@article {Prechl438804, author = {J{\'o}zsef Prechl}, title = {Network organization of antibody interactions in sequence and structure space: the RADARS model}, elocation-id = {438804}, year = {2019}, doi = {10.1101/438804}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Adaptive immunity in vertebrates represents a complex self-organizing network of protein interactions that develops throughout the lifetime of an individual. While deep sequencing of the antibody repertoire may reveal clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function. In this paper a novel model of antibody interaction space and network, termed radial adjustment of system resolution, or RADARS, is proposed. The model is based on the radial growth of interaction affinity of antibodies towards an infinity of directions representing molecular shapes. Levels of interaction strength appear as energy shells of the system. B-cell development and immune responses are interpreted in the model and quantitative properties of the antibody network are inferred from the physical properties of a quasi-spherical system growing multi-radially. The concept of system equilibrium constant is introduced, which is the median of equilibrium constants in the system and serves to define probability of interactions. This thermodynamic system is described by a power-law distribution of antibody free energies with a network degree exponent of phi square, representing a scale-free network of antibody interactions. Plasma cells are network hubs, memory B cells are nodes with intermediate degrees and B1 cells represent nodes with minimal degree. As an energy transduction system this network serves to optimize free energy consumption, removing antigens at the required rate at the same time.Thus, the RADARS model implies that an absolute sequence space is reduced to a thermodynamically viable structure space by means of a network of interactions, which control B-cell development. Understanding such quantitative network properties of the system should help the organization of sequence-derived structural data, offering the possibility to relate sequence to function in a complex, self-organizing biological system.}, URL = {https://www.biorxiv.org/content/early/2019/10/10/438804}, eprint = {https://www.biorxiv.org/content/early/2019/10/10/438804.full.pdf}, journal = {bioRxiv} }