Use of nanocolonies to detect minimal residual disease in patients with leukemia t(8;12)

Falaleeva, Marina; Chetverina, Helena V.; Kravchenko, A. V.; Chetverin, Alexander B.

doi:10.1134/s0026893309010221

Cited by 8 publications

(11 citation statements)

References 33 publications

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“…We applied three datasets to compare the performance of DLBEpitope model with ABCpred [ 7 ], BepiPred1.0 [ 8 ], BepiPred2.0 [ 11 ], AAPpred [ 14 ], and APCpred [ 10 ] models. The first dataset was derived from each IEDBx (x = 16, 18, 20, 22, 31, and 38).…”

Section: Resultsmentioning

confidence: 99%

“…Here, the lengths of 16, 18, and 20 were selected because ABCpred [ 7 ] and APCpred [ 10 ] models only provided these options. Additionally, the AAPpred [ 14 ] model only considers the length of 20 AAs. The reason to choose the lengths of 22, 31, and 38 is that the AUC values of these lengths were the local maximum (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In Bepipred2.0 [ 11 ], the random forest methods were applied to develop models with the annotated PDB data as the training and test datasets. Especially, the support vector machines were extensively used in developing models, which included AAP [ 12 ], BCpred and FBCpred [ 13 ], AAPpred [ 14 ], Bayesb [ 15 ], LEPS [ 16 ], SVMTriP [ 17 ], BEST [ 18 ], Lbtope [ 9 ], and APCpred [ 10 ]. Although these models have been developed, their performance is still away from satisfactory level.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning methods improve linear B-cell epitope prediction

Liu

Shi

2020

BioData Mining

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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/.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning methods improve linear B-cell epitope prediction

Liu

Shi

2020

BioData Mining

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“…Previous studies have reported that humoral immunity is induced in earlier phases of infection in BLV-infected animals [ 30 – 33 ]. The B cells of the immune system recognize the pathogen’s antigens by their membrane-bound immunoglobulin receptors and, in response, produce antibodies specific to these antigens; antibodies bind to antigens at specific sites that correspond to the antigenic determinants or B cell epitopes [ 34 – 36 ]. Therefore, identification and characterization of common B cell epitopes in target antigens is a key step in antibody production, epitope-driven vaccine design, and immunodiagnostic tests.…”

Section: Introductionmentioning

confidence: 99%

Identification and characterization of common B cell epitope in bovine leukemia virus via high-throughput peptide screening system in infected cattle

et al. 2015

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Background Bovine leukemia virus (BLV) is the causative agent of enzootic bovine leukosis, the most common neoplastic disease of cattle. BLV is closely related to human T cell leukemia virus. B cell epitopes are important for the use of antibodies as therapeutic agents, the epitope-driven vaccine design, and immunological assays. A common B cell epitope for BLV has not yet been found due to individual differences in disease susceptibility.ResultsWe used a peptide microarray with 156 synthetic 15-mer peptides covering the envelope glycoprotein gp51 and the Gag proteins p15, p24, and p12 to map B cell epitope and one B cell epitope, gp51p16, was recognized by all four cattle experimentally infected with BLV. A newly developed high-throughput peptide ELISA system revealed 590 (91.2 %) of 647 cattle naturally infected with BLV, carrying 25 different bovine leukocyte antigen class II DRB3 (BoLA-DRB3) alleles, responded to a 20-mer gp51p16-C peptide containing a C-terminal cysteine and gp51p16. Alanine mutation and comparison of the sequences at 17 amino acid positions within gp51p16-C revealed that R7, R9, F10, V16, and Y18 were the common binding sites to BLV antibodies, and two of these sites were found to be highly conserved. Transient expression in the cells of five infectious molecular clones of BLV with a single alanine mutation at five common antibody binding sites had no effect syncytia formation of the gp51 protein. In addition, the mutant proteins, R7A and R9A had no effect on the expression of gp51 protein; the gp51 protein expressions of F10A, V16A and Y18A were lower than that of the wild type protein.ConclusionsThis is the first report to identify a common B cell epitope in BLV by comprehensive screening of BLV-infected cattle with varied genetic backgrounds in BoLA-DRB3. Our results have important implications for disease control and diagnosis.

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“…After 30 years of research, it is known that the optimum size of peptides possessing cross-reactive immunogenicity is between 10-15 amino acids [ 13 ]. The earliest efforts made to understand and predict B-cell epitopes were based on the amino acid properties, such as flexibility [ 14 ], hydrophaty [ 15 ], antigenicity [ 7 ], beta turns [ 16 ] and accessibility [ 17 ]. Epitope prediction is important to design epitope-based vaccines and precise diagnostic tools such as diagnostic immunoassay for detection, isolation and characterization of associated molecules for various disease states.…”

Section: Introductionmentioning

confidence: 99%

Classification epitopes in groups based on their protein family

et al. 2015

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BackgroundThe humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope.ResultsWe investigated the possibility of linear epitopes from the same protein family to share common properties. This hypothesis led us to analyze physico-chemical (PCP) and predicted secondary structure (PSS) features of a curated dataset of epitope sequences available in the literature belonging to two different groups of antigens (metalloproteinases and neurotoxins). We discovered statistically significant parameters with data mining techniques which allow us to distinguish neurotoxin from metalloproteinase and these two from random sequences. After a five cross fold validation we found that PCP based models obtained area under the curve values (AUC) and accuracy above 0.9 for regression, decision tree and support vector machine.ConclusionsWe demonstrated that antigen's family can be inferred from properties within a single group of linear epitopes (metalloproteinases or neurotoxins). Also we discovered the characteristics that represent these two epitope groups including their similarities and differences with random peptides and their respective amino acid sequence. These findings open new perspectives to improve epitope prediction by considering the specific antigen's protein family. We expect that these findings will help to improve current computational mapping methods based on physico-chemical due it's potential application during epitope discovery.

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Use of nanocolonies to detect minimal residual disease in patients with leukemia t(8;12)

Cited by 8 publications

References 33 publications

Deep learning methods improve linear B-cell epitope prediction

Deep learning methods improve linear B-cell epitope prediction

Identification and characterization of common B cell epitope in bovine leukemia virus via high-throughput peptide screening system in infected cattle

Classification epitopes in groups based on their protein family

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