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 pro- cess, works in an evolutionary-like fashion: after binding to a foreign molecule, the anti- body-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 affin- ity 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 sam- ple 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.
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity / Asti, Lorenzo; Uguzzoni, Guido; Marcatili, Paolo; Pagnani, Andrea. - In: PLOS COMPUTATIONAL BIOLOGY. - ISSN 1553-7358. - 12:4(2016). [10.1371/journal.pcbi.1004870]
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity
PAGNANI, ANDREA
2016
Abstract
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 pro- cess, works in an evolutionary-like fashion: after binding to a foreign molecule, the anti- body-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 affin- ity 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 sam- ple 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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2646124
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