B-Pred, a structure based B-cell epitopes prediction server

Peptide microarray analysis of in-silico predicted B-cell epitopes in SARS-CoV-2 sero-positive healthcare workers in Bulawayo, Zimbabwe

Immunogenic peptides that mimic linear B-cell epitopes coupled with immunoassay validation may improve serological tests for emerging diseases. This study reports a general approach for profiling linear B-cell epitopes derived from SARS-CoV-2 using an in-silico method and peptide microarray immunoassay, using healthcare workers' SARS-CoV-2 sero-positive sera. SARS-CoV-2 was tested using rapid chromatographic immunoassays and real-time reverse-transcriptase polymerase chain reaction. Immunogenic peptides mimicking linear B-cell epitopes were predicted in-silico using ABCpred. Peptides with the lowest sequence identity with human protein and proteins from other human pathogens were selected using the NCBI Protein BLAST. IgG and IgM antibodies against the SARS-CoV-2 spike protein, membrane glycoprotein and nucleocapsid derived peptides were measured in sera using peptide microarray immunoassay. Fifty-three healthcare workers included in the study were RT-PCR negative for SARS-CoV-2. Using rapid chromatographic immunoassays, 10 were SARS-CoV-2 IgM sero-positive and 7 were SARS-CoV-2 IgG sero-positive. From a total of 10 SARS-CoV-2 peptides contained on the microarray, 3 (QTH34388.1-1-14, QTN64908.1-135-148, and QLL35955.1-22-35) showed reactivity against IgG. Three peptides (QSM17284.1-76-89, QTN64908.1-135-148 and QPK73947.1-8-21) also showed reactivity against IgM. Based on the results we predicted one peptide (QSM17284.1-76-89) that had an acceptable diagnostic performance. Peptide QSM17284.1-76-89 was able to detect IgM antibodies against SARS-CoV-2 with area under the curve (AUC) 0.781 when compared to commercial antibody tests. In conclusion in silico peptide prediction and peptide microarray technology may provide a platform for the development of serological tests for emerging infectious diseases such as COVID-19. However, we recommend using at least three in-silico peptide prediction tools to improve the sensitivity and specificity of B-cell epitope prediction, to predict peptides with excellent diagnostic performances.

Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics

The prevalence of allergic diseases is regarded as one of the key challenges in health worldwide. Although the precise mechanisms underlying this rapid increase in prevalence are unknown, emerging evidence suggests that genetic and environmental factors play a significant role. The immune system, microbiota, viruses, and bacteria have all been linked to the onset of allergy disorders in recent years. Avoiding allergen exposure is the best treatment option; however, steroids, antihistamines, and other symptom-relieving drugs are also used. Allergen bioinformatics encompasses both computational tools/methods and allergen-related data resources for managing, archiving, and analyzing allergological data. This study highlights allergy-promoting mechanisms, algorithms, and concepts in allergen bioinformatics, as well as major areas for future research in the field of allergology.

In silico prediction of B cell epitopes of the extracellular domain of insulin-like growth factor-1 receptor

The insulin-like growth factor-1 receptor (IGF-1R) is a transmembrane receptor with tyrosine kinase activity. The receptor plays a critical role in cancer. Using monoclonal antibodies (MAbs) against the IGF-1R, typically blocks ligand binding and enhances down-regulation of the cell-surface IGF-1R. Some MAbs such as cixutumumab are under clinical trial investigation. Targeting multiple distinct epitopes on IGF-1R, might be an effective strategy to inhibit IGF-1R pathway in cancer. In this study, new linear B cell epitopes for the extracellular domains of IGF-1R were predicted by in silico methods using a combination of linear B cell epitope prediction web servers such as ABCpred, Bepired, BCPREDs, Bcepred and Elliprro. Moreover, Discotope, B- pred and PEPOP web server tools were employed to predict new conformational B cell epitopes. In contrast to previously reported epitopes from extracellular region of the IGF-1R, we predicted new linear P8: (RQPQDGYLYRHNYCSK) and conformational Pc4: (HYYYAGVCVPACPPNTYRFE), Ppc6: (KMCPSTGKRENNESAPDNDT) and Ppc20: (ANILSAESSDSEFMQEPSGFI) epitopes. These epitopes are useful for further study as peptide antigens to actively immune host animals to develop new MAbs. Furthermore, the epitopes can be used in peptide-based cancer vaccines design.

The best of respiratory infections from the 2015 European Respiratory Society International Congress

The breadth and quality of scientific presentations on clinical and translational research into respiratory infections at the 2015 European Respiratory Society (ERS) International Congress in Amsterdam, the Netherlands, establishes this area as one of the leadings fields in pulmonology. The host-pathogen relationship in chronic obstructive pulmonary disease, and the impact of comorbidities and chronic treatment on clinical outcomes in patients with pneumonia were studied. Various communications were dedicated to bronchiectasis and, in particular, to different prognostic and clinical aspects of this disease, including chronic infection with Pseudomonas and inhaled antibiotic therapy. Recent data from the World Health Organization showed that Europe has the highest number of multidrug-resistant tuberculosis cases and the poorest countries have the least access to suitable treatments. Latent tuberculosis and different screening programmes were also discussed with particular attention to risk factors such as HIV infection and diabetes. Several biomarkers were proposed to distinguish between active tuberculosis and latent infection. Major treatment trials were discussed (REMOX, RIFQUIN and STREAM). The possibility of once-weekly treatment in the continuation phase (RIAQUIN) was especially exciting. The continuing rise of Mycobacterium abscessus as a significant pathogen was noted. This article reviews some of the best contributions from the Respiratory Infections Assembly to the 2015 ERS International Congress.

SEPPA 2.0--more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen

Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.

Structure of allergens and structure based epitope predictions

The structure determination of major allergens is a prerequisite for analyzing surface exposed areas of the allergen and for mapping conformational epitopes. These may be determined by experimental methods including crystallographic and NMR-based approaches or predicted by computational methods. In this review we summarize the existing structural information on allergens and their classification in protein fold families. The currently available allergen-antibody complexes are described and the experimentally obtained epitopes compared. Furthermore we discuss established methods for linear and conformational epitope mapping, putting special emphasis on a recently developed approach, which uses the structural similarity of proteins in combination with the experimental cross-reactivity data for epitope prediction.