Design and characterization of defined alpha-helix mini-proteins with intrinsic cell permeability

IF 2.6 4区 生物学 Q2 BIOLOGY
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引用次数: 0

Abstract

Proteins with intrinsic cell permeability that can access intracellular targets represent a promising strategy for novel drug development; however, a general design principle is still lacking. Here, we established a library of 46,678 de novo-designed mini-proteins and performed cell permeability screening via phage display. Analyses revealed a characteristic neighboring distribution of positive charges across helices among enriched mini-proteins of CPP7, CPP11, CPP55, CPP109 and CPP112. Compared with the state-of-the-art cell-penetrating mini-protein ZF5.3, the optimized mini-protein CPP11D36R exhibited a sevenfold increase in cell permeability. Endocytosis uptake and early endosome release are the key penetrating mechanisms. A machine learning model with high-throughput data achieved an F1 score of 0.41, significantly outperforming the previously reported CPP prediction models, including MLACP, CPPpred and CellPPD, by 41 %. Overall, our findings validate the effectiveness of a helical structure with a cationic distribution as a design principle on a large scale and present a robust approach for the development of cell-permeable mini-protein drugs.
设计并鉴定具有内在细胞渗透性的定义α-螺旋小蛋白。
具有内在细胞渗透性并能进入细胞内靶点的蛋白质是一种很有前景的新型药物开发策略;然而,目前仍缺乏一种通用的设计原则。在这里,我们建立了一个包含 46,678 个全新设计的迷你蛋白质库,并通过噬菌体展示进行了细胞渗透性筛选。分析表明,在 CPP7、CPP11、CPP55、CPP109 和 CPP112 的富集迷你蛋白中,正电荷在各螺旋之间呈邻近分布。与最先进的细胞穿透小蛋白 ZF5.3 相比,优化后的小蛋白 CPP11D36R 的细胞渗透性提高了七倍。内吞摄取和早期内质体释放是关键的穿透机制。利用高通量数据建立的机器学习模型的 F1 得分为 0.41,比之前报道的 CPP 预测模型(包括 MLACP、CPPpred 和 CellPPD)高出 41%。总之,我们的研究结果验证了具有阳离子分布的螺旋结构作为大规模设计原则的有效性,并为开发细胞渗透性微型蛋白药物提供了一种稳健的方法。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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