Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling.
Mol Ther
; 32(6): 1687-1700, 2024 Jun 05.
Article
in En
| MEDLINE
| ID: mdl-38582966
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Protein Binding
/
Protein Engineering
/
SARS-CoV-2
Limits:
Humans
Language:
En
Journal:
Mol Ther
Journal subject:
BIOLOGIA MOLECULAR
/
TERAPEUTICA
Year:
2024
Document type:
Article
Country of publication: