基于氨基酸对含纤连蛋白域蛋白质亚细胞的定位预测（英文）

背景：含FN域蛋白质在促进细胞迁移、黏附、生长、分化等方面发挥了重要功能,已被广泛应用于各种新型生物材料中。研究它们的亚细胞位置有利于它们的生物功能研究和新型生物材料开发。目的：实现含纤连蛋白域蛋白质的亚细胞定位预测。方法：从UniProt数据库中随机抽取80个人类含纤连蛋白域蛋白质。计算每个蛋白质的400种氨基酸对数量并组成400维向量。分别利用支持向量机和k最近邻法调用每个蛋白质的400维输入向量进行训练和测试。同时,利用jackknife检验法对测试结果进行检验。结果与结论：利用支持向量机法和k最近邻法法预测含纤连蛋白域蛋白质的亚细胞定位预测准确率分别为92.5%和95.0%。说明利用支持向量机和k最近邻法算法预测含纤连蛋白域蛋白质的亚细胞位置具有重要意义,有利于此类蛋白质的功能研究和新型生物材料的表面改造设计。

Subcellular localization prediction of proteins containing fibronectin domains using collocation of amino acid pairs

BACKGROUND：Proteins containing fibronectin domains play an important role in cell migration, adhesion, growth and differentiation and have been widely applied to a variety of new biological materials.Subcellular localization prediction of proteins containing fibronectin domains can promote protein function research and development of new biomaterials.OBJECTIVE：To realize subcellular localization prediction of proteins containing fibronectin domains.METHODS：A total of 80 human proteins were randomly selected from Uniprot database.The amino acid pairs for each protein were collocated to form 400 dimensional input feature vectors.The feature vectors were then trained and tested using support vector machine and k-nearest neighbor separately.The prediction quality was examined by the jackknife test.RESULTS AND CONCLUSION：The prediction accuracy was 92.5% and 95% for support vector machine and k-nearest neighbor methods respectively.This suggests that support vector machine and k-nearest neighbor methods are of important significance for predicting subcellular localization of proteins containing fibronectin domains and contribute to functional research of such proteins and surface modification of new biomaterials.