title: Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.
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 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.
authors.affiliation: Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza University of Roma, Roma, Italy.|Human Genetics Foundation, Molecular Biotechnology Center, Torino, Italy.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Asti
authors.affiliation: Human Genetics Foundation, Molecular Biotechnology Center, Torino, Italy.|Sorbonne Universités, UPMC, UMR 7238, Computational and Quantitative Biology, 15, rue de l'Ecole de Médecine - BC 1540 - 75006 Paris, France.|Dipartimento di Fisica, Universià di Parma, Parma, Italy.
authors.forename: Guido
authors.initials: G
authors.lastname: Uguzzoni
authors.affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.
authors.forename: Paolo
authors.initials: P
authors.lastname: Marcatili
authors.affiliation: Human Genetics Foundation, Molecular Biotechnology Center, Torino, Italy.|Department of Applied Science and Technologies (DISAT), Politecnico di Torino, Torino, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Pagnani
mesh_terms: D057134:Antibodies, Neutralizing / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D000915:Antibody Affinity / Q000235:genetics / Q000502:physiology*; D000937:Antigen-Antibody Reactions / Q000235:genetics / Q000502:physiology*; D001402:B-Lymphocytes / Q000276:immunology; D001666:Binding Sites, Antibody / Q000235:genetics / Q000502:physiology; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D019277:Entropy; D019143:Evolution, Molecular; D015483:HIV Antibodies / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D015658:HIV Infections / Q000235:genetics / Q000276:immunology; D015497:HIV-1 / Q000276:immunology; D006801:Humans; D018448:Models, Immunological; D008958:Models, Molecular; D009154:Mutation; D016011:Normal Distribution; D016415:Sequence Alignment
chemical_list: D057134:Antibodies, Neutralizing; D015483:HIV Antibodies