Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics

Ying-Tsang Lo,Hao-Teng Chang,Tun-Wen Pai,Wei-Kuo Wu

doi:10.1186/1471-2105-14-s4-s3

Abstract

BackgroundA conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow.ResultsWe prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen.ConclusionsOur method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.

Highlights

A B-cell epitope, known as an antigenic determinant, is the surface portion of an antigen that interacts with a B-cell receptor and/or an antibody to elicit either a cellular or humoral immune response [1,2]
In this report, we present a new conformational epitope (CE) predictor system called CE-KEG that combine an energy function computation for surface residues and the importance of occurred neighboring residue pairs on the antigen surface based on previously known CEs
To verify the performance of CE-KEG, we tested it with datasets of 247 antigen structures and 163 non-redundant protein structures that had been obtained from three benchmark datasets in conjunction with a 10-fold cross-validation assessment

Summary

Introduction

A B-cell epitope, known as an antigenic determinant, is the surface portion of an antigen that interacts with a B-cell receptor and/or an antibody to elicit either a cellular or humoral immune response [1,2] Because of their diversity, B-cell epitopes have a huge potential for immunology-related applications, such as vaccine design and disease prevention, diagnosis, and treatment [3,4]. A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen’s surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. The frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow

Methods

Results

Conclusion