Secreted protein prediction system combining CJ-SPHMM, TMHMM, and PSORT
Secreted protein prediction system combining CJ-SPHMM, TMHMM, and PSORT
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DOI：
10.1007/s00335-003-2296-6
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发表时间：
2003-12-01
期刊：
影响因子：
2.5
通讯作者：
Jiang, Y
中科院分区：
文献类型：
--
作者：
Chen, YJ;Yu, P;Jiang, Y
为了增加分泌蛋白预测的覆盖率，我们描述了一种组合策略。我们将联合收割机隐马尔可夫模型（HMM）的方法CJ-SPHMM和TMHMM与PSORT相结合，而不是使用单一的方法来预测分泌蛋白。CJ-SPHMM是一种基于HMM的信号肽预测方法，而TMHMM是一种基于HMM的跨膜（TM）蛋白预测算法。利用CJ-SPHMM和TMHMM，将具有预测的信号肽而没有预测的TM区的蛋白质作为推定的分泌蛋白。这种基于HMM的方法预测分泌蛋白的Ac（准确度）为0.82，Cc（相关系数）为0.75，这与PSORT的Ac为0.82，Cc为0.76相似。当我们进一步补充基于HMM的方法时，即，将CJ-SPHMM + TMHMM与PSORT相结合进行分泌蛋白预测，Ac值提高到0.86，Cc值提高到0.81。采用这种组合策略，从欧洲生物信息学研究所（FBI）维护的国际蛋白质索引（IPI）中搜索推定的分泌蛋白，我们构建了一个推定的人类分泌蛋白组5235个蛋白。本文所述的预测系统也可以应用于预测其他脊椎动物蛋白质组的分泌蛋白。
To increase the coverage of secreted protein prediction, we describe a combination strategy. Instead of using a single method, we combine Hidden Markov Model (HMM)-based methods CJ-SPHMM and TMHMM with PSORT in secreted protein prediction. CJ-SPHMM is an HMM-based signal peptide prediction method, while TMHMM is an HMM-based transmembrane (TM) protein prediction algorithm. With CJ-SPHMM and TMHMM, proteins with predicted signal peptide and without predicted TM regions are taken as putative secreted proteins. This HMM-based approach predicts secreted protein with Ac (Accuracy) at 0.82 and Cc (Correlation coefficient) at 0.75, which are similar to PSORT with Ac at 0.82 and Cc at 0.76. When we further complement the HMM-based method, i.e., CJ-SPHMM + TMHMM with PSORT in secreted protein prediction, the Ac value is increased to 0.86 and the Cc value is increased to 0.81. Taking this combination strategy to search putative secreted proteins from the International Protein Index (IPI) maintained at the European Bioinformatics Institute (FBI), we constructed a putative human secretome with 5235 proteins. The prediction system described here can also be applied to predicting secreted proteins from other vertebrate proteomes.