Predicting linear B‐cell epitopes using string kernels

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

doi:10.1002/jmr.893

Cited by 647 publications

(480 citation statements)

References 51 publications

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“…Concatenated polyproteins of the sequences of each group were constructed by randomly combining all sequences. Similarly, concatenated polyproteins of SwissProt sequences were constructed by randomly combining all sequences of the BCP12, BCP14, or BCP18 datasets (20).…”

Section: B-cell Epitope Peptide Reactivity With Anti-chlamydialmentioning

confidence: 99%

“…Hydrophobicity (Miyazawa,30), a ProtScale (29) for hydrophobicity; Bepipred, a hidden Markov model combined with the Parker hydrophilicity scale (19); IUPred-L, an algorithm for protein disorder tendency (31). C, all 6 -20-aa epitopes and non-epitopes of the Lbtope_Confirm dataset (24) grouped into 6 -11-, 12-15-, and 16 -20-aa peptides are compared with Swiss-Prot 12-, 14-, and 18-aa peptides of the Bcpreds BCP12, BCP14, and BCP18 datasets (20). The p value for hydrophilicity score differences between epitopes and non-epitopes is shown in green and between non-epitopes and Swiss-Prot random peptides in blue.…”

Section: Lbtope_confirm Datasetmentioning

confidence: 99%

“…With the availability of B-cell epitope databases, antigenicity scales (17,18) and machine learning approaches (19 -24) have been attempted, and improved prediction accuracy has been reported. Nevertheless, due to epitope redundancy (20), the predictive power may have been overestimated because these algorithms performed poorly on independent data (24). Therefore, B-cell epitope prediction algorithms must be evaluated on independent datasets that had not been used to train/develop the algorithms/scales.…”

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confidence: 99%

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Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Rahman

Chowdhury

Sachse

et al. 2016

Journal of Biological Chemistry

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X-ray crystallography has shown that an antibody paratope typically binds 15-22 amino acids (aa) of an epitope, of which 2-5 randomly distributed amino acids contribute most of the binding energy. In contrast, researchers typically choose for B-cell epitope mapping short peptide antigens in antibody binding assays. Furthermore, short 6 -11-aa epitopes, and in particular non-epitopes, are over-represented in published B-cell epitope datasets that are commonly used for development of B-cell epitope prediction approaches from protein antigen sequences. We hypothesized that such suboptimal length peptides result in weak antibody binding and cause false-negative results. We tested the influence of peptide antigen length on antibody binding by analyzing data on more than 900 peptides used for B-cell epitope mapping of immunodominant proteins of Chlamydia spp. We demonstrate that short 7-12-aa peptides of B-cell epitopes bind antibodies poorly; thus, epitope mapping with short peptide antigens falsely classifies many B-cell epitopes as non-epitopes. We also show in published datasets of confirmed epitopes and non-epitopes a direct correlation between length of peptide antigens and antibody binding. Elimination of short, <11-aa epitope/non-epitope sequences improved datasets for evaluation of in silico B-cell epitope prediction. Achieving up to 86% accuracy, protein disorder tendency is the best indicator of B-cell epitope regions for chlamydial and published datasets. For B-cell epitope prediction, the most effective approach is plotting disorder of protein sequences with the IUPred-L scale, followed by antibody reactivity testing of 16 -30-aa peptides from peak regions. This strategy overcomes the well known inaccuracy of in silico B-cell epitope prediction from primary protein sequences.Knowledge of B-cell epitopes of proteins is essential in many fields of applied biomedical research, such as antibody diagnostics and therapeutics, vaccines, as well basic research. Laboratory methods for identification of such epitopes are time-consuming and labor-intensive. Hence, any reduction in the need for discovery and confirmatory wet-lab research by epitope prediction algorithms is highly desirable. Among in silico predictive methods from primary sequence information, epitope prediction algorithms are distinguished for their lack of reliability (1). This underperformance prompted us to examine current approaches to B-cell epitope prediction by use of extensive data on epitopes and confirmed non-epitope regions of the Chlamydia spp. proteome, accumulated in research on chlamydial molecular serology (2).Recent three-dimensional antibody-antigen complex studies (3-7) show that about 15-22-aa 2 antigen peptide residues are structurally involved in binding of epitopes to ϳ17-aa residues in antibody complementarity-determining regions (CDRs; paratopes). Among these 15-22 structural epitope residues, about 2-5 aa, termed functional residues, contribute most of the total binding energy to antibodies (6). These functional residues lie...

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Section: B-cell Epitope Peptide Reactivity With Anti-chlamydialmentioning

confidence: 99%

Section: Lbtope_confirm Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Rahman

Chowdhury

Sachse

et al. 2016

Journal of Biological Chemistry

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“…For prediction of B-cell epitopes, BCPred software was used to determine the continuous B cell epitope based on single characters, including antigenicity, hydrophilicity, flexibility, polarity, exposed surface and accessibility (26). Also, the conformational epitopes for B cells were predicted by the Discotope server (27).…”

Section: Prediction Of B-cell Epitopesmentioning

confidence: 99%

Applying Bioinformatic Tools for Modeling and Modifying Type II E. coli l-Asparginase to Present a Better Therapeutic Agent/Drug for Acute Lymphoblastic Leukemia

et al. 2017

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Background: Asparginase is known to be one of the most important bedrocks of acute lymphoblastic leukemia (ALL) treatment in almost all pediatric regimens in treatment protocols. Escherichia coli L-Asparginase (EC 3.5.1.1) is one of the most common resources to produce this enzyme. One of the affordable methods to overcome the side effects of drug is utilizing bioinformatic tools in the form of In silico study. In this study we designed a new structure of L-Asparginase to decrease its toxicity, reduce some side effects and increase the stability. Methods: We used some bioinformatics software and servers like Toxin red, Popmusic, kobami and I-TASSER server to reduce toxicity level of enzyme, and to increase stability and enzyme half-life. Results: We obtained 6 protein sequences in which the best was Mut 6 with four changes in structure: L23G, K129L, S263C and R291F. In contrast to the wild type, the new predicted protein is not toxic and has 25 hours more half-life and 600 kcal/mol more stable with no significant change in protein secondary, tertiary structure, antigenicity and allergenicity. Conclusions: Finally, sequence number 6 was the only sequence with all distinct characteristics: non-toxic, more stability and more half life.

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“…Bcepred server (http://www.imtech.res.in/-raghava/ bcepred) was used for prediction of continuous B-cell epitopes (20mers) of the Stx1 and Stx2 subunits sequences (EL-Manzalawy et al 2008a). Prediction of conformational B-cell epitope from protein 3D structures was performed using Elipro server (Ponomarenko et al 2008).…”

Section: Prediction Of Antigenic B-cell Epitopesmentioning

confidence: 99%

In silico analysis of Shiga toxins (Stxs) to identify new potential vaccine targets for Shiga toxin-producing Escherichia coli

Golshani

Oloomi

Bouzari

2017

In Silico Pharmacol.

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Shiga toxins belong to a family of structurally and functionally related toxins serving as the main virulence factors for pathogenicity of the Shiga toxin-producing Escherichia coli (STEC) associating with Hemolytic uremic syndrome (HUS). At present, there is no effective treatment or prevention for HUS. The aim of the present study was to find conserved regions within the amino acid sequences of Stx1, Stx2 (Shiga toxin) and their variants. In this regard, In-silico identification of conformational continuous B cell and T-cell epitopes was performed in order to introduce new potential vaccine candidates. 93-100% Homology was observed in Stx1 and its variants. In Stx2 and its variants, 69-100% homology was shown. By sequence alignment with Stx1 and Stx2, 54% homology was detected. T-cell epitope identification in Stx1A and Stx2A epitopes with highest binding affinity for each HLA (human leukocyte antigen) was demonstrated with 100% identity among all Stxs. B-cell epitope prediction was resulted in finding of four common epitopes between Stxs. In silico analysis of Stxs was resulted to identification of new peptide targets that could be used in development of new epitope vaccine candidates or in immunodiagnostic tests.

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Predicting linear B‐cell epitopes using string kernels

Cited by 647 publications

References 51 publications

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Applying Bioinformatic Tools for Modeling and Modifying Type II E. coli l-Asparginase to Present a Better Therapeutic Agent/Drug for Acute Lymphoblastic Leukemia

In silico analysis of Shiga toxins (Stxs) to identify new potential vaccine targets for Shiga toxin-producing Escherichia coli

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