# Reliable repurposing of the antibody interactome inside the cell

## TL;DR

Researchers developed a method to reliably repurpose antibodies to function inside cells by optimizing charge and stability, enabling them to target various cytoplasmic proteins.

## Contribution

A novel approach using charge optimization and AI-led inverse folding to create functional intrabodies for cytoplasmic protein targeting.

## Key findings

- Charge optimization significantly improves intrabody solubility and stability inside cells.
- AI-led inverse folding was used to generate over 600 intrabody sequences targeting 60 cytoplasmic proteins.
- Validated intrabody interactions with proteins like p53, α-synuclein, and SOD1 demonstrate functional efficacy.

## Abstract

Eighty-five percent of the human proteome has at least one interacting monoclonal antibody. These molecules penetrate the cytoplasm poorly and are very often non-functional within the cell. Analysis of antibody variable domains and characterisation of forty-five single-chain variable fragment (scFv) intrabodies expressed in human cells indicated charge to have the greatest impact on solubility. We created new interdomain linkers, optimised scFv domain orientation and found an optimisable charge discrepancy between variable heavy framework and CDR sites. When applied to reduce the search space and rank the products of AI-led inverse folding this creates a single highly soluble, abundant and stable intrabody with parent antibody epitope recognition. Over six hundred intrabody sequences are presented targeting sixty cytoplasmic proteins with linear, conformational, post-translational modification or oligomer specificity. Interactions were validated for p53, α-synuclein, SOD1, polyQ, FUS/TLS, UCHL1 and GFP. Here we show reliable repurposing of the sequenced antibody interactome inside the cell.

O’Shea and colleagues establish that optimisation of charge and stability is sufficient to enable any single-chain variable fragment intrabody to function within the cell. The authors use AI-led inverse folding to optimise intrabody characteristics, and they present hundreds of intrabody sequences targeting sixty cytoplasmic proteins.