OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs
OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs
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DOI：
10.1093/bioinformatics/btz035
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发表时间：
2019-09-01
期刊：
影响因子：
5.8
通讯作者：
Mora, Thierry
中科院分区：
文献类型：
--
作者：
Sethna, Zachary;Elhanati, Yuval;Mora, Thierry
动机：大型免疫谱系的高通量测序使预测任何特定核苷酸序列的T细胞和B细胞受体V(D)J重组产生的可能性的方法得以开发。这些生成概率是非常不均匀的，在真实剧目中的范围超过20个数量级。由于受体的功能实际上取决于它的蛋白质序列，因此能够在氨基酸水平上预测这种产生的可能性是很重要的。然而，对所有核苷酸序列和正确的氨基酸翻译进行蛮力求和在计算上是困难的。结果：我们用动态规划法构造了一种高效灵活的算法，称为免疫球蛋白氨基酸序列的最优似然估计算法，用于计算由于V(D)J在B细胞或T细胞中的重组而产生给定的CDR3氨基酸序列或基序的概率，无论是否有V/J限制。我们将其应用于表位特异性T细胞受体的数据库，以评估典型人类受试者拥有对特定疾病相关表位反应的T细胞的可能性。模型预测结果与已发表的数据吻合较好。我们认为，Olga可能是指导疫苗设计的有用工具。
Motivation: High-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.Results: We 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.