Biology subjects

Harvey, E. P.

Publications and source records attributed to Harvey, E. P..

5 recordsLinked to original sources

bioRxiv2026

Machine Learning enables efficient and effective affinity maturation of nanobodies

Antibodies can bind their targets with exquisite potency and selectivity due in part to large antibody-target protein-protein interaction surface areas. Despite the very large size and diversity of synthetic libraries, in vitro sorting alone tends to yield binders with modest affinities. By analogy to the in vivo affinity maturation in the natural immune system, these initial hits are typically affinity matured in vitro to achieve high affinity binding. However, affinity maturation campaigns can be laborious, often requiring multiple selection rounds and strategies for each clone to be optimized. Here, we investigated whether one could accelerate the discovery of optimized binders using machine learning on sequencing data from single selection sorts of affinity maturation yeast-display campaigns. Our results show that sparse sequencing data from a single sorting round can predict sequences that are enriched after multiple rounds. We also find that linear models outperform deep neural networks and semi-supervised approaches in ranking validated affinity-enhancing substitutions. Linear models are also more interpretable, offering insights into residue preferences that can be leveraged for further engineering. We use our models to design and select optimized nanobody binders to relaxin family peptide receptor 1 (RXFP1), yielding multiple improved binders including 3 sub nanomolar binders with the best exhibiting a [~]2500-fold improvement over WT.

bioinformatics↗

bioRxiv2025

GPCR Antagonism via Antibody-Mediated Rewiring of Receptor Trafficking and Degradation

G-protein coupled receptors (GPCRs) represent one of the most important yet incompletely targeted classes of therapeutic proteins. Here, we report a novel strategy for functional GPCR antagonism through antibody-mediated endocytosis and lysosomal degradation. Our engineered bispecific antibodies, termed GPCR-TfR1 Targeting Chimeras (GTACs), achieve potent and selective downregulation of various GPCRs, including RXFP1 and CCR6, critical cancer and immune drug targets that are difficult to antagonize with conventional approaches. GTACs led to complete inhibition of receptor activity with over 100-fold greater potency than conventional antibody antagonists. Using four-color imaging, we elucidated the trafficking mechanism of both the target protein and TfR1 in living cells. The GTAC platform enables robust antagonism of signaling across diverse GPCR families and establishes induced endocytosis and lysosomal trafficking as a fundamentally new paradigm for therapeutic GPCR modulation.

bioengineering↗

bioRxiv2025

In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer

Antibodies are central mediators of the adaptive immune response, and they are powerful research tools and therapeutics. Antibody discovery requires substantial experimental effort, such as immunization campaigns or in vitro library screening. Predicting antibody-antigen binding a priori remains challenging. However, recent machine learning methods raise the possibility of in silico antibody discovery, bypassing or reducing initial experimental bottlenecks. Here, we report a virtual screen using AlphaFold-Multimer (AF-M) that prospectively identified nanobody binders to MRGPRX2, a G protein-coupled receptor (GPCR) and therapeutic target for the treatment of pseudoallergic inflammation and itch. Using previously reported nanobody-GPCR structures, we identified a set of AF-M outputs that effectively discriminate between interacting and non-interacting nanobody-GPCR pairs. We used these outputs to perform a prospective in silico screen, identified nanobodies that bind MRGPRX2 with high affinity, and confirmed activity in signaling and functional cellular assays. Our results provide a proof of concept for fully computational antibody discovery pipelines that can circumvent laboratory experiments.

biochemistry↗

bioRxiv2022

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. We designed a set of experiments using a diverse naive synthetic camelid antibody fragment ( nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally tested our models performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the model allowed us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its pharmacological properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

pharmacology and toxicology↗

bioRxiv2020

A Strategy to Assess the Cellular Activity of E3 Ligases against Neo-Substrates using Electrophilic Probes

Targeted protein degradation is a rapidly developing therapeutic modality that promises lower dosing and enhanced selectivity as compared to traditional occupancy-driven inhibitors, and the potential to modulate historically intractable targets. While the well-characterized E3 ligases CRBN and VHL have been successfully redirected to degrade numerous proteins, there are approximately 600 predicted additional E3 family members that may offer improved activity, substrate selectivity, and/or tissue distribution; however, characterizing the potential applications of these many ligases for targeted protein degradation has proven challenging. Here, we report the development of an approach to evaluate the ability of recombinant E3 ligase components to support neo-substrate degradation. Bypassing the need for hit finding to identify specific E3 ligase binders, this approach makes use of simple chemistry for Covalent Functionalization Followed by E3 Electroporation into live cells (COFFEE). We demonstrate this method by electroporating recombinant VHL, covalently functionalized with JQ1 or dasatinib, to induce degradation of BRD4 or kinase targets, respectively. Furthermore, by applying COFFEE to SPSB2, a SOCS box and SPRY-domain E3 ligase that has not previously been redirected for targeted protein degradation, we validate this method as a powerful approach to define the activity of previously uncharacterized ubiquitin ligases against neo-substrates.

molecular biology↗