Protein subcellular location prediction
Protein subcellular location prediction
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DOI：
10.1093/protein/12.2.107
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发表时间：
1999-02-01
期刊：
影响因子：
--
通讯作者：
Elrod, DW
中科院分区：
文献类型：
--
作者：
Chou, KC;Elrod, DW
蛋白质的功能与其亚细胞位置密切相关。随着进入数据库的新蛋白质序列的迅速增加，我们面临着一个挑战：是否有可能利用生物信息学的方法来帮助加快蛋白质亚细胞位置的确定？为了探索这个问题，根据蛋白质的亚细胞位置，将其分为以下12组：（1）叶绿体，（2）细胞质，（3）细胞骨架，（4）内质网，（5）细胞外，（6）高尔基体，（7）溶酶体，（8）线粒体，（9）细胞核，（10）过氧化物酶体，（11）质膜和（12）液泡，基于已覆盖动植物细胞内几乎所有细胞器和亚细胞区室的分类方案，提出了一种协变判别算法，根据氨基酸组成预测查询蛋白质的亚细胞定位。通过自一致性检验、刀切检验和独立数据集检验，结果表明该算法的预测正确率明显高于现有方法。预计分类方案和概念以及预测算法可以加快新蛋白质的功能性确定，这也可以用于通过基因组工作鉴定为药物设计的潜在分子靶标的基因和蛋白质的优先级排序。
The function of a protein is closely correlated with its subcellular location. With the rapid increase in new protein sequences entering into data banks, we are confronted with a challenge: is it possible to utilize a bioinformatic approach to help expedite the determination of protein subcellular locations? To explore this problem, proteins were classified, according to their subcellular locations, into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracell, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane and (12) vacuole, Based on the classification scheme that has covered almost all the organelles and subcellular compartments in an animal or plant cell, a covariant discriminant algorithm was proposed to predict the subcellular location of a query protein according to its amino acid composition. Results obtained through self-consistency, jackknife and independent dataset tests indicated that the rates of correct prediction by the current algorithm are significantly higher than those by the existing methods. It is anticipated that the classification scheme and concept and also the prediction algorithm can expedite the functionality determination of new proteins, which can also be of use in the prioritization of genes and proteins identified by genomic efforts as potential molecular targets for drug design.