scholarly journals A Structure-based B-cell Epitope Prediction Model Through Combing Local and Global Features

2021 ◽  
Author(s):  
Shuai Lu ◽  
Yuguang Li ◽  
Xiaofei Nan ◽  
Shoutao Zhang
Keyword(s):  
Amino Acid ◽  
B Cell ◽  
Amino Acid Residue ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Local Features ◽  
Superior Performance ◽  
Global Features ◽  
Local And Global Features ◽  
Target Amino Acid

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of epitopes is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for epitope prediction at much lower cost. Moreover, most of the currently computational methods focus on using local information around target amino acid residue for BCEs prediction without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method thorough combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use graph convolutional networks to capture information of spatial neighbors of a target amino acid residue. For global features, Attention based Bidirectional Long Short-Term Memory networks(Att-BLTM) are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction.

scholarly journals Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
B Cell ◽  
Recognition Performance ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Support Vector ◽  
B Cell Epitopes ◽  
Prediction Module ◽  
B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

B Cell Epitope Prediction

2013 ◽  
pp. 63-67
Author(s):  
Yasser EL-Manzalawy ◽  
Vasant Honavar
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
B Cell Epitope

scholarly journals INeo-Epp: A novel T-cell HLA class-I immunogenicity or neoantigenic epitope prediction method based on sequence related amino acid features

2019 ◽  
Author(s):  
Guangzhi Wang ◽  
Huihui Wan ◽  
Xingxing Jian ◽  
Yuyu Li ◽  
Jian Ouyang ◽  
...  
Keyword(s):  
Amino Acid ◽  
T Cell ◽  
Antigen Processing ◽  
External Validation ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Validation Data ◽  
Data Set ◽  
Immunogenic Peptide ◽  
Related Amino Acid

AbstractIn silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g. proteasomal cleavage, transporter associated with antigen processing (TAP) and major histocompatibility complex (MHC) combination. To date, however, the mechanism for immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including amino acid physicochemical property of epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score and frequency score for immunogenic peptide. Subsequently, a random forest classifier for T cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called ‘neoantigens’. Based on mutation information and sequence related amino acid characteristics, a prediction model of neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool ‘INeo-Epp’ was developed (available at http://www.biostatistics.online/INeo-Epp/neoantigen.php)for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

Sign in / Sign up

Export Citation Format

Share Document