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Fast, accurate ranking of engineered proteins by receptor binding propensity using structural modeling

Ding, Xiaozhe and Chen, Xinhong and Sullivan, Erin E. and Shay, Timothy F. and Gradinaru, Viviana (2023) Fast, accurate ranking of engineered proteins by receptor binding propensity using structural modeling. . (Unpublished)

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Deep learning-based methods for protein structure prediction have achieved unprecedented accuracy. However, the power of these tools to guide the engineering of protein-based therapeutics remains limited due to a gap between the ability to predict the structures of candidate proteins and the ability to assess which of those proteins are most likely to bind to a target receptor. Here we bridge this gap by introducing Automated Pairwise Peptide-Receptor AnalysIs for Screening Engineered proteins (APPRAISE), a method for predicting the receptor binding propensity of engineered proteins. After generating models of engineered proteins competing for binding to a target using an established structure-prediction tool such as AlphaFold2-multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As a proof-of-concept, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent engineered adeno-associated viral vectors, as well as diverse classes of engineered proteins such as miniproteins targeting the SARS-CoV-2 spike protein, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor and PD-L1. With its high accuracy, interpretability, and generalizability, APPRAISE has the potential to expand the utility of current structural prediction and accelerate protein engineering for biomedical applications.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemProject website
Ding, Xiaozhe0000-0002-0267-0791
Chen, Xinhong0000-0003-0408-0813
Shay, Timothy F.0000-0001-6591-3271
Gradinaru, Viviana0000-0001-5868-348X
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. We thank Elisha Mackey, Zhe Qu, and Pat Anguiano for administrative assistance, Catherine Oikonomou for help with manuscript editing, and Sripriya R. Kumar, Seongmin Jang, Jimin Park, and Changfan Lin for helpful discussions. Schematics in this manuscript were created with The study was funded by an NIH Director’s Pioneer Award DP1OD025535 (to V.G.). Competing Interest Statement. V.G. is a scientific co-founder and BoD member of Capsida Biotherapeutics. The terms of the arrangements have been reviewed and approved by the California Institute of Technology in accordance with its conflict of interest policies.
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Record Number:CaltechAUTHORS:20230316-182523000.38
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120152
Deposited By: George Porter
Deposited On:18 Mar 2023 02:35
Last Modified:18 Mar 2023 02:35

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