells on MACS columns. The C6 hinge was functionally indistinguishable from the commonly used CD8α hinge in vitro as well as in in vivo experiments in NSG mice. We also showed that the C6 hinge can be used for a variety of different CARs and mediates high killing efficacy without unspecific activation by target antigen-negative cells, thus making C6 ideally suited as a universal hinge for CARs for clinical applications.

First report on molecular docking analysis and drug resistance substitutions to approved HCV NS5A and NS5B inhibitors amongst Iranian patients

Background

NS5A and NS5B proteins of hepatitis C virus (HCV) are the main targets of compounds that directly inhibit HCV infections. However, the emergence of resistance-associated substitutions (RASs) may cause substantial reductions in susceptibility to inhibitors.

Methods

Viral load and genotyping were determined in eighty-seven naïve HCV-infected patients, and the amplified NS5A and NS5B regions were sequenced by Sanger sequencing. In addition, physicochemical properties, structural features, immune epitopes, and inhibitors-protein interactions of sequences were analyzed using several bioinformatics tools.

Results

Several amino acid residue changes were found in NS5A and NS5B proteins; however, we did not find any mutations related to resistance to the treatment in NS5B. Different phosphorylation and few glycosylation sites were assessed. Disulfide bonds were identified in both proteins that had a significant effect on the function and structure of HCV proteins. Applying reliable software to predict B-cell epitopes, 3 and 5 regions were found for NS5A and NS5B, respectively, representing a considerable potential to induce the humoral immune system. Docking analysis determined amino acids involved in the interaction of inhibitors and mentioned proteins may not decrease the drug efficiency.

Conclusions

Strong interactions between inhibitors, NS5A and NS5B proteins and the lack of efficient drug resistance mutations in the analyzed sequences may confirm the remarkable ability of NS5A and NS5B inhibitors to control HCV infection amongst Iranian patients. The results of bioinformatics analysis could unveil all features of both proteins, which can be beneficial for further investigations on HCV drug resistance and designing novel vaccines.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01988-y.

O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine

O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal-Wallis (K-W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.

Machine learning meets omics: applications and perspectives

The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.

Differential contribution of two organelles of endosymbiotic origin to iron-sulfur cluster synthesis and overall fitness in Toxoplasma

Iron-sulfur (Fe-S) clusters are one of the most ancient and ubiquitous prosthetic groups, and they are required by a variety of proteins involved in important metabolic processes. Apicomplexan parasites have inherited different plastidic and mitochondrial Fe-S clusters biosynthesis pathways through endosymbiosis. We have investigated the relative contributions of these pathways to the fitness of Toxoplasma gondii, an apicomplexan parasite causing disease in humans, by generating specific mutants. Phenotypic analysis and quantitative proteomics allowed us to highlight notable differences in these mutants. Both Fe-S cluster synthesis pathways are necessary for optimal parasite growth in vitro, but their disruption leads to markedly different fates: impairment of the plastidic pathway leads to a loss of the organelle and to parasite death, while disruption of the mitochondrial pathway trigger differentiation into a stress resistance stage. This highlights that otherwise similar biochemical pathways hosted by different sub-cellular compartments can have very different contributions to the biology of the parasites, which is something to consider when exploring novel strategies for therapeutic intervention.

Economizing the lignocellulosic hydrolysis process using heterologously expressed auxiliary enzymes feruloyl esterase D (CE1) and β-xylosidase (GH43) derived from thermophilic fungi Scytalidium thermophilum

Two lignocellulolytic accessory enzymes, feruloyl esterase D (FAED_SCYTH) and β-xylosidase (XYL43B_SCYTH) were cloned and produced in the Pichia pastoris X33 as host. The molecular weight of recombinant enzymes FAED_SCYTH and XYL43B_SCYTH were ~ 31 and 40 kDa, respectively. FAED_SCYTH showed optimal activity at pH 6.0, 60 °C; and XYL43B_SCYTH at pH 7.0, 50 °C. FAED_SCYTH and XYL43B_SCYTH exhibited t_1/2: 4 and 0.5 h, respectively (50 °C, pH 5.0). The β-xylosidase was bi-functional with pronounced activity against pNP-α-arabinofuranoside besides being highly xylose tolerant (retaining ~ 97% activity in the presence of 700 mM xylose). Cocktails prepared using these enzymes along with AA9 protein (PMO9D_SCYTH) and commercial cellulase CellicCTec2, showed improved hydrolysis of the pre-treated lignocellulosic biomass. Priming of pre-treated lignocellulosic biomass with these accessory enzymes was found to further enhance the hydrolytic potential of CellicCTec2 promising to reduce the enzyme load and cost required for obtaining sugars from biorefinery relevant pre-treated substrates.

Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

Characterization of New Allergens from the Venom of the European Paper Wasp Polistes dominula

Discriminating Polistes dominula and Vespula spp. venom allergy is of growing importance worldwide, as systemic reactions to either species’ sting can lead to severe outcomes. Administering the correct allergen-specific immunotherapy is therefore a prerequisite to ensure the safety and health of venom-allergic patients. Component-resolved diagnostics of Hymenoptera venom allergy might be improved by adding additional allergens to the diagnostic allergen panel. Therefore, three potential new allergens from P. dominula venom—immune responsive protein 30 (IRP30), vascular endothelial growth factor C (VEGF C) and phospholipase A2 (PLA2)—were cloned, recombinantly produced and biochemically characterized. Sera sIgE titers of Hymenoptera venom-allergic patients were measured in vitro to assess the allergenicity and potential cross-reactivity of the venom proteins. IRP30 and VEGF C were classified as minor allergens, as sensitization rates lay around 20–40%. About 50% of P. dominula venom-allergic patients had measurable sIgE titers directed against PLA2 from P. dominula venom. Interestingly, PLA2 was unable to activate basophils of allergic patients, questioning its role in the context of clinically relevant sensitization. Although the obtained results hint to a questionable benefit of the characterized P. dominula venom proteins for improved diagnosis of venom-allergic patients, they can contribute to a deeper understanding of the molecular mechanisms of Hymenoptera venoms and to the identification of factors that determine the allergenic potential of proteins.

Cloning, characterization and expression of a gene encoding endo-1, 4- β-xylanase from the fungus Termitomyces clypeatus

Enzymatic degradation of hemi-cellulosic substrates has gained plenty of industrial attentions recently. Complete enzymatic degradation of complex and recalcitrant hemicellulose requires an enzymatic cocktail consisting primarily of endo-1,4-β-xylanase (xyl), β-xylosidase, arabinofuranosidase etc. This article reports, for the first time, the identification, cloning, expression and partial characterization of a potent endo-1,4- β-xylanase gene (pxyl) from the mushroom Termitomyces clypeatus (TC) in E. coli and S. cerevisiae. The cDNA for pxyl was found to be 678 bp that in turn gives rise to a precursor protein (Pxyl) of 225 amino acids long when cloned in prokaryotic expression vector. To characterize additionally, the cDNA was also expressed in S. cerevisiae. Bioinformatics study predicted that the Pxyl contains a 19 amino acid long leader peptide that enables post translational modifications including glycosylation as well as its efficient secretion in the medium. The recombinant protein has been found to be a member of GH11 family containing two distant glutamic acids as catalytic residues. This report describes yet another new and potent source of xylanase for commercial exploitation by industry in future.

Three-dimensional structure and properties of the giant reed (Arundo donax) lectin (ADL)

Arundo donax lectin (ADL) is a 170 amino acid protein that can be purified from the rhizomes of the giant reed or giant cane exploiting its selective binding to chitin followed by elution with N-acetyl-D-glucosamine. The lectin is listed in the UniProt server, the largest protein sequence database, as an uncharacterized protein with chitin-binding domains (A0A0A9P802). This paper reports the purification, three-dimensional structure and ligand-binding properties of ADL. The lectin is a homodimer in which the two protomers are linked by two disulphide bridges. Each polypeptide chain presents four carbohydrate-binding modules that belong to family 18 (CBM18). A high degree of sequence similarity is observed among the modules present in each protomer. We have determined the X-ray structure of the apo-protein to a resolution of 1.70 Å. The carbohydrate-binding modules, that span a sequence of approximately 40 amino acids, present four internal disulfide bridges a very short antiparallel central beta sheet and three short alpha helices, two on one side of the beta sheet and one on the other. The structures of the complexes of the lectin with N-acetylglucosamine, N-acetyllactosamine, N-acetylneuraminic acid and N-N'diacetylchitobiose reveal that ADL has two primary and two secondary carbohydrate-binding sites per dimer. They are located at the interface between the two protomers and each binding site involves residues of both chains. The lectin presents structural similarity to the wheat germ agglutinin (WGA) family, in particular to isoform 3.

Heat shock protein 60 is a disease-associated sialoglycoprotein in human non-small cell lung cancer

The diagnostic and therapeutic potential of Maackia amurensis agglutinin (MAA) have been reported in various malignancies. Earlier, we have found that MAA specifically interacted with human non-small cell lung-cancer (NSCLC) cells and induced apoptosis in these cells. The present study was designed to identify M. amurensis leukoagglutinin (MAL-I, one of the components of MAA, having the same carbohydrate specificity as MAA) interacting membrane sialoglycoprotein(s) of two subtypes of human NSCLC cell lines. Nine proteins were identified using two-dimensional (2D)-polyacrylamide gel electrophoresis (PAGE) followed by MAL-I-overlay transblotting and matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS). Among these proteins, HSP60 was selected for further characterization. The sialoglycoprotein nature of membrane-HSP60 of NSCLC cell lines was confirmed by its reduced reactivity with MAL-I in Western blots in the presence of GM2 and by dual staining of the cell lines with MAL-I and HSP60-antibody. These findings were further substantiated by enzymatic analysis of membrane-HSP60 as well as in-silico evidence regarding this protein. Our observations were validated by immunohistochemical analysis of both subtypes of NSCLC tissue sections. Membrane-HSP60 was found to be involved in the inhibition of MAL-I-induced morphological alteration of NSCLC cells and also in the proliferation and migration of these cells, indicating the probable role of sialylated membrane-HSP60 in this disease.