Suppr超能文献

AiKPro:一种基于结构序列比对和分子 3D 构象集合描述符的激酶组全范围生物活性预测的深度学习模型。

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.

机构信息

AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea.

School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea.

出版信息

Sci Rep. 2023 Jun 24;13(1):10268. doi: 10.1038/s41598-023-37456-8.

Abstract

The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.

摘要

发现选择性和有效的激酶抑制剂对于治疗各种疾病至关重要,但由于激酶之间的结构高度相似,这个过程具有挑战性。有效的激酶组全活性分析对于理解激酶功能和识别选择性抑制剂至关重要。在这项研究中,我们提出了 AiKPro,这是一种深度学习模型,它结合了结构验证的多重序列比对和分子 3D 构象集合描述符,以预测激酶-配体结合亲和力。我们的深度学习模型使用基于注意力的机制来捕捉激酶和配体之间相互作用的复杂模式。为了评估 AiKPro 的性能,我们评估了描述符的影响、对未训练激酶和化合物的可预测性,以及基于奇数比的激酶活性分析。我们的模型 AiKPro 对测试集和未训练化合物集的 Pearson 相关系数分别为 0.88 和 0.87,这也表明了模型的稳健性。AiKPro 在整个激酶组中显示出良好的激酶活性谱,可能有助于发现新的相互作用和选择性抑制剂。我们的方法有可能为发现新型、选择性的激酶抑制剂和指导合理的药物设计提供启示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b70/10290719/e6be5fcb9f3e/41598_2023_37456_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验