Amino Acid Composition among Linear B-Cell Epitope of Different Organisms-A Statistical Study

This study hypothesized that, linear B-cell epitopes of different organism possesses different preferential amino acid residues and hence, an organism specific linear B-cell epitope prediction method could be designed, which in turn could enhance the accuracy of the linear B-cell epitope prediction. Introduction T-cell and B-cell epitopes are comprehensively studied due to their potential in synthetic vaccine design [1]. B-cell (Linear) Epitopes (LBE), in particular, play major role in immunodiagnostics of specific disease or condition. Beside several computational methods exist for the prediction of LBE, organism specific epitope prediction methods are in lacunae [2-4]. This may be due to the thought that the general prediction methods are good enough to predict the LBE accurately. However, we hypothesize that the LBE of different organism possesses different preferential amino acid residues, which can influence the accuracy of the prediction methods. Hence, in this study, to explore the amino acid preference or differences in LBE of different organisms, experimentally verified LBE of Leishmania, Plasmodium, Mycobacterium, Mus Musculus, and Arachis hypogea were collected and compared. As hypothesized, statistically significant differences in amino acid composition were observed between the difference organisms. Materials and Method Experimentally verified organism specific Linear B-cell Epitopes (LBE) were collected from IEDB database [5]. Duplicate entries and epitopes of length less than 5 were not used for this study; the sequence pre-processing was done using in-house perl scripts. Composition profiler was used to compute the statistical significance (based on two-sample t-test) between the dataset [6]. Results and Discussion The LBE of each organism were compared with all the SWISS-PROT entries and the statistically significant (p<0.005; 99.5% level) residues (enriched and depleted) were listed in Table 1 [7]. It could be observed from the results that the residues I (Isoleucine) and L (Leucine) were depleted in all the organisms LBE. This suggests that residues I and L are not preferred in the LBE. However, there are no residues in common that are preferred by LBE in all organisms. Closely related organism Leishmania and Plasmodium were enriched with the residues K (Lysin) and S (Serine), while in the other organisms, the same was not preferred. Similarly, the residue P (Proline) was enriched in Mycobacterium, Mus Musculus, and Arachis hypogea, but was not in Leishmania and Plasmodium. This result indicates that no single residue is commonly enriched in LBE of all organisms (included Vijayakumar Saravanan1* and Pradeep Das2 1Department of Statistics, Rajendra Memorial Research Institute of Medical Science, India 2Department of Molecular Biology, Rajendra Memorial Research Institute of Medical Science, India Organism Enricheda Depleteda Leishmania A,K,S I,L Plasmodium D,E,K,N,S A,F,G,H,I,L,M,R,T,V,W Mycobacterium A,G,P,W C,E,F,H,I,K,L Mus Musculus G,H,P,Q,R,W,Y A,F,I,L,V Arachis hypogea E,P,Q,R A,I,K,L,M,T,V Table 1: Statistically significant enriched and depleted residues of linear B-cell epitopes against SWISS-PROT. aStatistical significance (p<0.005; 99.5% level) based on two-sample t-test; Residues common in all organisms were bold faced. Vijayakumar Saravanan, et al., Annals of Biometry and Biostatistics Remedy Publications LLC. 2019 | Volume 1 | Issue 1 | Article 1003 2 in this study). To observe the enriched and depleted residues between the LBE’s of different organisms, Leishmania epitopes and each other organisms were subjected to composition profiling. The results of the analysis were plotted in Figure 1 and the corresponding statistically significant (p<0.005; 99.5% level) residues (enriched and depleted) were listed in Table 2. Residues K and S are enriched in Leishmania LBE on comparison to other organism LBE. It could also be observed that the amino acid preference of each organism LBE differ significantly with respect to each other. Since the amino acid preferences of LBE differ with each organism, a general LBE prediction method may not be sufficient for the more accurate prediction of LBE. Hence, more specific organism specific LBE prediction methods should be developed, in order to achieve efficient LBE predictions. Conclusion Linear B-cell epitopes are of high importance in the field of vaccine development. This study was intended to study the difference in composition of amino acid residues among linear B-cell epitopes of different organisms. Despite several state-of-the-art epitope predictions methods are available for LBE prediction, no tools are available for organism specific LBE prediction. Hence, the results of this study suggest that organism specific LBE predictions methods Figure 1: Amino acid composition profiles of LBE of different organisms. A) Comparison of Leishmania and Plasmodium LBE. B) Comparison of Leishmania and Mycobacterium LBE. C) Comparison of Leishmania and Pea nut LBE. D) Comparison of Leishmania and Mus musculus LBE. Organism Enricheda Depleteda Le Plasmodium A,F,M,P,R,V E,I,K,N,Y Le Mycobacterium C,E,H,K,S G,I,L,W Le Arachis hypogea A,K,M,S,T,V C,E,N,P,Q,R,Y Le Mus musculus A,D,K,S G,I,R,W,Y Table 2: Statistically significant enriched and depleted residues of Leishmania linear B-cell epitope against other organism linear B-cell epitopes. aStatistical significance (p<0.005; 99.5% level) based on two-sample t-test; Le Leishmania could improve the prediction accuracy of LBE, in comparison to the general LBE prediction methods. Acknowledgement The authors thank Indian Council for Medical Research (ICMR) New Delhi and Rajendra Memorial Research Institute of Medical Sciences (RMRIMS) for providing necessary computational facility for carrying out this work. References 1. Palatnik-de-Sousa CB, Soares IS, Rosa DS. Editorial: Epitope discovery and Synthetic Vaccine design. Front Immunol. 2018;9:826. 2. Saravanan V, Gautham N. Harnessing computational biology for exact linear B-cell epitope prediction: a novel amino acid composition-based feature descriptor. OMICS. 2015;19(10):648-58. 3. Dhanda SK, Usmani SS, Agrawal P, Nagpal G, Gautam A, Raghava GPS. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief Bioinform. 2017;18(3):467-78. 4. Saravanan V, Gautham N. [BCIgEPRED—a Dual-Layer Approach for Predicting Linear IgE Epitopes]. Mol Biol. 2018;52(2):333-43. 5. Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD, Cantrell JR, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 2015;43:D405-12. 6. Vacic V, Uversky VN, Dunker AK, Lonardi S. Composition Profiler: a tool for discovery and visualization of amino acid composition differences. BMC Bioinformatics. 2007;8:211. 7. Boutet E, Lieberherr D, Tognolli M, Schneider M, Bansal P, Bridge AJ, et al. UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase: how to use the entry view. Methods Mol Biol. 2016;1374:23-54.