Abstract
BackgroundA conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis.ResultsWe propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy.ConclusionsThe proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.
Highlights
A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure
In order to solve the problem of an undefined initial residue within a circular feature vector, we extend the original spiral feature vectors of known antigenic epitopes by repeating the vector twice and subtracting the last amino acid
In this study, we have adopted 12 newly annotated and non-redundant protein structures as a testing set for comparing our CE prediction system with all other systems available online
Summary
A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. Because of binding specificity characteristics, B-cell epitopes possess a huge potential for immunology-related applications, such as vaccine development, drug design and disease prevention, diagnosis and treatment [3, 4]. Clinical and biological researchers usually rely on biochemical/biophysical experiments to identify epitope-binding sites in B-cell receptors and/or antibodies, such experiments are expensive, time-consuming and not always successful [5]. By applying accurate epitope-prediction tools, immunologists can focus only on high-likelihood antigenic protein segments and reduce their experimental efforts. It was reported that computational methods could significantly reduce the epitope prediction time and costs of vaccine development [7,8,9]
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