机器学习辅助搜索P2Y6和其他P2Y受体的配体
Ana C Puhl1, Sarah A Lewicki2, Zhan-Guo Gao2
1Collaborations Pharmaceuticals, Inc, 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
Purinergic signalling
|March 25, 2024
概括
机器学习发现了P2Y6受体的新对手,P2Y6受体是各种疾病的目标. 该研究选了化合物,揭示了ABBV-744作为一种非竞争性的P2Y6R抑制剂,对其他P2YR亚型具有多种影响.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 由尿素二酸盐 (UDP) 激活的P2Y6受体 (P2Y6R) 是炎症,神经退行性和代谢疾病的治疗标.
- 目前已知P2Y6R的强效和选择性抗体数量有限,这需要新的方法来发现连接体.
研究的目的:
- 采用机器学习 (ML) 作为一种新的策略来识别P2Y6R对手.
- 在药理学上评估ML选择的化合物在三个P2Y受体 (P2YR) 亚型:P2Y6R,P2Y1R和P2Y14R.
主要方法:
- 使用已公布的P2Y6R激动剂数据生成和验证一系列的分类ML模型 (DL, ada, bnb, kNN, lreg, rf, SVC, XGB).
- 根据ML模型的共识选择了21种不同的分子结构.
- 使用人类P2Y6R诱导的过渡物和P2Y1R和P2Y14R的光结合抑制试验来选的化合物的选.
主要成果:
- 作为一种实验性抗癌药物,ML选择的化合物ABBV-744表现为hP2Y6R的非竞争性对抗作用和hP2Y1R的增强作用,但没有抑制hP2Y14R结合作用.
- 其他ML选择的化合物显示hP2Y6R的抑制很弱或不存在.
- 实验性药物TAK-593和GSK1070916显示显著抑制P2Y14R结合,而其他化合物的影响很小.
结论:
- 机器学习有效地识别了P2YR亚型的新型调节器,包括P2Y6R,P2Y1R和P2Y14R.
- 这项研究强调了ML在加速发现G蛋白结合受体 (如P2YRs) 的配体方面的潜力.
- ABBV-744是一种有前途的化合物,具有复杂的P2YR相互作用概况,需要进一步研究.
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