Tea bag synthesis of positional scanning synthetic combinatorial libraries and their use for mapping antigenic determinants.

Abstract

AbstractPositional scanning synthetic combinatorial libraries (PS-SCLs) are useful in identifying the amino acid sequences of antigenic determinants recognized by monoclonal antibodies (MAbs). These libraries are typically composed, in total, of tens of millions of nonsupport-bound peptides, and can be readily screened using enzyme-linked immunosorbent assay (ELISA) to determine specific sequences that bind to a target antibody. From our studies using peptide libraries, we have found that MAbs exhibit a broad range of specificities, ranging from those recognizing only conservative substitutions at one or two positions in the antigenic determinant to antibodies that recognize sequences completely unrelated to the parent antigen while having comparable affinities (1–10). The information derived from the screening of PS-SCLs is similar to that of “fingerprint” profiles using individual substitution analogs at each position of the antigenic determinant (11). For example, the more specific a position is, the fewer amino acids in the peptide mixtures that will be found to be effective. On the other hand, if there are many active peptide mixtures for a given defined position, then that position can be considered redundant or replaceable. In other instances, there may be several amino acids that are conservative replacements of each other that have equal activities. Additionally, since the diversities of PS-SCLs consist of millions of sequences, the extent of the multiple binding specificities of a given antibody can be readily addressed in a systematic manner. Finally, it should be noted that a single PS-SCL can be used to identify antigenic determinants and high-affinity sequences for many different antibodies.KeywordsAntigenic DeterminantPeptide MixturePeptide LibraryCompetitive ELISAIndividual PeptideThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.