Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.
Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.
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DOI：
10.1371/journal.pcbi.1005135
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发表时间：
2016-10
影响因子：
4.3
通讯作者：
Xie L
中科院分区：
文献类型：
--
作者：
Lim H;Poleksic A;Yao Y;Tong H;He D;Zhuang L;Meng P;Xie L
靶点筛选是药物发现的主要方法之一。除了预期的靶点外，经常发生意外的药物脱靶相互作用，其中许多尚未被识别和表征。脱靶相互作用可能导致治疗或副作用。因此，识别先导化合物或现有药物的全基因组脱靶对于设计有效和安全的药物至关重要，并为药物再利用提供新的机会。虽然已经开发了许多计算方法来预测药物-靶标相互作用，但它们要么比我们在这里提出的方法不准确，要么计算过于密集，从而限制了它们大规模脱靶识别的能力。此外，大多数基于机器学习的算法的性能主要用于预测数百种化学物质在同一基因家族中的脱靶相互作用。目前尚不清楚这些算法如何在蛋白质组规模上检测跨基因家族的脱靶。在这里，我们提出了一种快速准确的脱靶预测方法，REMAP，它是基于双重正则化一类协同过滤算法，探索连续的化学空间，蛋白质空间，以及它们的相互作用在大规模上。当在可靠、广泛和跨基因家族基准中进行测试时，REMAP优于最先进的方法。此外，REMAP是高度可扩展的。它可以在2小时内筛选20万种化学物质和2万种蛋白质。使用重建的全基因组靶向图谱作为化合物的指纹，我们预测七种FDA批准的药物可以重新用作新型抗癌疗法。其中6个化合物的抗癌活性得到了实验证据的支持。因此，REMAP是对现有的用于药物靶标鉴定、药物再利用、表型筛选和副作用预测的计算机工具箱的有价值的补充。该软件和基准测试可在https://github.com/hansaimlim/REMAP上获得。高通量技术产生了大量不同的组学和表型数据。然而，这些数据集尚未被充分探索，以提高药物发现的有效性和效率，这是一个传统上采用一种药物一个基因范式的过程。因此，将药物推向市场的成本是惊人的，失败率是令人生畏的。靶向药物发现的失败在很大程度上是由于药物很少只与其预期的受体相互作用，而且通常与其他受体结合。为了合理地设计有效和安全的治疗方法，我们需要确定生物体中与药物相互作用的所有可能的细胞蛋白质。现有的实验技术不足以解决这个问题，并将受益于计算建模。然而，要针对数十万种蛋白质可靠地筛选数百万种化学物质是一项艰巨的任务。在这里，我们介绍了一种快速，准确的方法REMAP大规模预测药物-靶点相互作用。REMAP在速度和准确性方面优于最先进的算法，并已成功应用于药物再利用。因此，REMAP可能在药物发现中具有广泛的应用。
Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP. High-throughput techniques have generated vast amounts of diverse omics and phenotypic data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, a process which has traditionally adopted a one-drug-one-gene paradigm. Consequently, the cost of bringing a drug to market is astounding and the failure rate is daunting. The failure of the target-based drug discovery is in large part due to the fact that a drug rarely interacts only with its intended receptor, but also generally binds to other receptors. To rationally design potent and safe therapeutics, we need to identify all the possible cellular proteins interacting with a drug in an organism. Existing experimental techniques are not sufficient to address this problem, and will benefit from computational modeling. However, it is a daunting task to reliably screen millions of chemicals against hundreds of thousands of proteins. Here, we introduce a fast and accurate method REMAP for large-scale predictions of drug-target interactions. REMAP outperforms state-of-the-art algorithms in terms of both speed and accuracy, and has been successfully applied to drug repurposing. Thus, REMAP may have broad applications in drug discovery.
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影响因子：
14.9
作者：
Kim S;Thiessen PA;Bolton EE;Chen J;Fu G;Gindulyte A;Han L;He J;He S;Shoemaker BA;Wang J;Yu B;Zhang J;Bryant SH
通讯作者：
Bryant SH
影响因子：
5.6
作者：
Fujiwara, Yukiko;Yamashita, Yoshiko;Shimizu, Ryo
通讯作者：
Shimizu, Ryo
影响因子：
5.6
作者：
Koutsoukas, Alexios;Lowe, Robert;Bender, Andreas
通讯作者：
Bender, Andreas
影响因子：
4.3
作者：
Kinnings SL;Liu N;Buchmeier N;Tonge PJ;Xie L;Bourne PE
通讯作者：
Bourne PE
影响因子：
48
作者：
Lin, Henry;Sassano, Maria F.;Roth, Bryan L.;Shoichet, Brian K.
通讯作者：
Shoichet, Brian K.