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Abstract

Antibiofouling peptide materials prevent the nonspecific adsorption of proteins on devices, enabling them to perform their designed functions as desired in complex biological environments. Due to their importance, research on antibiofouling peptide materials has been one of the central subjects of interfacial engineering. However, only a few antibiofouling peptide sequences have been developed. This narrow scope of antibiofouling peptide materials limits their capacity to adapt to the broad spectrum of application scenarios. To address this issue, we searched for antibiofouling peptides in the vast sequence pool of the microbiome library using a combination of deep learning-based high-throughput search and molecular dynamics (MD) simulations. A random forest-based model with an ensemble of ten independent classifiers was developed. Each classifier was trained by prompt-tuning the foundational protein language model Evolution Scaling Modeling version 2 (ESM2) on a distinct training data set. We constructed the databases containing the same amount of antibiofouling and biofouling peptide sequences to attenuate the bias of the existing databases. MD simulations were conducted to investigate the interfacial properties of six selected peptide candidates and their interactions with a lysozyme protein. Two known antibiofouling peptides, (glutamic acid (E)-lysine (K))15 and (EK-proline (P))10, and one known fouling peptide, (glycine)30, were used as the reference. The MD simulation results indicate that five of the six peptides present the potential to resist biofouling. Our research implies that deep learning and molecular simulations can be integrated to discover functional peptide materials for interfacial applications.

Document Type

Article

Publication Date

2025

Notes/Citation Information

© 2024 American Chemical Society

Digital Object Identifier (DOI)

https://doi.org/10.1021/acs.langmuir.4c04140

Funding Information

We thank Dr. Jin Chen for providing input for machine learning model selection. We want to acknowledge the National Institutes of Health (grant R01LM014510) for the financial support. D.W., S.Z., and D.X. are also partially supported by the National Institutes of Health (grant R35GM126985). S.B and Q.S. also acknowledge the support of the National Science Foundation (No. 2150337). The computation for this work was partially performed on the high-performance computing infrastructure provided by Research Computing Support Services at the University of Missouri. We also thank the University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for supporting and using associated research computing resources. This work also used Delta-GPU at NCSA through allocation CIS230053 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by the National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

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