Chou K C, Elrod D W
Computer-Aided Drug Discovery, Pharmacia & Upjohn, Kalamazoo, MI 49007-4940, USA.
Protein Eng. 1999 Feb;12(2):107-18. doi: 10.1093/protein/12.2.107.
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.
蛋白质的功能与其亚细胞定位密切相关。随着进入数据库的新蛋白质序列迅速增加，我们面临着一个挑战：是否有可能利用生物信息学方法来帮助加快蛋白质亚细胞定位的确定？为了探讨这个问题，根据蛋白质的亚细胞定位将其分为以下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。基于涵盖了动物或植物细胞中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，根据查询蛋白质的氨基酸组成预测其亚细胞定位。通过自一致性、留一法和独立数据集测试获得的结果表明，当前算法的正确预测率显著高于现有方法。预计该分类方案和概念以及预测算法能够加快新蛋白质功能的确定，这也可用于对基因组研究中鉴定出的作为药物设计潜在分子靶点的基因和蛋白质进行优先级排序。
