Unifying structural descriptors for biological and bioinspired nanoscale complexes
Unifying structural descriptors for biological and bioinspired nanoscale complexes
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DOI：
10.1038/s43588-022-00229-w
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发表时间：
2022-04-01
期刊：
影响因子：
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通讯作者：
Kotov, Nicholas A.
中科院分区：
文献类型：
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作者：
Cha, Minjeong;Emre, Emine Sumeyra Turali;Kotov, Nicholas A.
已知仿生纳米颗粒用作纳米级佐剂、酶模拟物和淀粉样蛋白纤维化抑制剂。它们的进一步发展需要更好地了解它们与蛋白质的相互作用。关于蛋白质-蛋白质相互作用的丰富知识可以作为设计蛋白质-纳米颗粒组装体的指南，但是用于蛋白质-蛋白质相互作用的计算包中使用的化学和生物输入不适用于无机纳米颗粒。分析蛋白质复合物的化学，几何和图论描述符，我们发现几何和图论描述符一致适用于生物和无机纳米结构，可以预测蛋白质对中的相互作用位点，准确率>80%，分类概率接近90%。我们将在蛋白质-蛋白质相互作用上训练的机器学习算法扩展到无机纳米粒子，并发现实验和预测的蛋白质相互作用位点之间几乎完全匹配。这些发现可以扩展到其他有机和无机纳米粒子，以预测它们与生物分子和其他化学结构的组装，形成锁和钥匙复合物。
Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein-protein interactions can serve as a guide for designing protein-nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein-protein interactions are not applicable to inorganic nanoparticles. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability similar to 90%. We extended the machine-learning algorithms trained on protein-protein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes.