PhageScout: Protease Cleavage Site Prediction Using an Experimental Substrate Phage Display Motif-Based Approach

Identification of protease cleavage sites is essential for understanding biological regulation and disease mechanisms, yet many predictive approaches rely on annotated substrates and curated databases, limiting performance for poorly characterized proteases. We present PhageScout, a framework for database-independent generation of protease-specific features to predict cleavage sites using de novo experimental substrate phage display screening. We screened a randomized 5-mer phage display library against two neutrophil serine proteases (cathepsin G, elastase). Cleaved peptides generated position weight matrices (PWMs) and peptide enrichment scores to evaluate cleavage-site likelihood across substrate sequences. Sequence-derived scores were integrated with structural features, including accessibility and flexibility, using XGBoost classification models. Performance was benchmarked against annotated cleavage sites from the MEROPS peptidase database as reference data. Phage-derived PWM scores alone captured protease preferences and discriminated cleavage sites from background sites. Without model fitting, PWM scores achieved an area under the curve (AUC) of 0.756 (95%CI: 0.714–0.797) (cathepsin G) and 0.787 (95%CI: 0.753–0.821) (elastase). Combining broad and specific phage-derived scores improved cathepsin G prediction (AUC = 0.783), whereas this improvement was not observed for elastase. Compared to only phage-derived features, XGBoost models integrating phage sequence and structural features provided modest gains for elastase (AUC = 0.775 to 0.806), with phage-derived features ranking among the strongest predictors, but not cathepsin G (AUC = 0.702 to 0.710). Our findings demonstrate that PhageScout can use experimentally derived cleavage signatures to generate protease-specific predictive features and prioritize protease cleavage sites, providing a framework that warrants further validation across diverse proteases and biological contexts.

Authors

Institutions

Publication Details

Journal
International Journal of Molecular Sciences
Published
2026-08-25
DOI
https://doi.org/10.3390/ijms27177593
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

PhageScout: Protease Cleavage Site Prediction Using an Experimental Substrate Phage Display Motif-Based Approach

Eddy Yu, Colin A. Kretz, Matthew L. Holding, Rex Huang et al.
International Journal of Molecular Sciences
Machine Learning in Bioinformatics
article

PhageScout: Protease Cleavage Site Prediction Using an Experimental Substrate Phage Display Motif-Based Approach

Eddy Yu, Colin A. Kretz, Matthew L. Holding, Rex Huang, Andrew Chan, Cherie Teney
article en

Abstract

Identification of protease cleavage sites is essential for understanding biological regulation and disease mechanisms, yet many predictive approaches rely on annotated substrates and curated databases, limiting performance for poorly characterized proteases. We present PhageScout, a framework for database-independent generation of protease-specific features to predict cleavage sites using de novo experimental substrate phage display screening. We screened a randomized 5-mer phage display library against two neutrophil serine proteases (cathepsin G, elastase). Cleaved peptides generated position weight matrices (PWMs) and peptide enrichment scores to evaluate cleavage-site likelihood across substrate sequences. Sequence-derived scores were integrated with structural features, including accessibility and flexibility, using XGBoost classification models. Performance was benchmarked against annotated cleavage sites from the MEROPS peptidase database as reference data. Phage-derived PWM scores alone captured protease preferences and discriminated cleavage sites from background sites. Without model fitting, PWM scores achieved an area under the curve (AUC) of 0.756 (95%CI: 0.714–0.797) (cathepsin G) and 0.787 (95%CI: 0.753–0.821) (elastase). Combining broad and specific phage-derived scores improved cathepsin G prediction (AUC = 0.783), whereas this improvement was not observed for elastase. Compared to only phage-derived features, XGBoost models integrating phage sequence and structural features provided modest gains for elastase (AUC = 0.775 to 0.806), with phage-derived features ranking among the strongest predictors, but not cathepsin G (AUC = 0.702 to 0.710). Our findings demonstrate that PhageScout can use experimentally derived cleavage signatures to generate protease-specific predictive features and prioritize protease cleavage sites, providing a framework that warrants further validation across diverse proteases and biological contexts.

International Journal of Molecular SciencesVol. 27(17)
University of Michigan (US), Thrombosis and Atherosclerosis Research Institute (CA), Hamilton Health Sciences (CA), McMaster University (CA)
National Institutes of Health, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada
Reduced inequalities
Openalex Percentile: Top 17%
Machine Learning in Bioinformatics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.