北京生物醫(yī)學(xué)工程

基于貝葉斯決策理論的磷酸化位點(diǎn)蛋白激酶識(shí)別算法

A novel algorithm for identifying protein kinases associated with phosphorylation sites based on Bayesian decision theory

作者：鄒亮李驁韓燕馮煥清王明會(huì)

單位：                      中國(guó)科學(xué)技術(shù)大學(xué)信息科學(xué)技術(shù)學(xué)院電子科學(xué)與技術(shù)系（合肥230601）        

關(guān)鍵詞：蛋白激酶；磷酸化；貝葉斯決策理論；生物信息學(xué)

分類號(hào)：

出版年·卷·期（頁(yè)碼）：2014·33·3（264-268）

摘要：

目的通過(guò)提出一種新穎的生物信息學(xué)算法，以準(zhǔn)確識(shí)別已知磷酸化位點(diǎn)的蛋白激酶信息，進(jìn)而解決蛋
白激酶的信息缺乏問(wèn)題。方法根據(jù)人類激酶的聚類規(guī)則，首先從最新版本的磷酸化數(shù)據(jù)庫(kù)Phospho.ELM
(9.0)中提取出激酶特異性的磷酸化數(shù)據(jù)，構(gòu)建用于激酶識(shí)別的數(shù)據(jù)集。然后基于貝葉斯決策理論，分析陽(yáng)
性數(shù)據(jù)和陰性數(shù)據(jù)中磷酸化位點(diǎn)附近的氨基酸分布規(guī)律，進(jìn)而給出相應(yīng)的統(tǒng)計(jì)模型并使用留一法對(duì)模型進(jìn)行
評(píng)估。結(jié)果對(duì)MAPK、PKA和RSK 3個(gè)激酶家族的測(cè)試表明，在假陽(yáng)性率不超過(guò)1%的高置信度水平下，激酶識(shí)
別的準(zhǔn)確率分別達(dá)到了23%、24%和33%。同時(shí)，該算法的識(shí)別結(jié)果明顯優(yōu)于KinasePhos、Netphosk等蛋白質(zhì)
磷酸化位點(diǎn)預(yù)測(cè)方法。結(jié)論本文提出的基于貝葉斯決策理論的磷酸化位點(diǎn)激酶信息識(shí)別算法可有效提高對(duì)
已知磷酸化位點(diǎn)的蛋白激酶識(shí)別性能，有助于理解蛋白質(zhì)磷酸化的生物機(jī)制。

Objective A novel machine learning method is proposed to identify protein kinase
for known phosphorylation sites,which can solve the problem of lacking kinase
information.Methods According to the hierarchy structure of human kinases,we firstly
constructed datasets for each kinase or kinase cluster by using the kinase-specific
phosphorylation instances extracted from the latest version of Phospho.ELM(9.0).Based on
Bayesian decision theory,we analyzed the amino acid distribution of each residue around the
phosphorylation sites in positive and negative dataset respectively and constructed
corresponding statistical models.In addition,we evaluated the performance of this algorithm by
using leave one out strategy in various datasets.Results The sensitivities of MAPK,PKA and RSK
reached 23%,24% and 33% when the false positive rate was 1%.The prediction performance was
also significantly better than phosphorylation site prediction methods such as KinasePhos and
Netphosk.Conclusions The proposed algorithm based on Bayesian decision theory effectively
enhanced the identification performance and contributed to better understanding of the
biological mechanism in protein phosphorylation process.

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