Estimation and extraction of B‐cell linear epitopes predicted by mathematical morphology approaches

Abstract

B-cell epitope prediction facilitates the design and synthesis of short peptides for various immunological applications. Several algorithms have been developed to predict B-cell linear epitopes (LEs) from primary sequences of antigens, providing important information for immunobiological experiments and antibody design. This paper describes two robust methods, LE prediction with/without local peak extraction (LEP-LP and LEP-NLP), based on antigenicity scale and mathematical morphology for the prediction of B-cell LEs. Previous studies revealed that LEs could occur in regions with low-to-moderate but not globally high antigenicity scales. Hence, we developed a method adopting mathematical morphology to extract local peaks from a linear combination of the propensity scales of physico-chemical characteristics at each antigen residue. Comparison among LEP-LP/LEP-NLP, BepiPred and BEPITOPE revealed that our algorithms performed better in retrieving epitopes with low-to-moderate antigenicity and achieved comparable performance according to receiver operation characteristics (ROC) curve analysis. Of the identified LEs, over 30% were unable to be predicted by BepiPred and BEPITOPE employing an average threshold of antigenicity index or default settings. Our LEP-LP method provides a bioinformatics approach for predicting B-cell LEs with low- to-moderate antigenicity. The web-based server was established at http://biotools.cs.ntou.edu.tw/lepd_antigenicity. php for free use.

Prediction of B‐cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification

“…The original AntiJen dataset comprised 3619 epitopes, of which 3168 were found in the Swiss-Port database. As in our previous report, we regenerated the original AntiJen dataset by removing the repeated epitopes [12]. The HIV dataset focused on one infectious pathogen and was recognized as a useful tool in the field of HIV immunology [39].…”

“…The first step retrieved primitive epitope candidates from a query protein sequence with LEP [12], which was developed in our previous work and was used with the default settings. Then, an SVM classifier was applied to remove less probable epitope candidates and improve prediction accuracies.…”

“…Most published B-cell LE predictors have been based on the characteristics of amino acids, like hydrophobicity, surface accessibility, mobility, protrusion area, physico-chemical properties, antigenicity, and pocket characteristics [1, 3, 11–16]. For example, BcePred [16], BEPITOPE [17], PEOPLE [11], VaxiJen [18], and LEP [12] are bioinformatics tool that use various mathematical approaches to predict LEs according to the physico-chemical propensities of amino acids. Nevertheless, in 2005, Blythe and Flower led a group that evaluated the physico-chemical propensities of amino acids to predict LEs in proteins; they reported that even the best physico-chemical propensity scales available performed only slightly better than a random model [19].…”

“…LEP could retrieve some of the LEs that were locally embedded in the noise signals of the antigenic index [12]. We reasoned that prediction accuracies could be further improved and retain the advantage of variable length conditions, by combining the LEP with machine learning technologies.…”

B-Pred, a structure based B-cell epitopes prediction server

“…Of all the predicted epitopes, only the linear ones were considered in the analysis. For the LEPD11 and the CBTOPE 14 methods, the analysis was performed using the default parameters.…”