Integrating computational detection and experimental validation for rapid GFRAL-specific antibody discovery

TL;DR

This paper introduces a combined computational and experimental pipeline for rapid discovery of GFRAL-specific antibodies, demonstrating high success rates and potential therapeutic applications for metabolic diseases.

Contribution

It presents a novel integration of the STAR computational method with experimental validation to efficiently identify therapeutic antibodies targeting GFRAL.

Findings

50% of identified antibodies showed binding capabilities

Discovery of 67 validated GFRAL antibodies

Convergent selection observed across different mice

Abstract

The identification and validation of therapeutic antibodies is critical for developing effective treatments for many diseases. We present a computational approach for identifying antibodies targeting GFRAL-specific receptors, receptors implicated in appetite regulation. Using humanized Trianni mice, we conducted a longitudinal study with repeated blood sampling and splenic analysis. We applied the STAR computational method for antibody discovery on bulk antibody repertoire data sampled at key time points. By mapping the output from STAR to single-cell data taken at the last time point, we successfully identified a pool of antibodies, of which 50% demonstrated binding capabilities. We observed convergent selection, where responding sequences with identical amino acid complementarity determining regions 3 (CDR3) were found in different mice. We provide a catalog of 67 experimentally validated antibodies against GFRAL. The potential of these antibodies as antagonists or agonists against GFRAL suggests therapeutic solutions for conditions like cancer cachexia, anorexia, obesity, and diabetes. This study underscores the utility of integrating computational methods and experimental validation for antibody discovery in therapeutic contexts by reducing time and increasing efficiency.