使用分數卡方法發現可辨識抗氧化蛋白 的特定二級結構

Abstract

酵素性抗氧化防禦系統(Enzymatic antioxidant defense systems)，是由一群具有能代謝活性氧化物（Reactive oxygen species, ROS）的抗氧化蛋白(Antioxidant Protein, AOP)而組成的系統。抗氧化蛋白作為活性氧化物的清除者(ROS scavengers)，以間接或直接的方式，清除各自對應的活性氧化物，避免氧化壓力的發生。抗氧化蛋白議題在過去已廣為研究，但多是對單一的抗氧化蛋白做探討，卻鮮少對整體抗氧化蛋白的功能特性分析及研究。SCM(scoring card method)是以雙胜肽為特徵，建置的二元分類器。應用雙胜肽差異量，經最佳化後，產生一張400個雙胜肽傾向分數表以及一張20個氨基酸傾向分數表。依據序列本身的傾向特徵值對應AAindex，將獲得與感興趣蛋白功能相關的物化特性訊息。本研究的目的為，使用分數卡方法(SCM)建置抗氧化蛋白分類器(SCMAOP)，依據SCMAOP輸出結果，探勘抗氧化蛋白的生物特性和可辨識抗氧化蛋白的特徵區。進一步，依據此特徵區結合SCMAOP，找尋可能的抗氧化蛋白(putative AOP)。 抗氧化蛋白(AOP) 數據集是依據已知的抗氧化蛋白對應的活性氧化物（O22-和H2O2），分成兩類的集合(AOP1和AOP2)。AOP1組為SOD (superoxide dismutase)、AOP2組為具有過氧化物活性(Peroxidase activity)的蛋白。數據收集分別取自SwissProt、PeroxiBase。SCMAOP效能評估與驗證結果顯示，在準確性評估上為86.17%，表示SCMAOP可作為AOP的預測分類器。數據分析結果顯示，(1.) 發現，抗氧化蛋白呈高傾向是alpha-helix結構。(2.) 發現，抗氧化蛋白序列具有高傾向CP、DP和部分Proline相關之雙胜肽組合。Proline residue是易影響蛋白質結構且常形成beta-turn結構的主因。經數據結果驗證beta-turn結構在抗氧化蛋白(AOP)中有高含量。(3.) 發現，抗氧化蛋白的功能特徵區呈現特定二級結構，是以beta-strand為起點，連結 loop – helix region，且內部需含有beta-turn結構。經驗證，此特徵區對於抗氧化蛋白(AOP)是具有重要性的。(4.) 應用新發現的抗氧化蛋白之特定二級結構，建置IDAOP方法，用以找尋Putative AOP。(5.) 應用IDAOP方法後，從初步SCMAOP分類出的12101條Putative AOP序列中，最後篩選出131條Putative AOP(佔原數據集1.082%)。前50排名中，發現有四類蛋白具有類似或以間接方式執行抗氧化的功能特性。總體可知，結合SCMAOP及抗氧化蛋白收尋系統IDAOP方法，可協助生物學家找尋可能的抗氧化蛋白。

Antioxidant protein (AOP) is considered as biological endogenous antioxidant defense mechanisms, tends to donate electrons to scavenge Reactive oxygen species (ROS). The issue in the past has been widely disused which mostly focus on one topic of AOP, but rarely on overall. Therefore, this study proposes a novel methodological approach called SCMAOP to estimating the propensity scores of 400 dipeptides and 20 amino acids in order to design prediction methods and characterize AOPs based on a scoring card method (SCM). Moreover, using SCMAOP to finding the functional recognition pattern on antioxidant protein. The SCMAOP method for predicting AOPs achieves a test accuracy of 86.17%. A dataset consisting of known AOPs corresponding its ROS substrate both H2O2 and O22- were collected from PeroxiBase and SwissProt database. Additionally, informative physicochemical properties of 20 amino acids are identified using the estimated propensity scores to characterize AOPs as follows: 1) AOP have high propensity derived from alpha helix designed sequences. 2) The structure of AOP have a lot of beta-turn structure which may as catalytic environments. 3) The specific functional structure of AOP may located from beta-strand connecting loop-helix conformation which contain beta-turn structure. Further, we develop a tool called IDAOP based on the specific functional structure to verify our finding. The analysis results reveals that the specific secondary structure is important to antioxidant protein, beta-turn structure is the main impact factor, identifying 131 putative AOP from putative dataset after IDAOP method. According to the published studied that four putative AOP from top 50 rankings may have similar antioxidant function. Therefore, we believe that combine SCMAOP and IDAOP can help biologists to finding novel antioxidant protein.