本文關(guān)鍵詞： 蛋白質(zhì)8態(tài)二級結(jié)構(gòu) 二次判別法 化學(xué)位移 抗癌肽 預(yù)測　出處：《內(nèi)蒙古農(nóng)業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：蛋白質(zhì)的生物功能在很大程度上由它的空間結(jié)構(gòu)所決定,所以要了解和掌握蛋白質(zhì)的功能的前提是要先分析出蛋白質(zhì)的空間結(jié)構(gòu)。而蛋白質(zhì)其二級結(jié)構(gòu)識別的研究一般作為蛋白質(zhì)空間結(jié)構(gòu)預(yù)測的一個非常重要步驟。一般來說,蛋白質(zhì)二級結(jié)構(gòu)的預(yù)測工作主要是集中在蛋白質(zhì)3態(tài)二級結(jié)構(gòu)(alpha-螺旋,beta-折疊,無規(guī)卷曲)的預(yù)測上,但與蛋白質(zhì)3態(tài)二級結(jié)構(gòu)的比較而言,蛋白質(zhì)8態(tài)二級結(jié)構(gòu)能夠提供更為細(xì)致的結(jié)構(gòu)信息,也因此而更具挑戰(zhàn)性,尤其對于那些低同源性的蛋白。本文針對蛋白質(zhì)8態(tài)二級結(jié)構(gòu)提出了一種新的預(yù)測模型,即基于多特征組合結(jié)合二次判別算法(QDA)進(jìn)行預(yù)測。首先,選取了 200個蛋白,其氨基酸序列的一致性均低于30%,接著在200個蛋白中利用統(tǒng)計(jì)學(xué)方法提取6個原子的平均化學(xué)位移作為特征參量。然后,使用這些化學(xué)位移并結(jié)合6類親疏水殘基作為特征參量去預(yù)測蛋白質(zhì)8態(tài)二級結(jié)構(gòu)。最后,在七折交叉檢驗(yàn)下,蛋白質(zhì)8態(tài)二級結(jié)構(gòu)的預(yù)測總精度(Q8)達(dá)到80.7%。在同一數(shù)據(jù)集中,對比了其他預(yù)測工具,比如:應(yīng)用C8-Scorpion在線服務(wù)器進(jìn)行預(yù)測,還有采用支持向量機(jī)算法(SVM)以及隨機(jī)森林(RF)算法實(shí)施預(yù)測。結(jié)果顯示:對于預(yù)測精度而言,我們的預(yù)測模型是優(yōu)于其他預(yù)測算法的精度�？拱╇氖且环N具有明顯抗腫瘤活性的抗微生物肽,它們可以在體內(nèi)快速地消滅有害病菌,同時對人體腫瘤細(xì)胞也有很大的抵制作用。如何有效地識別抗癌肽是近十多年生物醫(yī)學(xué)研究的熱點(diǎn)問題之一。本文在已發(fā)表的公用的抗癌肽數(shù)據(jù)集中,添加了蛋白質(zhì)3種二級結(jié)構(gòu)組分(3PSS)作為新的特征參量,并結(jié)合20種氨基酸組分(20AAC)和6類親疏水氨基酸組分(6HP)作為特征信息,采用二次判別法(QDA)實(shí)施預(yù)測。在7折交叉檢驗(yàn)下,當(dāng)采用蛋白質(zhì)3種二級結(jié)構(gòu)組分(3PSS)結(jié)合6種親疏水氨基酸組分(6HP)作為特征時,預(yù)測總精度(Acc)達(dá)到86%;當(dāng)采用蛋白質(zhì)3種二級結(jié)構(gòu)組分(3PSS)結(jié)合20種氨基酸組分(20AAC)作為特征時,預(yù)測總精度達(dá)到94%。預(yù)測結(jié)果顯示:氨基酸序列信息作為特征參數(shù)時,若添加了二級結(jié)構(gòu)信息后,預(yù)測精度都有不同程度的提高。最后,在同種數(shù)據(jù)集中,與其他預(yù)測工作相比較,顯示了我們的模型的優(yōu)越性。
[Abstract]:The biological function of protein is largely determined by its spatial structure. Therefore, the premise of understanding and mastering the function of protein is to analyze the spatial structure of protein first, and the study of protein secondary structure recognition is generally regarded as a very important step in predicting protein spatial structure. Generally speaking. The prediction of protein secondary structure is mainly focused on the prediction of alpha-helix beta- fold, random crimp. However, compared with protein 3 secondary structure, protein 8 secondary structure can provide more detailed structure information, and therefore more challenging. Especially for those proteins with low homology, this paper proposes a new prediction model for protein 8-state secondary structure, which is based on multi-feature combination combined with quadratic discriminant algorithm (QDA). First of all. 200 proteins were selected and the amino acid sequence consistency was less than 30. Then the average chemical shifts of 6 atoms were extracted from 200 proteins by statistical method. These chemical shifts and six hydrophilic residues were used as characteristic parameters to predict the secondary structure of protein 8 states. Finally, under the 70 fold cross test. The total prediction accuracy of the secondary structure of protein 8 was 80.7. In the same data set, the other prediction tools were compared. For example: apply C8-Scorpion online server to predict. The support vector machine (SVM) algorithm and the random forest forest (RFR) algorithm are also used to implement the prediction. The results show that: for the accuracy of the prediction. Our prediction model is superior to other prediction algorithms. Anticancer peptides are antimicrobial peptides with obvious antitumor activity. They can quickly eliminate harmful bacteria in vivo. At the same time, it also has a great resistance to human tumor cells. How to effectively identify anticancer peptides is one of the hot issues in biomedical research in recent ten years. Three secondary structure components of proteins (3PSS) were added as new characteristic parameters, and 20 amino acid components (20 AAC) and 6 kinds of hydrophobic amino acids (6 kinds of hydrophobic amino acids) were used as characteristic information. QDAs were used to predict. Under the 7 fold cross test, the protein 3 secondary structure components (3PSSs) combined with 6 hydrophilic amino acids (6 HPs) were used as the characteristics. The total accuracy of prediction is 86%; When the protein was characterized by three secondary structure components (3PSS) and 20 amino acid fractions (20AAC). The prediction results show that when amino acid sequence information is used as the characteristic parameter, the prediction accuracy is improved to some extent if the secondary structure information is added. Finally, in the same data set. Compared with other prediction work, it shows the superiority of our model.
【學(xué)位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：Q51;R91

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 滕志霞;郭茂祖;;蛋白質(zhì)功能預(yù)測方法研究進(jìn)展[J];智能計(jì)算機(jī)與應(yīng)用;2016年04期

2 李U嗘，

本文編號：1468808