Computational scanning for responding clonotypes in immune repertoires

Personalized medicine promises to exploit the diversity of human immune repertoires to design targeted T cell and B cell--based vaccines and therapeutics.

However, even for mass vaccine design, identifying responding clonotypes to new viruses is costly, labor intensive and time consuming.RESPOND turns existing algorithmic solutions for identifying responding immune clonotypes into a user-friendly platform to aid drug and therapeutics discovery. RESPOND combines different algorithmic solutions and finds the best computational approach to fit the user’s needs.

It is based on four methods developed during the ERC CoG STRUGGLE project: ALICE, NoiSET, fSTAR and HLA-Guessr. They are based on different properties of responding clonotypes, from sequence similarity to abundance and publicness.

The idea of RESPOND is to integrate these features in a user-friendly tool that exploits the strengths of all methods to find the best answer to the practitioner’s query.

RESPOND will return lists of candidates for responding clonotypes and a statistical analysis of their occurrence in databsses.Collaborating with big pharma, start-ups, medical researchers and practitioners, RESPOND will develop computational solutions to reduce the costs of biotechnological discovery and significantly decrease the time to test new vaccines and treatments.

Based on consulting and feedback we will aim to give the user what they need in an appealing interface.

The tool will be of use for everyone involved in exploiting immune repertoires for treatment and prophylactics, from personalized medicine, mass vaccine design to agriculture. RESPOND will pursue commercialization and market solutions both within academia, the biotechnology and medical sectors.