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

Abstract

Background 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. 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. Results We 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. Conclusions Our 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.

Epitope Prediction from Genes Encoding F Protein of Newcastle Disease Virus (NDV) Isolates Swan (Cygnus Olor) for Vaccine Development to Prevent Infectious Disease

“…The epitope prediction tools could facilitate the development of vaccines and predict the epitopes [15] . Vaccine production that depends on biochemical experiments can be expensive, time consuming and not always work, although this vaccine formulation of attenuated or inactivated microorganism contains a few hundred of unnecessary proteins for the induction of immunity, that may cause allergenic or reactogenic responses [16,17] . Therefore, prediction of epitopes of appropriate protein residues would help in production of peptide vaccine with powerful immunogenic and minimal allergenic effect [18,19] .…”

Vacunas, adyuvantes y bacteriófagos como vectores vacunales

“…El éxito en el diseño de vacunas efectivas se basa en la comprensión de los mecanismos de respuesta inmune inducidos por la vacuna o el adyuvante, en la unión del Ag a la célula efectora la cual determina el tipo de respuesta inmune que se generará, pero principalmente en la identificación de epítopos inmunogénicos e inmunoprotectores (12). Otro aspecto a considerar durante el diseño de vacunas es la conformación del epítopo, es decir, si ésos son lineales (una secuencia continua de aminoácidos) o conformacionales (secuencia discontinua de aminoácidos) que adquieren una estructura tridimensional (86). La conformación de los epítopos determina su capacidad para estimular selectivamente determinadas poblaciones celulares; mientras que los lineales se unen a las células T cuando se presentan en las moléculas de MHC I o MHC II, la mayoría de los epítopos que se unen al receptor de células B (aproximadamente el 90%) son epítopos conformacionales (87).…”

A Review of Important Discontinuous B-Cell Epitope Prediction Tools

“…Propensity scale methods are the most common way by which linear B-cell epitopes are predicted and they are entirely dependent on the primary structure of the proteins [22,23]. The original propensity scale methods make use of hydrophilicity [24], secondary structure [25,26] and side-chain solvent accessibility [27] in their algorithms [23]. Modern linear epitope algorithms make use of a combination of propensity scale methods, but have been shown to only be marginally better at predicting linear epitopes [1,28].…”