A Library of Immunoaffinity Reagents for RNA modifications

Period of Performance: 09/01/2016 - 08/31/2017

$150K

Phase 1 SBIR

Recipient Firm

Visterra, Inc.
CAMBRIDGE, MA 02139
Principal Investigator
Principal Investigator

Abstract

Project Abstract While there are commercial monoclonal antibodies against several RNA modifications, there is a general paucity of immunological tools for the >120 known RNA modifications. This lack of immunological tools is hindering study of the emerging roles for modified ribonucleosides as critical players in the many basic cell and organismal processes in viruses, bacteria, parasites and single cell eukaryotes, as well as in human health and disease. Furthermore, the specificity of available antibodies has not been rigorously or systematically validated against most other RNA modifications. To meet this need, we propose to exploit our expertise in antibody development and in nucleic acid modification chemistry and biology to pilot the development of a library of scFv antibody fragments capable of specifically binding modified ribonucleosides in RNA at the single nucleotide level. Recombinant antibodies will be identified using a novel yeast display platform building off of murine immunization with a panel of 10 ribonucleosides. These ribonucleosides were selected both because of their biological relevance and because they are of interest to the research community. Furthermore, we propose to fine-tune specificity and affinity by engineering the individual recombinant antibodies to recognize related structures using our paratope-epitope optimization technology. The resulting recombinant antibodies will be validated for common end-user applications, such as immunoprecipitation, ELISAs, and Northwestern blots. We anticipate that there will be numerous applications for high quality antibody reagents, including affinity purification of modified RNA species, immunohistochemistry in cells and tissue sections, Northwestern blots of total RNA, and diagnostic tools for RNA modification-based disease models.