从随机到预测:上下文特异性相互作用框架提高了未知药物途径中药物蛋白相互作用的选择

IF 1.5 4区 生物学 Q4 CELL BIOLOGY Integrative Biology Pub Date : 2022-01-01 DOI:10.1093/intbio/zyac002
Jennifer L Wilson,Alessio Gravina,Kevin Grimes
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引用次数: 0

摘要

由于药物损耗大,蛋白质-蛋白质相互作用(PPI)网络模型作为一种通过分析药物靶点下游的蛋白质来预测药物疗效的有效方法具有很大的吸引力。遗憾的是,这些方法往往会过度预测关联,精度和预测性能较低;性能往往不优于随机(AUROC ~0.5)。通常,PPI模型确定与下游蛋白质相关的排名表型,但方法在下游蛋白质的优先级上有所不同。大多数方法适用于评估所有表型的全局方法。我们假设单表型分析可以提高预测性能。我们比较了两种全球方法——统计方法和基于距离的方法——以及我们新颖的每表型方法——“情境特异性相互作用”(CSI)分析,以预测严重的副作用。我们使用了一个新的不良事件(或指定医疗事件,DMEs)数据集,发现CSI比全球方法提高了50% (AUROC为0.77,而非0.51),平均精度提高了76-95%(0.499,而非0.284,0.256)。我们的研究结果为在使用PPI网络方法预测药物表型时考虑基于每个表型的下游蛋白提供了定量依据。
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From random to predictive: a context-specific interaction framework improves selection of drug protein–protein interactions for unknown drug pathways
Abstract With high drug attrition, protein–protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesized that a per-phenotype analysis could improve prediction performance. We compared two global approaches—statistical and distance-based—and our novel per-phenotype approach, ‘context-specific interaction’ (CSI) analysis, on severe side effect prediction. We used a novel dataset of adverse events (or designated medical events, DMEs) and discovered that CSI had a 50% improvement over global approaches (AUROC 0.77 compared to 0.51), and a 76–95% improvement in average precision (0.499 compared to 0.284, 0.256). Our results provide a quantitative rationale for considering downstream proteins on a per-phenotype basis when using PPI network methods to predict drug phenotypes.
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来源期刊
Integrative Biology
Integrative Biology 生物-细胞生物学
CiteScore
4.90
自引率
0.00%
发文量
15
审稿时长
1 months
期刊介绍: Integrative Biology publishes original biological research based on innovative experimental and theoretical methodologies that answer biological questions. The journal is multi- and inter-disciplinary, calling upon expertise and technologies from the physical sciences, engineering, computation, imaging, and mathematics to address critical questions in biological systems. Research using experimental or computational quantitative technologies to characterise biological systems at the molecular, cellular, tissue and population levels is welcomed. Of particular interest are submissions contributing to quantitative understanding of how component properties at one level in the dimensional scale (nano to micro) determine system behaviour at a higher level of complexity. Studies of synthetic systems, whether used to elucidate fundamental principles of biological function or as the basis for novel applications are also of interest.
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