The Graduate Center, City University of New York, New York, NY 10016, USA.
Hunter College, City University of New York, New York, NY 10065, USA.
Cell Rep Methods. 2023 Apr 17;3(4):100452. doi: 10.1016/j.crmeth.2023.100452. eCollection 2023 Apr 24.
Drug-induced phenotypes result from biomolecular interactions across various levels of a biological system. Characterization of pharmacological actions therefore requires integration of multi-omics data. Proteomics profiles, which may more directly reflect disease mechanisms and biomarkers than transcriptomics, have not been widely exploited due to data scarcity and frequent missing values. A computational method for inferring drug-induced proteome patterns would therefore enable progress in systems pharmacology. To predict the proteome profiles and corresponding phenotypes of an uncharacterized cell or tissue type that has been disturbed by an uncharacterized chemical, we developed an end-to-end deep learning framework: TransPro. TransPro hierarchically integrated multi-omics data, in line with the central dogma of molecular biology. Our in-depth assessments of TransPro's predictions of anti-cancer drug sensitivity and drug adverse reactions reveal that TransPro's accuracy is on par with that of experimental data. Hence, TransPro may facilitate the imputation of proteomics data and compound screening in systems pharmacology.
药物诱导的表型源于生物系统各个层次的生物分子相互作用。因此，药理学作用的特征需要整合多组学数据。蛋白质组学谱可能比转录组学更直接地反映疾病机制和生物标志物，但由于数据稀缺和频繁出现缺失值，尚未得到广泛应用。因此，一种用于推断药物诱导的蛋白质组模式的计算方法将推动系统药理学的发展。为了预测未被表征的化学物质干扰的未被表征的细胞或组织类型的蛋白质组谱和相应的表型，我们开发了一个端到端的深度学习框架：TransPro。TransPro 分层整合了多组学数据，符合分子生物学的中心法则。我们对 TransPro 预测抗癌药物敏感性和药物不良反应的能力进行了深入评估，结果表明 TransPro 的准确性与实验数据相当。因此，TransPro 可能有助于在系统药理学中进行蛋白质组学数据的插补和化合物筛选。
