Abstract
MotivationHigh-throughput sequencing of large immune repertoires has enabled the development of methods to predict the probability of generation by V(D)J recombination of T- and B-cell receptors of any specific nucleotide sequence. These generation probabilities are very non-homogeneous, ranging over 20 orders of magnitude in real repertoires. Since the function of a receptor really depends on its protein sequence, it is important to be able to predict this probability of generation at the amino acid level. However, brute-force summation over all the nucleotide sequences with the correct amino acid translation is computationally intractable. The purpose of this paper is to present a solution to this problem.ResultsWe use dynamic programming to construct an efficient and flexible algorithm, called OLGA (Optimized Likelihood estimate of immunoGlobulin Amino-acid sequences), for calculating the probability of generating a given CDR3 amino acid sequence or motif, with or without V/J restriction, as a result of V(D)J recombination in B or T cells. We apply it to databases of epitope-specific T-cell receptors to evaluate the probability that a typical human subject will possess T cells responsive to specific disease-associated epitopes. The model prediction shows an excellent agreement with published data. We suggest that OLGA may be a useful tool to guide vaccine design.Availability and implementationSource code is available at https://github.com/zsethna/OLGA.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
The ability of the adaptive immune system to recognize foreign peptides, while avoiding self peptides, depends crucially on the specificity of receptor-antigen binding and the diversity of the receptor repertoire
We present a solution to this problem in the form of an algorithm and computational tool, called OLGA, which implements an exact computation of the generation probability of any BCR or TCR sequence, or motif
To verify the correctness of the OLGA code, we compared its predictions for generation probabilities to those estimated by Monte Carlo (MC) sequence generation (Pogorelyy et al, 2018a)
Summary
The ability of the adaptive immune system to recognize foreign peptides, while avoiding self peptides, depends crucially on the specificity of receptor-antigen binding and the diversity of the receptor repertoire. Recent work has shown that responding clonotypes often form disjoint clusters of similar amino acid sequences, which has lead to the identification of responsive amino acid motifs (Dash et al, 2017; Glanville et al, 2017). In order for these techniques to have practical applications in therapy and vaccine design, one needs a fast and efficient algorithm to evaluate which specific amino acid sequences and sequence motifs are likely to be generated and found in repertoires. We present a solution to this problem in the form of an algorithm and computational tool, called OLGA, which implements an exact computation of the generation probability of any BCR or TCR sequence (nucleotide or amino acid), or motif
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