Three strategies for mapping T cell epitopes

Epitope mapping is the process of identifying the specific peptide sequences on an antigen that elicit an immune response - either by binding to antibodies or MHC molecules. Epitope identification requires a systematic screening of the antigen, which can be difficult when the antigen has multiple conformations or binding domains. Precise and quick identification of these epitopes is powered by high-throughput peptide delivery formats, such as libraries, that span hundreds of peptide combinations simultaneously. GenScript provide a variety of 7 online peptide library design tools for free.


Figure 1. Free online peptide library design tools in GenScript

The overlapping peptide library is the most common tool for short, linear and continuous epitope mapping. Characterized by two parameters, peptide length and offset number, each library is generated by dividing the original protein or peptide into many overlapping peptides of equal length. Choice of the appropriate peptide length and offset number depends on the application of the peptides and also the cost of the peptide set. As a general guideline, the length of peptide should be at least six residues to cover an epitope. The offset number reflects the degree of overlap and it usually designed to be 1/3 the peptide length.

The most common applications of overlapping peptide library are for T cells and antibodies epitope mapping. T cell epitopes are short peptide sequences, ranging from 8-11 AAs on MHC-I and 15-24 AAs on MHC-II complexes. In most cases, peptides are 15 amino acids in length and overlapping by 11 amino acids spanning the complete sequence of an antigen. There are three strategies for mapping T cell epitopes: “Test all”, Matrix-pool and Mini-pool.

“Test all” is the classic approach, the peptides are plated individually and each in its own well, in progressive order. Although this is the most precise approach, when confronted with extensively large number of peptides in a library, scientists prefer to use peptide pools.


Fig. 2. Illustration of the epitope mapping strategies. Peptide pool size and organization for the Mini-pool (A) and Matrix-pool (B) strategies (Fiore-Gartland, Andrew, et al.).

Matrix pool is an economic strategy for identification of target peptides, and is capable of reducing the number of test wells without losing the information of peptide specificity. The design of a matrix ensures that each particular peptide in two separate pools. In the figure 3 shown 100 peptides are pooled into 20 matrix pools. In the left example, pools C8 and R6 test positive, indicating that the target peptide is p58. In figure 3B, pools C5, C6, C8, R3, R4, and R6 test positive, indicating that p25, p26, p28, p35, p36, p38, p55, p56, or p58 may be recognized. Further testing of individual peptides in this example is necessary to reveal that the targeted peptides are actually p26, p35, and p58. 


Fig. 3. Peptide Matrix. (Anthony, Donald D., and Paul V. Lehmann.)

The approach of matrix pool works best when only one peptide is targeted. However, the results can become difficult to interpret in terms of specificity when multiple peptides are targeted. Moreover, the peptide matrix pool must have no overlapping peptides in the same pool when overlapping peptide series are tested. 

An alternative strategy is to map an epitope with Mini-pool in two subsequent steps (Fig. 1 and Fig. 4). In figure 4, the overlapping peptides are divided into five mini pools, each containing roughly equal number of peptides, the pool D is the positive mini pool. In step 2, each peptides within a positive mini pool are tested and the peptide 18 is targeted. Typically, in pooling based mapping strategy, only the positive pools are tested in subsequent stages. In any strategy a positive response must be confirmed using single peptide stimulation.



Fig. 4. Combined method for identification of target peptide within peptide array. (Anthony, Donald D., and Paul V. Lehmann.)

An appropriate approach for epitope mapping depends on the applications, the size of the test, and also the cost of the peptide set. At GenScript, the PhD level scientists have rich experiences in peptide synthesis, pooling and design. If you are interested in more economical and higher quality peptide libraries for epitope mapping, please contact us to discuss your projects at peptide@genscript.com. Find more custom peptide services and free resources at https://www.genscript.com/peptide-services.html?

As mentioned previously, mapping epitopes with peptide libraries becomes one of the valuable tools for vaccine development by identifying the specific peptide antigen that elicit an immune response. The global outbreak of novel coronavirus continues to threaten lives, and now report shows that there are more than 100 research groups racing to develop COVID-19 vaccine. To meet the increasing demand in COVID-19 research, GenScript developed a series of SARS-CoV-2 Peptide Libraries  which can be used for T-cell assays, immune monitoring, Antigen specific T-cell stimulation, T-cell expansion and Cellular immune response. Additionally, a collection of most popular antigen peptides published in journals are available on Molecular Cloud. Click here and find more details.  

Reference

1. Fiore-Gartland, Andrew, et al. "Pooled-peptide epitope mapping strategies are efficient and highly sensitive: an evaluation of methods for identifying human T cell epitope specificities in large-scale HIV vaccine efficacy trials." PloS one 11.2 (2016).

2. Sospedra, Mireia, Clemencia Pinilla, and Roland Martin. "Use of combinatorial peptide libraries for T-cell epitope mapping." Methods 29.3 (2003): 236-247.

3. Weiss, Y., et al. "Epitope mapping: the first step in developing epitope-based vaccines." Biodrugs (2006).

4. Hemmer, B., et al. "The use of soluble synthetic peptide combinatorial libraries to determine antigen recognition of T cells." The Journal of peptide research 52.5 (1998): 338-345.

5. Sung, Myong-Hee, et al. "T-cell epitope prediction with combinatorial peptide libraries." Journal of Computational Biology 9.3 (2002): 527-539.

6. Paulmurugan, Ramasamy, and Sanjiv S. Gambhir. "Combinatorial library screening for developing an improved split-firefly luciferase fragment-assisted complementation system for studying protein− protein interactions." Analytical chemistry 79.6 (2007): 2346-2353

7. Anthony, Donald D., and Paul V. Lehmann. "T-cell epitope mapping using the ELISPOT approach." Methods 29.3 (2003): 260-269. 


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