PSOPIA: Toward more reliable protein-protein interaction prediction from sequence information

Abstract

A better understanding of biological processes, pathways and functions requires reliable information about protein-protein interactions (PPIs). However, it is still a difficult task to identify complete PPI-networks experimentally in a cell or organism. To supplement the limitations of current experimental techniques, we have proposed PSOPIA, a computational method to predict whether two proteins interact or not (http://mizuguchilab.org/PSOPIA/) [1]. The selection of datasets is a big issue for the PPI prediction [2, 3]. It is generally believed that increasing the size and diversity of examples makes the dataset more representative and reduces the noise effects; however, for many algorithms, it is impractical to use a large-scale dataset at the proteome level because of the memory and CPU time requirements. In this study, PSOPIA was retrained on a highly imbalanced large-scale dataset having a diverse set of examples at the proteome level. The dataset consisted of 43,060 high confidence direct physical PPIs obtained from TargetMine [4] (as positives being only 0.13% of the total) and 33,098,951 negative PPIs. As a result, the new prediction model achieved the higher AUC of 0.89 (pAUCfpr

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PSOPIA:从序列信息走向更可靠的蛋白质相互作用预测

为了更好地了解生物过程、途径和功能，需要有关蛋白质-蛋白质相互作用(PPIs)的可靠信息。然而，在细胞或生物体中通过实验确定完整的ppi网络仍然是一项艰巨的任务。为了补充当前实验技术的局限性，我们提出了PSOPIA，一种预测两种蛋白质是否相互作用的计算方法(http://mizuguchilab.org/PSOPIA/)[1]。数据集的选择是PPI预测的一个大问题[2,3]。一般认为，增加样本的大小和多样性可以使数据集更具代表性，降低噪声影响;然而，对于许多算法来说，由于内存和CPU时间要求，在蛋白质组水平上使用大规模数据集是不切实际的。在这项研究中，PSOPIA在一个高度不平衡的大规模数据集上进行了再训练，该数据集在蛋白质组水平上具有不同的示例集。该数据集包括从TargetMine[4]获得的43,060个高置信度直接物理ppi(阳性仅占总数的0.13%)和33,098,951个阴性ppi。结果表明，新模型的AUC值较高，为0.89 (pAUCfpr< 0)。5% = 0.24)。此外，它还被应用于从低置信度的人类PPI数据集中过滤掉被错误地确定为相互作用(假阳性)的蛋白质对的问题。在这里，我们认为多样化的大规模样本是更可靠的PPI预测的关键，证明了PSOPIA在蛋白质组水平上的表现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。