MULGA,一种基于多视图图自动编码器的统一方法,用于识别药物-蛋白质相互作用和药物重新定位。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad524
Jiani Ma, Chen Li, Yiwen Zhang, Zhikang Wang, Shanshan Li, Yuming Guo, Lin Zhang, Hui Liu, Xin Gao, Jiangning Song
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引用次数: 0

摘要

动机:识别药物-蛋白质相互作用(DPI)是药物重新定位的关键一步,这允许重复使用可能对治疗不同疾病有效的获批药物,从而缓解新药开发的挑战。尽管已经提出了多种DPI预测的计算方法,但关键挑战,如可扩展和无偏的相似性计算、异构信息利用和可靠的负样本选择,仍有待解决。结果:为了解决这些问题,我们提出了一种新的、统一的多视图图自动编码器框架,称为MULGA,用于DPI和药物重新定位预测。MULGA的特点是:(i)一种多视角学习技术,可以有效地学习真实的药物亲和力和靶点亲和力矩阵;(ii)图自动编码器,用于推断缺失的DPI交互;以及(iii)一种新的基于“关联有罪”的负采样方法,用于选择高度可靠的非DPI。基准实验表明,MULGA在DPI预测方面优于最先进的方法,消融研究验证了每个拟议组件的有效性。重要的是,我们重点介绍了MULGA入围的针对严重急性呼吸综合征冠状病毒2(SAR-CoV-2)刺突糖蛋白的顶级药物,为新冠肺炎的治疗提供了更多见解和潜在的有用选择。结合数据集和源代码的可用性,我们设想MULGA可以作为DPI预测和药物重新定位的有用工具进行探索。可用性和实施:MULGA可在https://github.com/jianiM/MULGA/.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning.

Motivation: Identifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed.

Results: To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new "guilty-by-association"-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning.

Availability and implementation: MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/.

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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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