Institute for Frontier Materials, Deakin University, Geelong, 3216 VIC, Australia.
School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853, United States.
ACS Nano. 2021 Nov 23;15(11):18260-18269. doi: 10.1021/acsnano.1c07298. Epub 2021 Nov 6.
Peptide sequence engineering can potentially deliver materials-selective binding capabilities, which would be highly attractive in numerous biotic and abiotic nanomaterials applications. However, the number of known materials-selective peptide sequences is small, and identification of new sequences is laborious and haphazard. Previous attempts have sought to use machine learning and other informatics approaches that rely on existing data sets to accelerate the discovery of materials-selective peptides, but too few materials-selective sequences are known to enable reliable prediction. Moreover, this knowledge base is expensive to expand. Here, we combine a comprehensive and integrated experimental and modeling effort and introduce a Bayesian Effective Search for Optimal Sequences (BESOS) approach to address this challenge. Through this combined approach, we significantly expand the data set of Au-selective peptide sequences and identify an additional Ag-selective peptide sequence. Analysis of the binding motifs for the Ag-binders offers a roadmap for future prediction with machine learning, which should guide identification of further Ag-selective sequences. These discoveries will enable wider and more versatile integration of Ag nanoparticles in biological platforms.
肽序列工程有可能提供具有材料选择性的结合能力，这在众多生物和非生物纳米材料应用中极具吸引力。然而，具有材料选择性的肽序列的数量很少，而且新序列的鉴定既费力又随机。以前的尝试已经寻求使用机器学习和其他依赖现有数据集的信息学方法来加速具有材料选择性的肽的发现，但已知的具有材料选择性的序列太少，无法进行可靠的预测。此外，这个知识库的扩展成本很高。在这里，我们结合了全面综合的实验和建模工作，并引入了一种贝叶斯有效搜索最优序列（BESOS）方法来应对这一挑战。通过这种综合方法，我们大大扩展了金选择性肽序列数据集，并确定了另一个银选择性肽序列。对银结合物的结合基序的分析为使用机器学习进行未来预测提供了路线图，这应该指导进一步的银选择性序列的鉴定。这些发现将使银纳米粒子在生物平台中的应用更加广泛和多样化。
