AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK.
AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK.
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DOI：
10.1371/journal.pcbi.1010263
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发表时间：
2023-05
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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PNCK，或CAMK1b，是钙-钙调蛋白依赖激酶家族的一种未被充分研究的激酶，最近在几项大规模多组学研究中被确定为癌症进展和生存的标志。PNCK的生物学及其与肿瘤发生的关系也开始被阐明，数据表明其在DNA损伤反应、细胞周期控制、细胞凋亡和hif -1- α相关途径中发挥着多种作用。为了进一步探索PNCK作为临床靶点，必须开发有效的小分子分子探针。目前，CAMK家族在临床前或临床研究中还没有靶向小分子抑制剂。此外，还没有实验推导出PNCK的晶体结构。在此，我们报告了一个三管齐下的化学探针发现活动，利用同源性建模、机器学习、虚拟筛选和分子动力学，从市上可获得的化合物文库中识别出具有低微摩尔效力的小分子，以对抗PNCK活性。我们报告了首次有针对性地发现PNCK抑制剂的热门系列的发现，这将作为未来药物化学工作的起点，以优化有效的化学探针。机器学习和虚拟筛选是药理学家加速药物发现过程的强大工具。然而，当靶向不太为人所知的蛋白质时，重要的是首先开发一种有效的、选择性的化学探针。除了基因敲除或敲除试验外，化学探针允许对蛋白质活性进行药理学抑制，以研究感兴趣的蛋白质的生物学功能。在之前对患者肿瘤的多组学研究中，我们已经确定PNCK是肾癌的一个感兴趣的靶标。然而，PNCK的功能在很大程度上是未知的，因为它被指定为一种研究不足的激酶。因此，我们利用已知靶向结构相似激酶的化合物的激酶活性数据来开发机器学习模型，以预测与PNCK结合的小分子。此外，我们简单地使用形状筛选在我们的多个同源模型中找到与ATP结合在PNCK活性位点上的形状和电子结构相似的化合物。通过虚拟方法的组合，我们能够识别出几种具有有利的、易于处理的支架的靶向化合物，从而在靶向到先导的活动中向前推进，开发出同类中第一个选择性PNCK化学探针。这项工作将导致阐明pnks在几种癌症模型中的功能，并随后导致一种新药的开发。
PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes. Machine learning and virtual screening are powerful tools in the pharmacologist’s arsenal for accelerating the process of drug discovery. When targeted lesser-known proteins, however, it is important to first develop a potent, selective chemical probe. The chemical probe allows for pharmacological inhibition of protein activity to be used in addition to genetic knock-down or knock-out assays for studying the biological function of your protein in interest. In a previous multi-omics study of patient tumors, we had identified PNCK as a target of interest in kidney cancer. However, the function of PNCK is largely unknown as it has been designated as an understudied kinase. As such, we utilized kinase activity data for compounds known to target structurally similar kinases to develop machine learning models to predict small molecule binding to PNCK. Additionally, we simply used shape screening to find compound similar in shape and electronics to ATP when bound to the active site of PNCK in our multiple homology models. Using a combination of virtual methods, we were able to identify several hit compounds with favorable, tractable scaffolds, to move forward in a hit-to-lead campaign to develop the first in class, selective PNCK chemical probe. This work will lead to the elucidation of PNCKs function in several cancer models and subsequently lead to the development of a novel drug.
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影响因子：
14.9
作者：
Goujon M;McWilliam H;Li W;Valentin F;Squizzato S;Paern J;Lopez R
通讯作者：
Lopez R
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
14.9
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Nguyen DT;Mathias S;Bologa C;Brunak S;Fernandez N;Gaulton A;Hersey A;Holmes J;Jensen LJ;Karlsson A;Liu G;Ma'ayan A;Mandava G;Mani S;Mehta S;Overington J;Patel J;Rouillard AD;Schürer S;Sheils T;Simeonov A;Sklar LA;Southall N;Ursu O;Vidovic D;Waller A;Yang J;Jadhav A;Oprea TI;Guha R
通讯作者：
Guha R
影响因子：
2.9
作者：
Jacobson, MP;Pincus, DL;Friesner, RA
通讯作者：
Friesner, RA
影响因子：
3.5
作者：
McGann, Mark
通讯作者：
McGann, Mark