Asti Lorenzo, Uguzzoni Guido, Marcatili Paolo, Pagnani Andrea
Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza University of Roma, Roma, Italy.
Human Genetics Foundation, Molecular Biotechnology Center, Torino, Italy.
PLoS Comput Biol. 2016 Apr 13;12(4):e1004870. doi: 10.1371/journal.pcbi.1004870. eCollection 2016 Apr.
The immune system has developed a number of distinct complex mechanisms to shape and control the antibody repertoire. One of these mechanisms, the affinity maturation process, works in an evolutionary-like fashion: after binding to a foreign molecule, the antibody-producing B-cells exhibit a high-frequency mutation rate in the genome region that codes for the antibody active site. Eventually, cells that produce antibodies with higher affinity for their cognate antigen are selected and clonally expanded. Here, we propose a new statistical approach based on maximum entropy modeling in which a scoring function related to the binding affinity of antibodies against a specific antigen is inferred from a sample of sequences of the immune repertoire of an individual. We use our inference strategy to infer a statistical model on a data set obtained by sequencing a fairly large portion of the immune repertoire of an HIV-1 infected patient. The Pearson correlation coefficient between our scoring function and the IC50 neutralization titer measured on 30 different antibodies of known sequence is as high as 0.77 (p-value 10-6), outperforming other sequence- and structure-based models.
免疫系统已经发展出许多独特的复杂机制来塑造和控制抗体库。其中一种机制，即亲和力成熟过程，以类似进化的方式起作用：在与外来分子结合后，产生抗体的B细胞在编码抗体活性位点的基因组区域表现出高频突变率。最终，产生对其同源抗原具有更高亲和力抗体的细胞被选择并进行克隆扩增。在此，我们提出一种基于最大熵建模的新统计方法，其中从个体免疫库序列样本中推断出与抗体针对特定抗原的结合亲和力相关的评分函数。我们使用我们的推断策略，对通过对一名HIV-1感染患者的相当大部分免疫库进行测序而获得的数据集推断出一个统计模型。我们的评分函数与在30种已知序列的不同抗体上测得的IC50中和效价之间的皮尔逊相关系数高达0.77（p值为10-6），优于其他基于序列和结构的模型。
