生物矿化
文石
化学
方解石
磷酸肽
支持向量机
肽
人工智能
矿物学
生物化学
化学工程
计算机科学
工程类
作者
Andre Leopold S. Nidoy,Jose Isagani B. Janairo
出处
期刊:Chemistry Letters
[The Chemical Society of Japan]
日期:2024-03-19
卷期号:53 (4)
被引量:1
标识
DOI:10.1093/chemle/upae054
摘要
Abstract An exploratory machine learning (ML) classification model that seeks to examine CaCO3 polymorph selection is presented. The ML model can distinguish if a given peptide sequence binds with calcite or aragonite, polymorphs of CaCO3. The classifier, which was created using SVM and amino acid chemical composition as the input descriptors, yielded satisfactory performance in the classification task, as characterized by AUC = 0.736 and F1 = 0.800 in the test set. Model optimization revealed that tiny, aliphatic, aromatic, acidic, and basic residues are essential descriptors for discriminating aragonite biomineralization peptides from calcite. The presented model offers valuable insights on the significant chemical attributes of biomineralization peptides involved in polymorph binding preference. This can deepen our understanding about the biomineralization phenomenon and may be deployed in the future for the creation biomimetic materials.
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