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Background: The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. Results: Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate “induced fit” biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training. Conclusions: With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0. [ABSTRACT FROM AUTHOR]

Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.

Background: The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies., Results: Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training., Conclusions: With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications., Competing Interests: All authors are employees at the company NEC OncoImmunity AS., (© 2024 The Authors.)

Acinetobacter baumannii is a well-recognized cause of nosocomial infections. This organism is recognized to be among the most difficult antimicrobial-resistant gram-negative bacilli to control and treat. One of the main challenges we face is Carbapenem resistance in A. baumannii. Carbapenem resistance in A. baumannii associated with the loss of an outer membrane protein designated CarO (Carbapenem resistance outer membrane protein). This protein is a membrane porin of A. baumannii. Using specific antibodies against this protein exert a bacteriostatic or bactericidal effect in vitro. Attempts should be made to discover peptides that could mimic protein epitopes and possess the same immunogenicity as the complete protein. Subsequently, bioinformatics methods for epitope prediction have been developed leading to synthesis of such peptides that are important for development of vaccine. This study provides a basis for the design of pathogen specifically, B cell epitope-based vaccine that is targeted to diseases caused by A. baumannii in the global human population. A combination of available bioinformatics tools are used to understand and characterize the Baumannii Acinetobactin utilization structure of A. baumannii and appropriate selection regions as effective B cell epitopes and functional exposed amino acids. In conclusion, amino acids 19–158 were selected as vaccine candidate. [ABSTRACT FROM AUTHOR]

Immunoinformatics is a modern branch of science formed as a result of the intersection between immunology and computer science. One of the important steps in the design of multi-epitope vaccines is the prediction of B cell epitopes. B cell epitopes are of two types, linear and discontinuous. Linear epitope residues lie next to each other in the primary structure of a protein. The amino acids that constitute discontinuous epitopes lie close to each other in the three-dimensional structure of the protein. Recognition of B cell epitopes by antibodies on an antigen constitutes an important event in the immune responses toward the antigenic challenge and also forms the basis for several immunological applications. Prediction of B cell epitopes in an antigen constitutes one of the important steps in the design of multi-epitope-based vaccines. This chapter explains the prediction of linear B cell epitopes in an antigen as well as their allergenicity, antigenicity, and toxicity by using online tools., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Multidrug-resistant Acinetobacter baumannii is recognized to be among the most difficult antimicrobial-resistant gram negative bacilli to control and treat. One of the major challenges that the pathogenic bacteria face in their host is the scarcity of freely available iron. To survive under such conditions, bacteria express new proteins on their outer membrane and also secrete iron chelators called siderophores. Antibodies directed against these proteins associated with iron uptake exert a bacteriostatic or bactericidal effect against A. baumanii in vitro, by blocking siderophore mediated iron uptake pathways. Attempts should be made to discover peptides that could mimic protein epitopes and possess the same immunogenicity as the whole protein. Subsequently, theoretical methods for epitope prediction have been developed leading to synthesis of such peptides that are important for development of immunodiagnostic tests and vaccines. The present study was designed to in silico resolving the major obstacles in the control or in prevention of the diseases caused by A. baumannii. We exploited bioinformatic tools to better understand and characterize the Baumannii acinetobactin utilization structure of A. baumannii and select appropriate regions as effective B cell epitopes. In conclusion, amino acids 26-191 of cork domain and 321-635 of part of the barrel domain including L4-L9, were selected as vaccine candidates. These two regions contain functional exposed amino acids with higher score of B cell epitopes properties. Majority of amino acids are hydrophilic, flexible, accessible, and favorable for B cells from secondary structure point of view. [ABSTRACT FROM AUTHOR]