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用于筛选潜在HSP90抑制剂的机器学习模型的开发。

Development of machine learning models for the screening of potential HSP90 inhibitors.

作者信息

Khan Mohd Imran, Park Taehwan, Imran Mohammad Azhar, Gowda Saralamma Venu Venkatarame, Lee Duk Chul, Choi Jaehyuk, Baig Mohammad Hassan, Dong Jae-June

机构信息

Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

Department of Family Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

出版信息

Front Mol Biosci. 2022 Oct 19;9:967510. doi: 10.3389/fmolb.2022.967510. eCollection 2022.

DOI:10.3389/fmolb.2022.967510
PMID:36339714
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9626531/
Abstract

Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different approaches to screen the KCB database to identify the potential HSP90 inhibitors. Further evaluation of these inhibitors on various cancer cell lines showed favorable inhibitory activity. These inhibitors could serve as a basis for future development of effective HSP90 inhibitors.

摘要

热休克蛋白90（Hsp90）是一种分子伴侣，在客户蛋白的折叠过程中发挥着重要作用。这种细胞蛋白与多种癌症类型的进展有关，包括乳腺癌、肺癌和胃肠道间质瘤。几种致癌激酶是Hsp90的客户蛋白，它们的活性依赖于这种分子伴侣。这使得HSP90成为癌症治疗的一个重要治疗靶点。研究已证实抑制HSP90是癌症治疗的一种显著治疗方法。在本研究中，我们利用机器学习和不同方法筛选KCB数据库，以识别潜在的HSP90抑制剂。对这些抑制剂在各种癌细胞系上的进一步评估显示出良好的抑制活性。这些抑制剂可为未来开发有效的HSP90抑制剂奠定基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/f801b87e975d/fmolb-09-967510-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/02ee5fa9d7c5/fmolb-09-967510-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/2424e3461ffd/fmolb-09-967510-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/8ba700dac947/fmolb-09-967510-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/13391415dd47/fmolb-09-967510-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/76831757c4fd/fmolb-09-967510-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/a6978a8e0593/fmolb-09-967510-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/f801b87e975d/fmolb-09-967510-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/02ee5fa9d7c5/fmolb-09-967510-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/2424e3461ffd/fmolb-09-967510-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/8ba700dac947/fmolb-09-967510-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/13391415dd47/fmolb-09-967510-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/76831757c4fd/fmolb-09-967510-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/a6978a8e0593/fmolb-09-967510-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e67/9626531/f801b87e975d/fmolb-09-967510-g007.jpg

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Impact of the Double Mutants on Spike Protein of SARS-CoV-2 B.1.617 Lineage on the Human ACE2 Receptor Binding: A Structural Insight.关于 SARS-CoV-2 B.1.617 谱系的双突变体对人类 ACE2 受体结合的影响：结构见解。
Viruses. 2021 Nov 17;13(11):2295. doi: 10.3390/v13112295.
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Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation.
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Machine Learning in Drug Discovery: A Review.药物发现中的机器学习：综述
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Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors.整合基于机器学习的虚拟筛选与多种蛋白质结构及生物测定评估以发现新型GSK3β抑制剂
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