Prediction of protein antigenic determinants from amino acid sequences

The oncogenic fusion protein DNAJB1-PRKACA can be specifically targeted by peptide-based immunotherapy in fibrolamellar hepatocellular carcinoma

The DNAJB1-PRKACA fusion transcript is the oncogenic driver in fibrolamellar hepatocellular carcinoma, a lethal disease lacking specific therapies. This study reports on the identification, characterization, and immunotherapeutic application of HLA-presented neoantigens specific for the DNAJB1-PRKACA fusion transcript in fibrolamellar hepatocellular carcinoma. DNAJB1-PRKACA-derived HLA class I and HLA class II ligands induce multifunctional cytotoxic CD8+ and T-helper 1 CD4+ T cells, and their cellular processing and presentation in DNAJB1-PRKACA expressing tumor cells is demonstrated by mass spectrometry-based immunopeptidome analysis. Single-cell RNA sequencing further identifies multiple T cell receptors from DNAJB1-PRKACA-specific T cells. Vaccination of a fibrolamellar hepatocellular carcinoma patient, suffering from recurrent short interval disease relapses, with DNAJB1-PRKACA-derived peptides under continued Poly (ADP-ribose) polymerase inhibitor therapy induces multifunctional CD4+ T cells, with an activated T-helper 1 phenotype and high T cell receptor clonality. Vaccine-induced DNAJB1-PRKACA-specific T cell responses persist over time and, in contrast to various previous treatments, are accompanied by durable relapse free survival of the patient for more than 21 months post vaccination. Our preclinical and clinical findings identify the DNAJB1-PRKACA protein as source for immunogenic neoepitopes and corresponding T cell receptors and provide efficacy in a single-patient study of T cell-based immunotherapy specifically targeting this oncogenic fusion.

Fuzzy spherical truncation-based multi-linear protein descriptors: From their definition to application in structural-related predictions

This study introduces a set of fuzzy spherically truncated three-dimensional (3D) multi-linear descriptors for proteins. These indices codify geometric structural information from kth spherically truncated spatial-(dis)similarity two-tuple and three-tuple tensors. The coefficients of these truncated tensors are calculated by applying a smoothing value to the 3D structural encoding based on the relationships between two and three amino acids of a protein embedded into a sphere. At considering, the geometrical center of the protein matches with center of the sphere, the distance between each amino acid involved in any specific interaction and the geometrical center of the protein can be computed. Then, the fuzzy membership degree of each amino acid from an spherical region of interest is computed by fuzzy membership functions (FMFs). The truncation value is finally a combination of the membership degrees from interacting amino acids, by applying the arithmetic mean as fusion rule. Several fuzzy membership functions with diverse biases on the calculation of amino acids memberships (e.g., Z-shaped (close to the center), PI-shaped (middle region), and A-Gaussian (far from the center)) were considered as well as traditional truncation functions (e.g., Switching). Such truncation functions were comparatively evaluated by exploring: 1) the frequency of membership degrees, 2) the variability and orthogonality analyses among them based on the Shannon Entropy’s and Principal Component’s methods, respectively, and 3) the prediction performance of alignment-free prediction of protein folding rates and structural classes. These analyses unraveled the singularity of the proposed fuzzy spherically truncated MDs with respect to the classical (non-truncated) ones and respect to the MDs truncated with traditional functions. They also showed an improved prediction power by attaining an external correlation coefficient of 95.82% in the folding rate modelling and an accuracy of 100% in distinguishing structural protein classes. These outcomes are better than the ones attained by existing approaches, justifying the theoretical contribution of this report. Thus, the fuzzy spherically truncated-based protein descriptors from MuLiMs-MCoMPAs (http://tomocomd.com/mulims-mcompas) are promising alignment-free predictors for modeling protein functions and properties.

A Promising Tool in Serological Diagnosis: Current Research Progress of Antigenic Epitopes in Infectious Diseases

Infectious diseases, caused by various pathogens in the clinic, threaten the safety of human life, are harmful to physical and mental health, and also increase economic burdens on society. Infections are a complex mechanism of interaction between pathogenic microorganisms and their host. Identification of the causative agent of the infection is vital for the diagnosis and treatment of diseases. Etiological laboratory diagnostic tests are therefore essential to identify pathogens. However, due to its rapidity and automation, the serological diagnostic test is among the methods of great significance for the diagnosis of infections with the basis of detecting antigens or antibodies in body fluids clinically. Epitopes, as a special chemical group that determines the specificity of antigens and the basic unit of inducing immune responses, play an important role in the study of immune responses. Identifying the epitopes of a pathogen may contribute to the development of a vaccine to prevent disease, the diagnosis of the corresponding disease, and the determination of different stages of the disease. Moreover, both the preparation of neutralizing antibodies based on useful epitopes and the assembly of several associated epitopes can be used in the treatment of disease. Epitopes can be divided into B cell epitopes and T cell epitopes; B cell epitopes stimulate the body to produce antibodies and are therefore commonly used as targets for the design of serological diagnostic experiments. Meanwhile, epitopes can fall into two possible categories: linear and conformational. This article reviews the role of B cell epitopes in the clinical diagnosis of infectious diseases.

Evolution-Guided Biosynthesis of Terpenoid Inhibitors

Terpenoids, the largest and most structurally diverse group of natural products, include a striking variety of biologically active compounds, from flavors to medicines. Despite their well-documented biochemical versatility, the evolutionary processes that generate new functional terpenoids are poorly understood and difficult to recapitulate in engineered systems. This study uses a synthetic biochemical objective─a transcriptional system that links the inhibition of protein tyrosine phosphatase 1B (PTP1B), a human drug target, to the expression of a gene for antibiotic resistance in Escherichia coli (E. coli)─to evolve a terpene synthase to produce enzyme inhibitors. Site saturation mutagenesis of poorly conserved residues on γ-humulene synthase (GHS), a promicuous enzyme, yielded mutants that improved fitness (i.e., the antibiotic resistance of E. coli) by reducing GHS toxicity and/or by increasing inhibitor production. Intriguingly, a combination of two mutations enhanced the titer of a minority product─a terpene alcohol that inhibits PTP1B─by over 50-fold, and a comparison of similar mutants enabled the identification of a site where mutations permit efficient hydroxylation. Findings suggest that the plasticity of terpene synthases enables an efficient sampling of structurally distinct starting points for building new functional molecules and provide an experimental framework for exploiting this plasticity in activity-guided screens.

ProteinPrompt: a webserver for predicting protein-protein interactions

Motivation

Protein-protein interactions (PPIs) play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine learning algorithms to calculate specific, currently unknown PPIs. Our tool is designed to quickly and reliably predict contact propensities based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.

Results

We collected and thoroughly filtered a comprehensive database of known binders from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest (RF) algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network (GNN) implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for binders. To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the RF method achieved an accuracy rate of 0.88 and an area under the curve of 0.95. The GNN achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of RF and GNNs reduces the likelihood of errors. The consensus reached an accuracy of 0.89.

Availability and implementation

ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt, where training and test data used to optimize the methods are also available. The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

Prediction of B cell epitopes in proteins using a novel sequence similarity-based method

Prediction of B cell epitopes that can replace the antigen for antibody production and detection is of great interest for research and the biotech industry. Here, we developed a novel BLAST-based method to predict linear B cell epitopes. To that end, we generated a BLAST-formatted database upon a dataset of 62,730 known linear B cell epitope sequences and considered as a B cell epitope any peptide sequence producing ungapped BLAST hits to this database with identity ≥ 80% and length ≥ 8. We examined B cell epitope predictions by this method in tenfold cross-validations in which we considered various types of non-B cell epitopes, including 62,730 peptide sequences with verified negative B cell assays. As a result, we obtained values of accuracy, specificity and sensitivity of 72.54 ± 0.27%, 81.59 ± 0.37% and 63.49 ± 0.43%, respectively. In an independent dataset incorporating 503 B cell epitopes, this method reached accuracy, specificity and sensitivity of 74.85%, 99.20% and 50.50%, respectively, outperforming state-of-the-art methods to predict linear B cell epitopes. We implemented this BLAST-based approach to predict B cell epitopes at http://imath.med.ucm.es/bepiblast.

In vivo study of the immune response to bioengineered spider silk spheres

Bioengineered MS1 silk is derived from major ampullate spidroin 1 (MaSp1) from the spider Nephila clavipes. The MS1 silk was functionalized with the H2.1 peptide to target Her2-overexpressing cancer cells. The immunogenic potential of drug carriers made from MS1-type silks was investigated. The silk spheres were administered to healthy mice, and then (i) the phenotypes of the immune cells that infiltrated the Matrigel plugs containing spheres (implanted subcutaneously), (ii) the presence of silk-specific antibodies (after two intravenous injections of the spheres), (iii) the splenocyte phenotypes and their activity after restimulation ex vivo in terms of proliferation and cytokine secretion (after single intravenous injection of the spheres) were analyzed. Although the immunogenicity of MS1 particles was minor, the H2.1MS1 spheres attracted higher levels of B lymphocytes, induced a higher anti-silk antibody titer, and, after ex vivo restimulation, caused the activation of splenocytes to proliferate and express more IFN-γ and IL-10 compared with the PBS and MS1 groups. Although the H2.1MS1 spheres triggered a certain degree of an immunological response, multiple injections (up to six times) neither hampered the carrier-dependent specific drug delivery nor induced toxicity, as previously indicated in a mouse breast cancer model. Both findings indicate that a drug delivery system based on MS1-type silk has great potential for the treatment of cancer and other conditions.

JmjC Family of Histone Demethylases Form Nuclear Condensates

The Jumonji-C (JmjC) family of lysine demethylases (KDMs) (JMJC-KDMs) plays an essential role in controlling gene expression and chromatin structure. In most cases, their function has been attributed to the demethylase activity. However, accumulating evidence demonstrates that these proteins play roles distinct from histone demethylation. This raises the possibility that they might share domains that contribute to their functional outcome. Here, we show that the JMJC-KDMs contain low-complexity domains and intrinsically disordered regions (IDR), which in some cases reached 70% of the protein. Our data revealed that plant homeodomain finger protein (PHF2), KDM2A, and KDM4B cluster by phase separation. Moreover, our molecular analysis implies that PHF2 IDR contributes to transcription regulation. These data suggest that clustering via phase separation is a common feature that JMJC-KDMs utilize to facilitate their functional responses. Our study uncovers a novel potential function for the JMJC-KDM family that sheds light on the mechanisms to achieve the competent concentration of molecules in time and space within the cell nucleus.

Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes

Systems of catalytic RNAs presumably gave rise to important evolutionary innovations, such as the genetic code. Such systems may exhibit particular tolerance to errors (error minimization) as well as coding specificity. While often assumed to result from natural selection, error minimization may instead be an emergent by-product. In an RNA world, a system of self-aminoacylating ribozymes could enforce the mapping of amino acids to anticodons. We measured the activity of thousands of ribozyme mutants on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited shared preferences for substrates, indicating that adoption of additional amino acids by existing ribozymes would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of ribozyme evolution could lead to adaptive value in specificity and error tolerance.

JMJD3 intrinsically disordered region links the 3D-genome structure to TGFβ-dependent transcription activation

Enhancers are key regulatory elements that govern gene expression programs in response to developmental signals. However, how multiple enhancers arrange in the 3D-space to control the activation of a specific promoter remains unclear. To address this question, we exploited our previously characterized TGFβ-response model, the neural stem cells, focusing on a ~374 kb locus where enhancers abound. Our 4C-seq experiments reveal that the TGFβ pathway drives the assembly of an enhancer-cluster and precise gene activation. We discover that the TGFβ pathway coactivator JMJD3 is essential to maintain these structures. Using live-cell imaging techniques, we demonstrate that an intrinsically disordered region contained in JMJD3 is involved in the formation of phase-separated biomolecular condensates, which are found in the enhancer-cluster. Overall, in this work we uncover novel functions for the coactivator JMJD3, and we shed light on the relationships between the 3D-conformation of the chromatin and the TGFβ-driven response during mammalian neurogenesis.

Here the authors demonstrate that TGFβ drives multi-enhancer contacts and ultimately gene activation during neuronal commitment, and that this requires the intrinsically disordered region (IDR) of the histone demethylase JMJD3 likely through its role in promoting phase-separated biomolecular condensates.

Identification of promising CD8 and CD4 T cell epitopes for peptide vaccine formulation against SARS-CoV-2

The novel virus “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)” has been responsible for the worldwide pandemic causing huge devastation and deaths since December 2019. The disease caused by this virus is known as COVID-19. The present study is based on immuno-informatics approach to develop a multi-epitope-loaded peptide vaccine to combat the COVID-19 menace. Here, we have reported the 9-mer CD8 T cell epitopes and 15-mer CD4 T cell epitopes, free from glycosylation sites, belonging to three proteins, viz. surface glycoprotein, membrane glycoprotein and envelope protein of this virus. Immunogenicity, aliphatic amino acid, antigenicity and hydrophilicity scores of the predicted epitopes were estimated. In addition, other physicochemical parameters, namely net charge, Boman index and amino acid contents, were also accounted. Out of all the epitopes, three CD8 T cell epitopes viz. PDPSKPSKR, DPSKPSKRS and QTQTNSPRR and three CD4 T cell epitopes viz. ASYQTQTNSPRRARS, RIGNYKLNTDHSSSS and RYRIGNYKLNTDHSS were found to be efficient targets for raising immunity in human against this virus. With the help of our identified potent epitopes, various pharma industries might initiate efforts to incorporate those epitopes with carrier protein or adjuvant to develop a multi-epitope-loaded peptide vaccine against SARS-CoV-2. The peptide vaccines are usually cost-effective and therefore, could be administered as a preventive measure to combat the spread of this disease. Proper clinical trials must be conducted prior to the use of identified epitopes as vaccine candidates.

A new paradigm to search for allergenic proteins in novel foods by integrating proteomics analysis and in silico sequence homology prediction: Focus on spirulina and chlorella microalgae

Since novel nutrient sources with high protein content, such as yeast, fungi, bacteria, algae, and insects, are increasingly introduced in the consumer market, safety evaluation studies on their potentially allergenic proteins are required. A pipeline for in silico establishing the sequence-based homology between proteins of spirulina (Arthrospira platensis) and chlorella (Chlorella vulgaris) micro-algae and those included in the AllergenOnline (AO) database (AllergenOnline.org) is described. The extracted proteins were first identified through tryptic peptides analysis by reversed-phase liquid chromatography and high resolution/accuracy Fourier-transform tandem mass spectrometry (RPLC-ESI-FTMS/MS), followed by a quest on the UniProt database. The AO database was subsequently interrogated to assess sequence similarity between identified microalgal proteins and known allergens, based on criteria established by the World Health Organization (WHO) and Food and Agriculture Organization (FAO). A direct search for microalgal proteins already included in allergen databases was also performed using the Allergome database. Six proteins exhibiting a significant homology with food allergens were identified in spirulina extracts. Five of them, i.e., two thioredoxins (D4ZSU6, K1VP15), a superoxide dismutase (C3V3P3), a glyceraldehyde-3-phosphate dehydrogenase (K1W168), and a triosephosphate isomerase (D5A635), resulted from the search on AO. The sixth protein, C-phycocyanin beta subunit (P72508), was directly obtained after examining the Allergome database. Two proteins exhibiting significant sequence homology with food allergens were retrieved in chlorella extracts, viz. calmodulin (A0A2P6TFR8), which is related to troponin c (D7F1Q2), and fructose-bisphosphate aldolase (A0A2P6TDD0). Specific serum screenings based on immunochemical tests should be undertaken to confirm or rule out the allergenicity of the identified proteins.

Bitiscetin-3, a Novel C-Type Lectin-like Protein Cloned from the Venom Gland of the Viper Bitis arietans, Induces Platelet Agglutination and Inhibits Binding of Von Willebrand Factor to Collagen

Bitiscetin-1 (aka bitiscetin) and bitiscetin-2 are C-type lectin-like proteins purified from the venom of Bitis arietans (puff adder). They bind to von Willebrand factor (VWF) and—at least bitiscetin-1—induce platelet agglutination via enhancement of VWF binding to platelet glycoprotein Ib (GPIb). Bitiscetin-1 and -2 bind the VWF A1 and A3 domains, respectively. The A3 domain includes the major site of VWF for binding collagen, explaining why bitiscetin-2 blocks VWF-to-collagen binding. In the present study, sequences for a novel bitiscetin protein—bitiscetin-3—were identified in cDNA constructed from the B. arietans venom gland. The deduced amino acid sequences of bitiscetin-3 subunits α and β share 79 and 80% identity with those of bitiscetin-1, respectively. Expression vectors for bitiscetin-3α and -3β were co-transfected to 293T cells, producing the heterodimer protein recombinant bitiscetin-3 (rBit-3). Functionally, purified rBit-3 (1) induced platelet agglutination involving VWF and GPIb, (2) did not compete with bitiscetin-1 for binding to VWF, (3) blocked VWF-to-collagen binding, and (4) lost its platelet agglutination inducing ability in the presence of an anti-VWF monoclonal antibody that blocked VWF-to-collagen binding. These combined results suggest that bitiscetin-3 binds to the A3 domain, as does bitiscetin-2. Except for a small N-terminal fragment of a single subunit—which differs from that of both bitiscetin-3 subunits—the sequences of bitiscetin-2 have never been determined. Therefore, by identifying and analyzing bitiscetin-3, the present study is the first to present the full-length α- and β-subunit sequences and recombinant expression of a bitiscetin-family toxin that blocks the binding of VWF to collagen.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

In Silico Prediction and Design of Uropathogenic Escherichia coli Alpha-Hemolysin Generate a Soluble and Hemolytic Recombinant Toxin

The secretion of α-hemolysin by uropathogenic Escherichia coli (UPEC) is commonly associated with the severity of urinary tract infections, which makes it a predictor of poor prognosis among patients. Accordingly, this toxin has become a target for diagnostic tests and therapeutic interventions. However, there are several obstacles associated with the process of α-hemolysin purification, therefore limiting its utilization in scientific investigations. In order to overcome the problems associated with α-hemolysin expression, after in silico prediction, a 20.48 kDa soluble α-hemolysin recombinant denoted rHlyA was constructed. This recombinant is composed by a 182 amino acid sequence localized in the aa542–723 region of the toxin molecule. The antigenic determinants of the rHlyA were estimated by bioinformatics analysis taking into consideration the tertiary form of the toxin, epitope analysis tools, and solubility inference. The results indicated that rHlyA has three antigenic domains localized in the aa555–565, aa600–610, and aa674–717 regions. Functional investigation of rHlyA demonstrated that it has hemolytic activity against sheep red cells, but no cytotoxic effect against epithelial bladder cells. In summary, the results obtained in this study indicate that rHlyA is a soluble recombinant protein that can be used as a tool in studies that aim to understand the mechanisms involved in the hemolytic and cytotoxic activities of α-hemolysin produced by UPEC. In addition, rHlyA can be applied to generate monoclonal and/or polyclonal antibodies that can be utilized in the development of diagnostic tests and therapeutic interventions.

Dual keratinocyte-attachment and anti-inflammatory coatings for soft tissue sealing around transmucosal oral implants

Unlike the attachment of soft epithelial skin tissue to penetrating solid natural structures like fingernails and teeth, sealing around percutaneous/permucosal devices such as dental implants is hindered by inflammation and epidermal down growth. Here, we employed a dual keratinocyte-adhesive peptide and anti-inflammatory biomolecule coating on titanium to promote oral epithelial tissue attachment. For minimizing inflammation-triggered epidermal down growth, we coated pristine and oxygen plasma pre-treated polished titanium (pTi) with conjugated linoleic acid (CLA). Further, in order to aid in soft tissue attachment via the formation of hemidesmosomes, adhesive structures by oral keratinocytes, we coated the anionic linoleic acid (LA) adsorbed titanium with cationic cell adhesive peptides (CAP), LamLG3, a peptide derived from Laminin 332, the major extracellular matrix component of the basement membrane in skin tissue and Net1, derived from Netrin-1, a neural chemoattractant capable of epithelial cell attachment via α6β4 integrins. The dual CLA-CAP coatings on pTi were characterized by X-ray photoelectron spectroscopy and dynamic water contact angle measurements. The proliferation of human oral keratinocytes (TERT-2/OKF6) was accelerated on the peptide coated titanium while also promoting the expression of Col XVII and β-4 integrin, two markers for hemidesmosomes. Simultaneously, CLA coating suppressed the production of inducible nitric oxide synthase (anti-iNOS); a pro-inflammatory M1 marker expressed in lipopolysaccharide (LPS) stimulated murine macrophages (RAW 264.7) and elevated expression of anti-CD206, associated to an anti-inflammatory M2 macrophage phenotype. Taken together, the dual keratinocyte-adhesive peptide and anti-inflammatory biomolecule coating on titanium can help reduce inflammation and promote permucosal/peri-implant soft tissue sealing.

Organism-specific training improves performance of linear B-cell epitope prediction

MOTIVATION

In silico identification of linear B-cell epitopes represents an important step in the development of diagnostic tests and vaccine candidates, by providing potential high-probability targets for experimental investigation. Current predictive tools were developed under a generalist approach, training models with heterogeneous datasets to develop predictors that can be deployed for a wide variety of pathogens. However, continuous advances in processing power and the increasing amount of epitope data for a broad range of pathogens indicate that training organism or taxon-specific models may become a feasible alternative, with unexplored potential gains in predictive performance.

RESULTS

This article shows how organism-specific training of epitope prediction models can yield substantial performance gains across several quality metrics when compared to models trained with heterogeneous and hybrid data, and with a variety of widely used predictors from the literature. These results suggest a promising alternative for the development of custom-tailored predictive models with high predictive power, which can be easily implemented and deployed for the investigation of specific pathogens.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article, as well as the full reproducibility scripts, are available at https://github.com/fcampelo/OrgSpec-paper. The R package that implements the organism-specific pipeline functions is available at https://github.com/fcampelo/epitopes.

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Selecting the Antigen

The classical method for generating polyclonal or monoclonal antibodies relies on the in vivo humoral response of animals. Here we describe the factors that antigens can have that might influence the strength and quality of an antibody response. This introduction is divided into three sections: (1) an overview of immunogenicity, (2) choosing the best form for the immunogen, and (3) methods for modifying antigens to make them more immunogenic.

Compositional features and pattern of codon usage for mitochondrial CO genes among reptiles

The phenomenon of non-random occurrence of synonymous nucleotide triplets (codons) in the coding sequences of genes is the codon usage bias (CUB). In this study, we used bioinformatic tool kit to analyze the compositional pattern and CUB of mitogenes namely COI, COII and COIII across different orders of reptiles. Estimation of overall base composition in the protein-coding sequences of COI, COII and COIII genes of the reptilian orders revealed an uneven usage of nucleotides. The overall count of A nucleotide was found to be the highest while the overall count of G nucleotide was the least. The CO genes across the three reptilian orders were prominently AT biased. Comparison of the GC proportion at each codon position displayed that GC1 percentage ranked the highest in all the three CO genes of the reptilian orders. SCUO values indicated weaker CUB, while considerable variation of SCUO values existed in the three CO genes across the studied reptiles. Relative synonymous codon usage (RSCU) values indicated that mostly the A ending codons were preferred. Based on the parameters namely neutrality plot, mutational responsive index and translational selection, we could conclude that natural selection was the major evolutionary force in COI, COII and COIII genes in the studied reptilian orders. However, correspondence analysis, parity plot and correlation studies indicated the existence of mutation pressure as well on the CO genes.

Molecular Characterization of a Tetraspanin TSP11 Gene in Echinococcus granulosus and Evaluation Its Immunoprotection in Model Dogs

Cystic echinococcosis (CE) is a cosmopolitan zoonosis caused by the larval stage of Echinococcus granulosus, which affects humans and a wide range of mammalian intermediate hosts. Parasite tetraspanin proteins are crucial for host-parasite interactions, and therefore they may be useful for vaccine development or disease diagnosis. In the present study, the major antigen coding sequence of tetraspanin 11 (Eg-TSP11) from E. granulosus was determined. The results of immunolocalization showed that Eg-TSP11 was mainly located in the tegument of adult worms and protoscoleces. Western blotting analysis showed that the serum from dogs injected with recombinant Eg-TSP11 (rEg-TSP11) could recognize Eg-TSP11 among natural protoscolex proteins. Moreover, the serum from dogs with E. granulosus infection also recognized rEg-TSP11. Serum indirect enzyme-linked immunosorbent assays demonstrated that IgG levels gradually increased after the first immunization with rEg-TSP11 compared with those in the control group. Furthermore, the serum levels of interleukin 4, interleukin 5, and interferon gamma were significantly altered in the rEg-TSP11 group. Importantly, we found that vaccination with rEg-TSP11 significantly decreased worm burden and inhibited segment development in a dog model of E. granulosus infection. Based on these findings, we speculated that rEg-TSP11 might be a potential candidate vaccine antigen against E. granulosus infection in dogs.

Engineering of Cytolethal Distending Toxin B by Its Reducing Immunogenicity and Maintaining Stability as a New Drug Candidate for Tumor Therapy; an In Silico Study

The cytolethal distending toxin (CDT), Haemophilus ducreyi, is one of the bacterial toxins that have recently been considered for targeted therapies, especially in cancer therapies. CDT is an A-B2 exotoxin. Its catalytic subunit (CdtB) is capable of inducing DNA double strand breaks, cell cycle arrest and apoptosis in host eukaryotic cells. The sequence alignment indicates that the CdtB is structurally homologyr to phosphatases and deoxyribonucleases I (DNase I). Recently, it has been found that CdtB toxicity is mainly related to its nuclease activity. The immunogenicity of CDT can reduce its effectiveness in targeted therapies. However, the toxin can be very useful if its immunogenicity is significantly reduced. Detecting hotspot ectopic residues by computational servers and then mutating them to eliminate B-cell epitopes is a promising approach to reduce the immunogenicity of foreign protein-based therapeutics. By the mentioned method, in this study, we try to reduce the immunogenicity of the CdtB- protein sequence. This study initially screened residue of the CdtB is B-cell epitopes both linearly and conformationally. By overlapping the B-cell epitopes with the excluded conserve residues, and active and enzymatic sites, four residues were allowed to be mutated. There were two mutein options that show reduced antigenicity probability. Option one was N19F, G74I, and S161F with a VaxiJen score of 0.45 and the immune epitope database (IEDB) score of 1.80, and option two was N19F, G74I, and S161W with a VaxiJen score of 0.45 and IEDB score of 1.88. The 3D structure of the proposed sequences was evaluated and refined. The structural stability of native and mutant proteins was accessed through molecular dynamic simulation. The results showed that the mutations in the mutants caused no considerable changes in their structural stability. However, mutant 1 reveals more thermodynamic stability during the simulation. The applied approaches in this study can be used as rough guidelines for finding hot spot immunogen regions in the therapeutic proteins. Our results provide a new version of CdtB that, due to reduced immunogenicity and increased stability, can be used in toxin-based drugs such as immunotoxins.

ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.

Classifying Residues in Mechanically Stable and Unstable Substructures Based on a Protein Sequence: The Case Study of the DnaK Hsp70 Chaperone

Artificial proteins can be constructed from stable substructures, whose stability is encoded in their protein sequence. Identifying stable protein substructures experimentally is the only available option at the moment because no suitable method exists to extract this information from a protein sequence. In previous research, we examined the mechanics of E. coli Hsp70 and found four mechanically stable (S class) and three unstable substructures (U class). Of the total 603 residues in the folded domains of Hsp70, 234 residues belong to one of four mechanically stable substructures, and 369 residues belong to one of three unstable substructures. Here our goal is to develop a machine learning model to categorize Hsp70 residues using sequence information. We applied three supervised methods: logistic regression (LR), random forest, and support vector machine. The LR method showed the highest accuracy, 0.925, to predict the correct class of a particular residue only when context-dependent physico-chemical features were included. The cross-validation of the LR model yielded a prediction accuracy of 0.879 and revealed that most of the misclassified residues lie at the borders between substructures. We foresee machine learning models being used to identify stable substructures as candidates for building blocks to engineer new proteins.

Heparin promotes fibrillation of most phenol-soluble modulin virulence peptides from Staphylococcus aureus

Phenol-soluble modulins (PSMs), such as α-PSMs, β-PSMs, and δ-toxin, are virulence peptides secreted by different Staphylococcus aureus strains. PSMs are able to form amyloid fibrils, which may strengthen the biofilm matrix that promotes bacterial colonization of and extended growth on surfaces (e.g., cell tissue) and increases antibiotic resistance. Many components contribute to biofilm formation, including the human-produced highly sulfated glycosaminoglycan heparin. Although heparin promotes S. aureus infection, the molecular basis for this is unclear. Given that heparin is known to induce fibrillation of a wide range of proteins, we hypothesized that heparin aids bacterial colonization by promoting PSM fibrillation. Here, we address this hypothesis using a combination of thioflavin T-fluorescence kinetic studies, CD, FTIR, electron microscopy, and peptide microarrays to investigate the mechanism of aggregation, the structure of the fibrils, and identify possible binding regions. We found that heparin accelerates fibrillation of all α-PSMs (except PSMα2) and δ-toxin but inhibits β-PSM fibrillation by blocking nucleation or reducing fibrillation levels. Given that S. aureus secretes higher levels of α-PSM than β-PSM peptides, heparin is therefore likely to promote fibrillation overall. Heparin binding is driven by multiple positively charged lysine residues in α-PSMs and δ-toxins, the removal of which strongly reduced binding affinity. Binding of heparin did not affect the structure of the resulting fibrils, that is, the outcome of the aggregation process. Rather, heparin provided a scaffold to catalyze or inhibit fibrillation. Based on our findings, we speculate that heparin may strengthen the bacterial biofilm and therefore enhance colonization via increased PSM fibrillation.

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

S. Typhi derived vaccines and a proposal for outer membrane vesicles (OMVs) as potential vaccine for typhoid fever

Typhoid fever is a serious systemic infection caused by Salmonella Typhi (S. Typhi), spread by the feco-oral route and closely associated with poor food hygiene and inadequate sanitation. Nearly 93% of S. Typhi strains have acquired antibiotic resistance against most antibiotics. Vaccination is the only promising way to prevent typhoid fever. This review covers the nature and composition of S. Typhi, pathogenecity and mode of infection, epidemiology, and nature of drug resistance. Several components (Vi-polysaccharides, O-antigens, flagellar antigens, full length OMPs, and short peptides from OMPs) of S. Typhi have been utilized for vaccine design for protection against typhoid fever. Vaccine delivery systems also contribute to efficacy of the vaccines. In this study, we propose to develop S. Typhi derived OMVs as vaccine for protection against typhoid fevers.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Intrinsic basis of thermostability of prolyl oligopeptidase from Pyrococcus furiosus

Salt-bridges play a key role in the thermostability of proteins adapted in stress environments whose intrinsic basis remains to be understood. We find that the higher hydrophilicity of PfP than that of HuP is due to the charged but not the polar residues. The primary role of these residues is to enhance the salt-bridges and their ME. Unlike HuP, PfP has made many changes in its intrinsic property to strengthen the salt-bridge. First, the desolvation energy is reduced by directing the salt-bridge towards the surface. Second, it has made bridge-energy more favorable by recruiting energetically advantageous partners with high helix-propensity among the six possible salt-bridge pairs. Third, ME-residues that perform intricate interactions have increased their energy contribution by making major changes in their binary properties. The use of salt-bridge partners as ME-residues, and ME-residues' overlapping usage, predominant in helices, and energetically favorable substitution are some of the favorable features of PfP compared to HuP. These changes in PfP reduce the unfavorable, increase the favorable ME-energy. Thus, the per salt-bridge stability of PfP is greater than that of HuP. Further, unfavorable target ME-residues can be identified whose mutation can increase the stability of salt-bridge. The study applies to other similar systems.

The Detection of Bovine Estrus by Lactoferrin Monoclonal Antibody

Simple Summary

This study aimed to develop monoclonal antibodies with high specificity against bovine lactoferrin, which we have previously demonstrated to be overexpressed in bovine cervical mucus during estrus. Using an enzyme-linked immunosorbent assay, we observed that our monoclonal exhibited strong affinity for bovine lactoferrin protein. In addition, upon testing the new heat detection kits based on our antibody on 12 Korean native cows, we demonstrated an accurate detection of estrus during estrous synchronization. This is the first report of a non-invasive method to detect estrous using antibodies that bind to physiological material in cows. The results of this study suggest that the antibodies and a fabricated heat detection kit can be utilized to improve estrous detection in the cattle industry.

Abstract

To improve reproductive performance in cattle, the accurate detection of estrus and optimization of insemination relative to ovulation are necessary. However, poor heat detection by farm staff leads to a decreased conception rate, thus inflicting economic damage to the beef and dairy industries. This study aimed to develop monoclonal antibodies (mAb) that can specifically bind to the bovine lactoferrin (bLF) protein, which we have previously demonstrated to be overexpressed in bovine cervical mucus during estrus. Female rats were intraperitoneally immunized with bLF protein as the antigen. Anti-bLF mAbs were then purified by affinity chromatography, and their binding affinity for the bLF antigen was examined using ELISA. We found a high binding affinity between mAbs and bLF. Finally, we developed a rapid bovine heat detection kit using the anti-bLF mAbs that we generated and tested on cervical mucus from 12 cows (estrous synchronization, n = 2; natural cycling, n = 10). We found that the kits accurately detected estrus. Overall, our fabricated heat detection kit based on rat anti-bLF mAbs could pave the way for the development of potent tools for heat detection devices for dairy cattle, thereby preventing economic loss.

Component Parts of Bacteriophage Virions Accurately Defined by a Machine-Learning Approach Built on Evolutionary Features

Antimicrobial resistance (AMR) continues to evolve as a major threat to human health, and new strategies are required for the treatment of AMR infections. Bacteriophages (phages) that kill bacterial pathogens are being identified for use in phage therapies, with the intention to apply these bactericidal viruses directly into the infection sites in bespoke phage cocktails. Despite the great unsampled phage diversity for this purpose, an issue hampering the roll out of phage therapy is the poor quality annotation of many of the phage genomes, particularly for those from infrequently sampled environmental sources. We developed a computational tool called STEP³ to use the “evolutionary features” that can be recognized in genome sequences of diverse phages. These features, when integrated into an ensemble framework, achieved a stable and robust prediction performance when benchmarked against other prediction tools using phages from diverse sources. Validation of the prediction accuracy of STEP³ was conducted with high-resolution mass spectrometry analysis of two novel phages, isolated from a watercourse in the Southern Hemisphere. STEP³ provides a robust computational approach to distinguish specific and universal features in phages to improve the quality of phage cocktails and is available for use at http://step3.erc.monash.edu/.

IMPORTANCE In response to the global problem of antimicrobial resistance, there are moves to use bacteriophages (phages) as therapeutic agents. Selecting which phages will be effective therapeutics relies on interpreting features contributing to shelf-life and applicability to diagnosed infections. However, the protein components of the phage virions that dictate these properties vary so much in sequence that best estimates suggest failure to recognize up to 90% of them. We have utilized this diversity in evolutionary features as an advantage, to apply machine learning for prediction accuracy for diverse components in phage virions. We benchmark this new tool showing the accurate recognition and evaluation of phage component parts using genome sequence data of phages from undersampled environments, where the richest diversity of phage still lies.

Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network

BACKGROUND

Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA-protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs.

RESULTS

We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement.

CONCLUSIONS

This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver ( http://csbg-jlu.site/lpc/predict ) is developed to be convenient for users.

Identification and Amino Acid Analysis of Allergenic Epitopes of a Novel Allergen Paramyosin (Rap v 2) from Rapana venosa

Besides tropomyosin (TM) that is widely recognized as a major allergen in molluscs, a 99-kDa novel allergen (Rap v 2) was recently found in the sea snail Rapana venosa and identified as paramyosin (PM). However, the allergenic epitopes of PM in any molluscs have not been identified yet. In the present study, seven allergenic epitopes of Rap v 2 were identified by immunoinformatics tools, dot-blot inhibition assay, and basophil degranulation assay. Based on the analysis of PM and allergenic epitope amino acids, it was found that highly hydrophobic and positively charged amino acid residues play an important role in the formation of Rap v 2 epitopes. In addition, three potential critical amino acids that may account for TM and PM cross-reactivity in molluscs were found by sequence- and structure-based methods. These findings could be of major importance for improving the understanding of relevant paramyosin epitopes and the prevention and therapy of mollusc allergy.

The role of proteomics in defining autoimmunity

INTRODUCTION

Proteomics, i.e. the study of the set of proteins produced in a cell, tissue, organism, or biological entity, has made possible analyses and contextual comparisons of proteomes/proteins and biological functions among the most disparate entities, from viruses to the human being. In this way, proteomic scrutiny of tumor-associated proteins, autoantigens, and pathogen antigens offers the tools for fighting cancer, autoimmunity, and infections.

AREAS COVERED

Comparative proteomics and immunoproteomics, the new scientific disciplines generated by proteomics, are the main themes of the present review that describes how comparative analyses of pathogen and human proteomes led to re-modulate the molecular mimicry concept of the pre-proteomic era. I.e. before proteomics, molecular mimicry - the sharing of peptide sequences between two biological entities - was considered as intrinsically endowed with immunologic properties and was related to cross-reactivity. Proteomics allowed to redefine such an assumption using physicochemical parameters according to which frequency and hydrophobicity preferentially confer an immunologic potential to shared peptide sequences.

EXPERT OPINION

Proteomics is outlining peptide platforms to be used for the diagnostics and management of human diseases. A Molecular Medicine targeted to obtain healing without paying the price for adverse events is on the horizon. The next step is to take up the challenge and operate the paradigm shift that the current proteomic era requires.

N-semble-based method for identifying Parkinson’s disease genes

Parkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to identify Parkinson’s disease genes. The artificial neural network is trained in a unique way to ensemble the multiple model predictions. The proposed n-semble method is composed of four parts: (1) protein sequences are used to construct feature vectors using physicochemical properties of amino acid; (2) dimensionality reduction is achieved using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method, (3) the Jaccard method is applied to find likely negative samples from unknown (candidate) genes, and (4) gene prediction is performed with n-semble method. The proposed n-semble method has been compared with Smalter’s, ProDiGe, PUDI and EPU methods using various evaluation metrics. It has been concluded that the proposed n-semble method outperforms the existing gene identification methods over the other methods and achieves significantly higher precision, recall and F Score of 88.9%, 90.9% and 89.8%, respectively. The obtained results confirm the effectiveness and validity of the proposed framework.

Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies

As the current worldwide outbreaks of the SARS-CoV-2, it is urgently needed to develop effective therapeutic agents for inhibiting the pathogens or treating the related diseases. Antimicrobial peptides (AMP) with functional activity against coronavirus could be a considerable solution, yet there is no research for identifying anti-coronavirus (anti-CoV) peptides with the computational approach. In this study, we first investigated the physiochemical and compositional properties of the collected anti-CoV peptides by comparing against three other negative sets: antivirus peptides without anti-CoV function (antivirus), regular AMP without antivirus functions (non-AVP) and peptides without antimicrobial functions (non-AMP). Then, we established classifiers for identifying anti-CoV peptides between different negative sets based on random forest. Imbalanced learning strategies were adopted due to the severe class-imbalance within the datasets. The geometric mean of the sensitivity and specificity (GMean) under the identification from antivirus, non-AVP and non-AMP reaches 83.07%, 85.51% and 98.82%, respectively. Then, to pursue identifying anti-CoV peptides from broad-spectrum peptides, we designed a double-stages classifier based on the collected datasets. In the first stage, the classifier characterizes AMPs from regular peptides. It achieves an area under the receiver operating curve (AUCROC) value of 97.31%. The second stage is to identify the anti-CoV peptides between the combined negatives of other AMPs. Here, the GMean of evaluation on the independent test set is 79.42%. The proposed approach is considered as an applicable scheme for assisting the development of novel anti-CoV peptides. The datasets and source codes used in this study are available at https://github.com/poncey/PreAntiCoV.

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

Genogrouping of Infectious Bursal Disease Viruses Circulating in Ethiopian Chickens: Proposal for Assigning Very Virulent Strains in the Country into New Sub Genogroup 3d

Introduction

In 2017 infectious bursal disease viruses (IBDVs) were reclassified into genogroups based on nature of clustering on a phylogenetic tree constructed using VP2 gene sequence data rather than according to their pathotype and/or antigenic types. Ethiopian IBD viruses were not reclassified according to the proposed genogrouping.

Methods

In order to genogroup the Ethiopian IBDVs, available VP2 gene sequences data together with reference strain sequences were retrieved from GenBank and genogrouped as recently recommended based on evolutionary tree reconstruction and determination of their clustering on the phylogenetic tree.

Results

The Ethiopian IBDVs were grouped into genogroups 1 and 3 that antigenically represent classically virulent and very virulent IBDVs, respectively. The genogroup 1 IBDVs were clustered with the vaccine strain while the genogroup 3 viruses were clustered with four known viruses belonging to sub-genogroup 3a and sub-genogroup 3b. Almost half of the Ethiopian IBDVs reported did not cluster with the specific sub-groups of genogroup 3; rather, the isolates were clustered differently suggesting they deserve a different sub-genogroup tentatively proposed as 3d. The two genogroups observed based on clustering on a phylogenetic tree were supported by corresponding deduced amino acid changes in similar positions in VP2 sequences. In addition, virulence marker amino acid genes coupled with second major hydrophilic region (amino acid positions 314–325) were predicted in these sequences that could be responsible for the occurrence of IBD outbreaks.

Conclusion

A new sub-genogroup of IBDVs, 3d, were observed in the sequences that could be one of the reasons for the frequent occurrence of IBD outbreaks and questions the protective potential of the existing vaccine. To institute disease control in the country, the effectiveness of the vaccine in use needs to be assessed in vivo against both genogroups 1 and 3 viruses and all three sub-genogroup 3 viruses circulating in the country.

A method for making alignments of related protein sequences that share very little similarity; shark interleukin 2 as an example

An optimized alignment of related protein sequences helps to see their important shared features and to deduce their phylogenetic relationships. At low levels of sequence similarity, there are no suitable computer programs for making the best possible alignment. This review summarizes some guidelines for how in such instances, nevertheless, insightful alignments can be made. The method involves, basically, the understanding of molecular family features at both the protein and intron-exon level, and the collection of many related sequences so that gradual differences may be observed. The method is exemplified by identifying and aligning interleukin 2 (IL-2) and related sequences in Elasmobranchii (sharks/rays) and coelacanth, as other authors have expressed difficulty with their identification. From the point of general immunology, it is interesting that the unusual long "leader" sequence of IL-15, already known in other species, is even more impressively conserved in cartilaginous fish. Furthermore, sequence comparisons suggest that IL-2 in cartilaginous fish has lost its ability to bind an IL-2Rα/15Rα receptor chain, which would prohibit the existence of a mechanism for regulatory T cell regulation identical to mammals.

The amphibian peptide Yodha is virucidal for Zika and dengue viruses

Zika virus (ZIKV) has emerged as a serious health threat in the Americas and the Caribbean. ZIKV is transmitted by the bite of an infected mosquito, sexual contact, and blood transfusion. ZIKV can also be transmitted to the developing fetus in utero, in some cases resulting in spontaneous abortion, fetal brain abnormalities, and microcephaly. In adults, ZIKV infection has been correlated with Guillain-Barre syndrome. Despite the public health threat posed by ZIKV, neither a vaccine nor antiviral drugs for use in humans are currently available. We have identified an amphibian host defense peptide, Yodha, which has potent virucidal activity against ZIKV. It acts directly on the virus and destroys Zika virus particles within 5 min of exposure. The Yodha peptide was effective against the Asian, African, and South American Zika virus strains and has the potential to be developed as an antiviral therapeutic in the fight against Zika virus. The peptide was also effective against all four dengue virus serotypes. Thus, Yodha peptide could potentially be developed as a pan-therapeutic for Zika and dengue viruses.

Composition, codon usage pattern, protein properties, and influencing factors in the genomes of members of the family Anelloviridae

The present study was carried out on 62 genome sequences of members of the family Anelloviridae, as there have been no reports of genome analysis of these DNA viruses using a bioinformatics approach. The genes were found to be rich in AC content with low codon usage bias (CUB). Relative synonymous codon usage (RSCU) values identified the preferred codons for each amino acid in the family. The codon AGA was overrepresented, while the codons TCG, TTG, CGG, CGT, ACG, GCG and GAT were underrepresented in all of the genomes. A significant correlation was found between the effective number of codons (ENC) and base constraints, indicating that compositional properties might have influenced the CUB. A highly significant correlation was observed between the overall base content and the base content at the third codon position, indicating that mutations might have affected the CUB. A highly significant positive correlation was observed between GC12 and GC3 (r = 0.904, p < 0.01), which indicated that directional mutation pressure influenced all three codon positions. A neutrality plot revealed that the contribution of mutation and natural selection in determining the CUB was 58.6% and 41.4%, respectively.

Increased frequency of canine distemper virus-specific antibodies in multiple sclerosis

BACKGROUND AND PURPOSE

Canine distemper virus (CDV) is a candidate agent in the etiology of multiple sclerosis (MS). Elevated anti-CDV levels were previously found in the sera from MS patients compared with controls. We now investigated whether there was an age-related association with the presence of antibodies specific to CDV-hemagglutinin (H) protein in MS.

METHODS

Sera from patients with MS, other neurological diseases, and inflammatory and/or autoimmune diseases, and healthy individuals were screened for anti-CDV in an ELISA using linear peptides of the CDV-H protein as antigen. Antibody levels to measles and varicella-zoster virus were measured and served as controls.

RESULTS

Analysis of the new cohort of MS patients and controls confirmed our initial finding of elevated anti-CDV-H levels in MS patients. An increase in measles but not varicella-zoster virus antibody levels was found in MS patients compared with healthy controls. Data from the new cohort of patients and controls were combined with data from the original study and analyzed with respect to age distribution of anti-CDV IgG. Mean CDV antibody levels were significantly elevated in each decade from 20 to 50 years of age in MS compared with healthy and disease controls. Antibody levels to measles virus were not consistently elevated during this age span. A striking relationship (p < .0001, odds ratio = 5.0) was observed between elevated anti-CDV-H levels and diagnosis of MS.

CONCLUSIONS

The finding that anti-CDV levels are elevated in MS patients of all ages provides substantial evidence of a strong association between elevated anti-CDV and MS.

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.

Integrative approach for detecting membrane proteins

Background

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

Results

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

Conclusion

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

ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides

The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.

Genome-wide analysis of codon usage pattern in herpesviruses and its relation to evolution

The preferential use of a specific codon, out of a group of synonymous codons encoding the same amino acid, in a gene transcript results from the bias in codon choice. Various evolutionary forces namely mutation pressure and natural selection influence the pattern of codon usage i.e. distinct for each gene/genome. We investigated the pattern of codon usage of eight human herpesvirus genomes and compared them with two other herpesvirus genomes namely murine herpesvirus 68 and bovine herpesvirus type 1.1 to elucidate its compositional features, pattern of codon usage across the genomes and report the differences of codon usage pattern of human herpesviruses from that of other two other viruses. We also identified the similarity of the codon usage of human herpesviruses with its host (human). The genes were found to be CG rich in HHV2, HHV3, HHV4, HHV6, HHV7 and BH genomes while TA rich in HHV1, HHV5, HHV8 and MH genomes. The codon usage bias (CUB) of genes was low. A highly significant correlation was found among compositional contents depicting the role of mutational pressure along with natural selection in framing CUB. Several more frequently used codons as well as less frequently used codons were identified to be similar between each human virus and its host (human), while murine herpesvirus 68 and bovine herpesvirus type 1.1 genomes did not possess similar adaptation strategy as human herpesviruses to human (host), thus we could conclude that viral CUB might have been shaped as per their host's nature for better surveillance. Neutrality plot revealed mutational pressure mostly influenced the CUB of HHV1, HHV8 and MH viruses, while natural selection had a major impact in the CUB of HHV2, HHV3, HHV4, HHV5, HHV6, HHV7 and BH genomes.