Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop

Tengyan Xu, Jiaqi Wang, Shuang Zhao, Dinghao Chen, Hongyue Zhang, Yu Fang, Nan Kong, Ziao Zhou, Wenbin Li () and Huaimin Wang ()
Additional contact information
Tengyan Xu: Westlake University
Jiaqi Wang: Research Center for the Industries of the Future, Westlake University
Shuang Zhao: School of Engineering, Westlake University
Dinghao Chen: Westlake University
Hongyue Zhang: Westlake University
Yu Fang: Westlake University
Nan Kong: Westlake University
Ziao Zhou: Westlake University
Wenbin Li: Research Center for the Industries of the Future, Westlake University
Huaimin Wang: Westlake University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector Cg, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation reaches 87.1%. Notably, the de novo-designed peptide hydrogel selected from this work boosts the immune response of the receptor binding domain of SARS-CoV-2 in the mice model. Our approach taps into the potential of machine learning for predicting peptide hydrogelator and significantly expands the scope of natural peptide hydrogels.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-39648-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39648-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-39648-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().