Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling
Open accessPublished version of a preprint
Abstract
Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.
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Flagged first, then by citations
- Authors
- Ding, X., Chen, X., Sullivan, E. E., Shay, T. F., & Gradinaru, V.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Ding, Xiaozhe
- Gradinaru, Viviana
- Venue
- Molecular Therapy
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 32
- Issue
- 6
- Pages
- 1687-1700
- ISSN
- 1525-0016 (ISSN-L)
- PMC
- PMC11184338
- OpenAlex
- W4394013662
- Cited by
- 20
- References
- 10
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 1 · 2023: 1
- FWCI
- 2.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular Therapy (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (DP1OD025535)
- California Institute of Technology Tianqiao and Chrissy Chen Institute for Neuroscience
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- engineered proteins, protein structure modeling, peptide-receptor binding, protein engineering, adeno-associated virus vectors, nanobodies, SARS-CoV-2 spike protein, G protein-coupled receptors, transferrin receptor, PD-L1
- MeSH terms
- Deep Learning, COVID-19, SARS-CoV-2, Dependovirus, Genetic Vectors, Humans, Models, Molecular, Protein Binding, Protein Conformation, Protein Engineering, B7-H1 Antigen, Single-Domain Antibodies, Spike Glycoprotein, Coronavirus
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