Manually curated dataset of
catalytic peptides for ester hydrolysis.
Data Brief 2023;
48:109290. [PMID:
37383747 PMCID:
PMC10294096 DOI:
10.1016/j.dib.2023.109290]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/12/2023] [Accepted: 05/30/2023] [Indexed: 06/30/2023] Open
Abstract
Catalytic peptides are low cost biomolecules able to catalyse chemical reactions such as ester hydrolysis. This dataset provides a list of catalytic peptides currently reported in literature. Several parameters were evaluated, including sequence length, composition, net charge, isoelectric point, hydrophobicity, self-assembly propensity and mechanism of catalysis. Along with the analysis of physico-chemical properties, the SMILES representation for each sequence was generated to provide an easy-to-use means of training machine learning models. This offers a unique opportunity for the development and validation of proof-of-concept predictive models. Being a reliable manually curated dataset, it also enables the benchmark for comparison of new models or models trained on automatically gathered peptide-oriented datasets. Moreover, the dataset provides an insight in the currently developed catalytic mechanisms and can be used as the foundation for the development of next-generation peptide-based catalysts.
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