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Computational design of theragnostic nanobodies: targeting missense mutants in metastatic breast cancer cells

We aim at developing a new time and cost-effective in silico based strategy to produce patient-specific biotherapeutics. Personalised targeted therapies heavily rely on the identification of selective antibodies capable of targeting their antigens in/on cancer cells. Accumulation of mutations along cancer progression results in patient-specific aberrant expression of proteins. For instance, the oncosuppressor protein p53 is mutated in ~23% of breast cancer samples. Aberrant missense mutants lead to non-functional proteins. Their sequence closely resembles the wild-type sequence. These are considered “difficult” targets by conventional immunization and antibody in vivo maturation protocols. We will use the in silico design of specific antibodies to target chosen epitopes of specific missense mutants of the oncosuppressor protein p53. Three main goals will be achieved: (i) provide a validated computational/experimental protocol for the time-effective design of highly selective customised binders; (ii) develop a set of highly optimised antibody fragments (VHH) for selected mutants engineered into reagents suitable for immunostaining; (iii) functionalize the designed VHHs to develop a cell-permeable theragnostic VHH for targeting mutants in living cells. Replacing the process of monoclonal antibody development with a computational based protocol represents a huge step forward in the timely production of cost-effective biotherapeutics, speeding up the development of new personalised targeted therapies.

LogoEnteFinanziatore AIRC
AIRC IG 2020 - Fortuna
Data inizio
Data fine
Persone coinvolte
Sara Fortuna
Sara Fortuna
Computational mOdelling of NanosCalE and bioPhysical sysTems
Total budget: 392.795,80€
Total contribution: 392.795,80€