OLGA：快速计算B细胞和T细胞受体氨基酸序列和基序的生成概率
OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs
摘要：
动机：大免疫库的高通量测序使得能够开发预测任何特定核苷酸序列的T细胞和B细胞受体通过V（D）J重组产生的可能性的方法。这些生成概率是非常不均匀的，在真实的剧目中超过20个数量级。由于受体的功能实际上取决于其蛋白质序列，因此能够在氨基酸水平上预测产生的概率是很重要的。然而，对所有具有正确氨基酸翻译的核苷酸序列进行蛮力求和在计算上是难以处理的。本文的目的是提出一个解决这个问题的方法。结果如下：我们使用动态规划来构建一种高效灵活的算法，称为OLGA（Optimized Likestimate of immunoGlobulin Amino Acid Sequences），用于计算在B或T细胞中由于V（D）J重组而产生给定的CDR 3氨基酸序列或基序（有或没有V/J限制）的概率。我们将其应用于表位特异性T细胞受体的数据库，以评估典型的人类受试者将拥有对特定疾病相关表位有反应的T细胞的概率。模型预测结果与已发表的数据吻合良好。我们认为OLGA可能是一个有用的工具，以指导疫苗的设计。可用性：源代码可在https://github.com/zsethna/OLGA上获得
英文摘要：
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. Availability: Source code is available at https://github.com/zsethna/OLGA
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
