Dataset Information

Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae.

ABSTRACT:

Background

Nodaviridae infection is one of the leading causes of death in commercial fish. Although many vaccines against this virus family have been developed, their efficacies are relatively low. Nodaviridae are categorized into three subfamilies: alphanodavirus (infects insects), betanodavirus (infects fish), and gammanodavirus (infects prawns). These three subfamilies possess host-specific characteristics that could be used to identify effective linear epitopes (LEs).

Methodology

A multi-expert system using five existing LE prediction servers was established to obtain initial LE candidates. Based on the different clustered pathogen groups, both conserved and exclusive LEs among the Nodaviridae family could be identified. The advantages of undocumented cross infection among the different host species for the Nodaviridae family were applied to re-evaluate the impact of LE prediction. The surface structural characteristics of the identified conserved and unique LEs were confirmed through 3D structural analysis, and concepts of surface patches to analyze the spatial characteristics and physicochemical propensities of the predicted segments were proposed. In addition, an intelligent classifier based on the Immune Epitope Database (IEDB) dataset was utilized to review the predicted segments, and enzyme-linked immunosorbent assays (ELISAs) were performed to identify host-specific LEs.

Principal findings

We predicted 29 LEs for Nodaviridae. The analysis of the surface patches showed common tendencies regarding shape, curvedness, and PH features for the predicted LEs. Among them, five predicted exclusive LEs for fish species were selected and synthesized, and the corresponding ELISAs for antigenic feature analysis were examined.

Conclusion

Five identified LEs possessed antigenicity and host specificity for grouper fish. We demonstrate that the proposed method provides an effective approach for in silico LE prediction prior to vaccine development and is especially powerful for analyzing antigen sequences with exclusive features among clustered antigen groups.

SUBMITTER: Shih TC

PROVIDER: S-EPMC9319239 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background:B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes. Results:After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available. Conclusions:We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

Project description:BACKGROUND: Enterovirus 71 (EV71) has emerged as a major causative agent of hand, foot and mouth disease in the Asia-Pacific region over the last decade. Hand, foot and mouth disease can be caused by different etiological agents from the enterovirus family, mainly EV71 and coxsackieviruses, which are genetically closely related. Nevertheless, infection with EV71 may occasionally lead to high fever, neurologic complications and the emergence of a rapidly fatal syndrome of pulmonary edema associated with brainstem encephalitis. The rapid progression and high mortality of severe EV71 infection has highlighted the need for EV71-specific diagnostic and therapeutic tools. Monoclonal antibodies are urgently needed to specifically detect EV71 antigens from patient specimens early in the infection process. Furthermore, the elucidation of viral epitopes will contribute to the development of targeted therapeutics and vaccines. RESULTS: We have identified the monoclonal antibody 7C7 from a screen of hybridoma cells derived from mice immunized with the EV71-B5 strain. The linear epitope of 7C7 was mapped to amino acids 142-146 (EDSHP) of the VP2 capsid protein and was characterized in detail. Mutational analysis of the epitope showed that the aspartic acid to asparagine mutation of the EV71 subgenogroup A (BrCr strain) did not interfere with antibody recognition. In contrast, the serine to threonine mutation at position 144 of VP2, present in recently emerged EV71-C4 China strains, abolished antigenicity. Mice injected with this virus strain did not produce any antibodies against the VP2 protein. Immunofluorescence and Western blotting confirmed that 7C7 specifically recognized EV71 subgenogroups and did not cross-react to Coxsackieviruses 4, 6, 10, and 16. 7C7 was successfully used as a detection antibody in an antigen-capture ELISA assay. CONCLUSIONS: Detailed mapping showed that the VP2 protein of Enterovirus 71 contains a single, linear, non-neutralizing epitope, spanning amino acids 142-146 which are located in the VP2 protein's E-F loop. The S/T(144) mutation in this epitope confers a loss of VP2 antigenicity to some newly emerged EV71-C4 strains from China. The corresponding monoclonal antibody 7C7 was used successfully in an AC-ELISA and did not cross-react to coxsackieviruses 4, 6, 10, and 16 in immunofluorescence assay and Western blots. 7C7 is the first monoclonal antibody described, that can differentiate Coxsackievirus 16 from Enterovirus 71.

Project description:BackgroundAround 30% of the HCV infected patients can spontaneously clear the virus. Cumulative evidence suggests the role of neutralizing antibodies in such spontaneous resolution. Understanding the epitope specificity of such antibodies will inform the rational vaccine design as such information is limited to date. In addition to conformational epitope targeted antibodies, linear epitope specific antibodies have been identified that are broadly cross reactive against diverse HCV strains. In this study, we have characterized the potential role of three conserved linear epitopes in the spontaneous clearance of HCV.MethodsWe tested the reactivity of sera from chronic patients (CP) and spontaneous resolvers (SR) with linear peptides corresponding to three conserved regions of HCV envelope protein E2 spanning amino acids 412-423, 523-532 and 432-443 using ELISA. Subsequently, we characterized the dependency of HCV neutralization by the reactive serum samples on the antibodies specific for these epitopes using pseudoparticle-based neutralization assay. In ELISA most of the CP sera showed reactivity to multiple peptides while most of the SR samples were reactive to a single peptide suggesting presence of more specific antibodies in the SR sera. In most of the HCVpp neutralizing sera of particular peptide reactivity the neutralization was significantly affected by the presence of respective peptide. HCV neutralization by CP sera was affected by multiple peptides while 75% of the HCVpp neutralizing SR sera were competed by the 432 epitope.ConclusionsThese findings suggest that individuals who spontaneously resolve HCV infection at the acute phase, can produce antibodies specific for conserved linear epitopes, and those antibodies can potentially play a role in the spontaneous viral clearance. The epitope present in the 432-443 region of E2 was identified as the primary neutralizing epitope with potential role in spontaneous viral clearance and this epitope potentiates for the design of immunogen for prophylactic vaccine.

Project description:BackgroundShort linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1).ResultsWe implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions.ConclusionWe have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.