中国组织工程研究与临床康复
中國組織工程研究與臨床康複
중국조직공정연구여림상강복
JOURNAL OF CLINICAL REHABILITATIVE TISSUE ENGINEERING RESEARCH
2011年
21期
3983-3986
,共4页
李立奇%张瑗%周跃%王开发
李立奇%張瑗%週躍%王開髮
리립기%장원%주약%왕개발
亚细胞定位%氨基酸对%纤连蛋白%支持向量机%k最近邻
亞細胞定位%氨基痠對%纖連蛋白%支持嚮量機%k最近鄰
아세포정위%안기산대%섬련단백%지지향량궤%k최근린
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.
揹景:含FN域蛋白質在促進細胞遷移、黏附、生長、分化等方麵髮揮瞭重要功能,已被廣汎應用于各種新型生物材料中.研究它們的亞細胞位置有利于它們的生物功能研究和新型生物材料開髮.目的:實現含纖連蛋白域蛋白質的亞細胞定位預測.方法:從UniProt數據庫中隨機抽取80箇人類含纖連蛋白域蛋白質.計算每箇蛋白質的400種氨基痠對數量併組成400維嚮量.分彆利用支持嚮量機和k最近鄰法調用每箇蛋白質的400維輸入嚮量進行訓練和測試.同時,利用jackknife檢驗法對測試結果進行檢驗.結果與結論:利用支持嚮量機法和k最近鄰法法預測含纖連蛋白域蛋白質的亞細胞定位預測準確率分彆為92.5%和95.0%.說明利用支持嚮量機和k最近鄰法算法預測含纖連蛋白域蛋白質的亞細胞位置具有重要意義,有利于此類蛋白質的功能研究和新型生物材料的錶麵改造設計.
배경:함FN역단백질재촉진세포천이、점부、생장、분화등방면발휘료중요공능,이피엄범응용우각충신형생물재료중.연구타문적아세포위치유리우타문적생물공능연구화신형생물재료개발.목적:실현함섬련단백역단백질적아세포정위예측.방법:종UniProt수거고중수궤추취80개인류함섬련단백역단백질.계산매개단백질적400충안기산대수량병조성400유향량.분별이용지지향량궤화k최근린법조용매개단백질적400유수입향량진행훈련화측시.동시,이용jackknife검험법대측시결과진행검험.결과여결론:이용지지향량궤법화k최근린법법예측함섬련단백역단백질적아세포정위예측준학솔분별위92.5%화95.0%.설명이용지지향량궤화k최근린법산법예측함섬련단백역단백질적아세포위치구유중요의의,유리우차류단백질적공능연구화신형생물재료적표면개조설계.
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.