Introduction

Phosphoinositide 3-kinase gamma (PI3Kγ) has been regarded as a promising drug target for the treatment of various diseases, and the diverse physiological roles of class I PI3K isoforms (α, β, δ, and γ) highlight the importance of isoform selectivity in the development of PI3Kγ inhibitors. However, the high structural conservation among the PI3K family makes it a big challenge to develop selective PI3Kγ inhibitors.

Objectives

A novel machine learning-based virtual screening with multiple PI3Kγ protein structures was developed to discover novel PI3Kγ inhibitors.

Methods

A large chemical database was screened using the virtual screening model, the top-ranked compounds were then subjected to a series of bio-evaluations, which led to the discovery of JN-KI3. The selective inhibition mechanism of JN-KI3 against PI3Kγ was uncovered by a theoretical study.

Results

49 hits were identified through virtual screening, and the cell-free enzymatic studies found that JN-KI3 selectively inhibited PI3Kγ at a concentration as low as 3,873 nM but had no inhibitory effect on Class IA PI3Ks, leading to the selective cytotoxicity on hematologic cancer cells. Meanwhile, JN-KI3 potently blocked the PI3K signaling, finally led to distinct apoptosis of hematologic cell lines at a low concentration. Lastly, the key residues of PI3Kγ and the structural characteristics of JN-KI3, which both would influence γ isoform-selective inhibition, were highlighted by systematic theoretical studies.

Conclusion

The developed virtual screening model strongly manifests the robustness to find novel PI3Kγ inhibitors. JN-KI3 displays a specific cytotoxicity on hematologic tumor cells, and significantly promotes apoptosis associated with the inhibition of the PI3K signaling, which depicts PI3Kγ as a potential target for the hematologic tumor therapy. The theoretical results reveal that those key residues interacting with JN-KI3 are less common compared to most of the reported PI3Kγ inhibitors, indicating that JN-KI3 has novel structural characteristics as a selective PIK3γ inhibitor.

中文翻译：

---

通过将基于机器学习的虚拟筛选与多种蛋白质结构和生物评估相结合，发现新型选择性 PI3Kγ 抑制剂

介绍

磷酸肌醇 3-激酶 γ (PI3Kγ) 被认为是治疗各种疾病的有希望的药物靶点，I 类 PI3K 异构体（α、β、δ 和 γ）的不同生理作用突出了异构体选择性的重要性。 PI3Kγ抑制剂的开发。然而，PI3K 家族的高度结构保守性使其成为开发选择性 PI3Kγ 抑制剂的一大挑战。

目标

开发了一种新的基于机器学习的具有多种 PI3Kγ 蛋白结构的虚拟筛选，以发现新的 PI3Kγ 抑制剂。

方法

使用虚拟筛选模型筛选大型化学数据库，然后对排名靠前的化合物进行一系列生物评估，从而发现了 JN-KI3。理论研究揭示了JN-KI3对PI3Kγ的选择性抑制机制。

结果

通过虚拟筛选鉴定出49个命中，无细胞酶研究发现，JN-KI3在低至3,873 nM的浓度下选择性抑制PI3Kγ，但对IA类PI3K没有抑制作用，导致对血液癌细胞的选择性细胞毒性. 同时，JN-KI3 有效阻断 PI3K 信号传导，最终在低浓度下导致血液细胞系明显凋亡。最后，通过系统的理论研究强调了 PI3Kγ 的关键残基和 JN-KI3 的结构特征，这两者都会影响 γ 亚型选择性抑制。

结论

开发的虚拟筛选模型有力地证明了寻找新型 PI3Kγ 抑制剂的稳健性。JN-KI3 对血液肿瘤细胞表现出特异性细胞毒性，并显着促进与抑制 PI3K 信号传导相关的细胞凋亡，这将 PI3Kγ 描述为血液肿瘤治疗的潜在靶点。理论结果表明，与大多数报道的 PI3Kγ 抑制剂相比，与 JN-KI3 相互作用的关键残基较少见，表明 JN-KI3 作为选择性 PIK3γ 抑制剂具有新颖的结构特征。