Prediction of Body Fluids where Proteins are Secreted into Based on Protein Interaction Network

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.确定分泌蛋白可被分泌进入的体液类型，对于蛋白质功能注释与疾病生物标志物（biomarker）的发现具有重要意义。本研究开发了一种基于网络的方法，用于预测人类蛋白质可被分泌进入的体液类别。针对一个由529个人类分泌蛋白（secreted proteins）组成的新建基准数据集（benchmark dataset），本研究通过刀切检验（jackknife test）得到的模型对最优候选体液的预测准确率达79.02%，显著高于随机猜测的成功率（29.36%）。模型预测的前四顺位体液覆盖蛋白质真实分泌体液的概率为62.94%。本方法还通过两组独立数据集开展了进一步验证：第一组包含57种可分泌至血液的蛋白质；第二组包含61种可分泌至血浆/血清且可作为多种癌症相关潜在生物标志物的蛋白质。针对第一组数据集中的57个蛋白质，有55个被正确预测为血液分泌蛋白；针对第二组数据集中的61个蛋白质，有58个被预测为最可能分泌至血浆/血清的蛋白质。上述令人鼓舞的结果表明，该基于网络的预测方法颇具应用前景。预计该方法将为基础研究与药物开发相关领域提供有力支撑。