本文選題：蛋白質(zhì)結(jié)合位點　切入點：氨基酸組成偏好　出處：《大連理工大學》2012年博士論文　論文類型：學位論文

【摘要】：生物分子和很多其它有機配體能夠與蛋白質(zhì)在其表面特定位點高度親和結(jié)合。如何區(qū)分這樣的結(jié)合位點與蛋白質(zhì)其它表面區(qū)域,這個問題是蛋白質(zhì)研究領(lǐng)域的前沿課題。近些年來,在蛋白質(zhì)分子表面上預測可能結(jié)合區(qū)域的潛在價值越來越重要。隨著生物學和醫(yī)學中重要蛋白質(zhì)的結(jié)構(gòu)知識的不斷增長,這樣的預測方法變得更加實用化。它能夠為合理藥物分子設(shè)計提供幫助,同時也可以揭示蛋白質(zhì)分子功能。對于功能預測和合理藥物設(shè)計兩方面的應(yīng)用,都需要一個可靠的蛋白質(zhì).配體結(jié)合位點識別和定義方法。在蛋白質(zhì)復合體三維結(jié)構(gòu)已知的情況下,就可以對蛋白質(zhì).蛋白質(zhì)相互作用界面以及蛋白質(zhì).配體結(jié)合面做關(guān)于氨基酸分布和物理化學特征的系統(tǒng)分析,這使得活性位點的識別成為可能。已經(jīng)有很多計算方法被開發(fā)出來,利用這些信息預測蛋白質(zhì)可能的結(jié)合位點。但是,目前的方法在預測精度和效率上仍然存在不足,所以需要進一步研究結(jié)合位點預測方法以提高其預測能力,揭示其關(guān)鍵影響因素。 本文研究蛋白質(zhì)結(jié)合位點的預測方法,主要包括四個部分。 第一章,首先描述了蛋白質(zhì)-配體相互作用原理,包括熱力學理論、結(jié)合過程理論模型和物理學性質(zhì)。然后,概述了蛋白質(zhì)結(jié)合位點預測研究現(xiàn)狀,包括蛋白質(zhì).配體結(jié)合位點預測和蛋白質(zhì).蛋白質(zhì)結(jié)合位點預測兩個方面內(nèi)容。最后,簡要介紹了本文主要工作內(nèi)容以及取得的結(jié)果。 第二章,提出了兩種新的氨基酸組成偏好表示模型,分別以原子和原子接觸對作為統(tǒng)計對象,區(qū)別于傳統(tǒng)使用殘基作為統(tǒng)計對象的模型�；谌挚诖玫呐潴w結(jié)合口袋識別方法測試結(jié)果顯示,基于原子和基于原子接觸對模型要優(yōu)于基于殘基的模型。由于結(jié)合位點上存在所謂熱點區(qū)域,我們定義偏好值最大的局部區(qū)域作為一個口袋的熱點,這個局部偏好值代表整個口袋的偏好屬性,再結(jié)合口袋大小屬性形成了基于局部口袋偏好的配體結(jié)合口袋識別方法。結(jié)果分析顯示,這兩個屬性能夠相互促進、極大提高識別能力；與文獻上發(fā)表的一些預測方法比較,我們的方法取得了相當?shù)臏蚀_率并具有計算簡單的優(yōu)點。 第三章,基于蛋白質(zhì)-配體結(jié)合位點與蛋白質(zhì)-蛋白質(zhì)結(jié)合位點在幾何特征和物理化學性質(zhì)方面的差異,我們分別提出了兩種殘基屬性定義模型,即單塊和多塊殘基屬性定義模型。由殘基屬性定義模型得到的殘基特征,利用隨機森林算法構(gòu)建了結(jié)合殘基分類預測器。另外,我們還提出了一種新的聚類方法用來發(fā)現(xiàn)并預測結(jié)合位點。這些方法分別被應(yīng)用于蛋白質(zhì)-配體與蛋白質(zhì)-蛋白質(zhì)結(jié)合殘基的預測。采用相同數(shù)據(jù)集及成功標準,基于單塊殘基屬性定義模型的隨機森林分類器在蛋白質(zhì)-配體結(jié)合位點預測準確率方面要優(yōu)于Q-SiteFinder, SCREEN和Morita's method三種方法；同樣,平衡準確率和CC(Correlation Coefficient)值結(jié)果顯示,基于多塊殘基屬性定義模型的隨機森林分類器在蛋白質(zhì).蛋白質(zhì)結(jié)合殘基預測能力方面優(yōu)于Yan、Wang以及Chen and Jeong的方法；在蛋白質(zhì)-蛋白質(zhì)結(jié)合位點預測方面,基于多塊殘基屬性定義模型的預測器也都優(yōu)于Bradford and Westhead's method、Bradford and Needham's method和Higa and Tozzi's method。 第四章,把基于隨機森林的蛋白質(zhì)結(jié)合位點預測方法用于輔助分子對接。對于蛋白質(zhì)-配體分子對接,隨機森林預測方法以前端使用方式縮小構(gòu)象搜索空間。對接結(jié)果表明,該預測方法在輔助對接方面要優(yōu)于流行軟件Accelrys Discovery Studio中的結(jié)合位點預測方法。在蛋白質(zhì)-蛋白質(zhì)分子對接中,隨機森林預測方法按后端使用方式,即作為一種打分函數(shù)用來挑選近自然構(gòu)象,對接實驗表明,基于預測信息設(shè)計的打分模型在識別近自然構(gòu)象方面與ZDOCK打分函數(shù)各有優(yōu)勢,有較大的互補性。 論文最后部分對本文的工作做了總結(jié)并且對后續(xù)研究進行了展望。 本文工作受到國家自然科學基金項目“藥物分子優(yōu)化設(shè)計的網(wǎng)格計算方法研究(No.10772042)”,國家863科技計劃項目“新藥研發(fā)網(wǎng)(No.2006AA01A124)”和《國家重點基礎(chǔ)研究發(fā)展規(guī)劃》項目“蛋白質(zhì)動態(tài)行為和相互作用模擬新方法研究(No.2009CB918501)”的資助。
[Abstract]:Molecular biology and many other organic ligands can bind with high affinity protein on the surface of a specific location. How to distinguish such binding sites and other protein surface area, this problem is a frontier field of protein research. In recent years, the protein molecules on the surface of prediction may be combined with potential value areas continuously along with the more and more important. The growth structure knowledge of important proteins in biology and medicine, this prediction method becomes more practical. It can provide help for rational drug design, but also can reveal the protein molecular function. The function prediction and rational drug design and application of the two aspects, are in need of a reliable protein ligand binding site. The identification and definition method. In protein complexes with known 3D structure case, can the protein protein interaction field. The surface and proteins. Ligand binding surface analysis system on the distribution of amino acid and physical and chemical characteristics, which makes the identification of active sites as possible. There have been many computational methods were developed to predict protein binding sites may use this information. However, the current method in the prediction accuracy and efficiency are still insufficient, so it is necessary to further study of binding site prediction methods to improve the prediction ability, reveals the key influencing factors.
This paper studies the prediction methods of protein binding sites, including four parts.
The first chapter, first described the protein ligand interaction principle, including thermodynamic theory, combined with the process of theoretical model and physics properties. Then, the protein binding site prediction research, including protein ligand binding site prediction and protein. The protein binding site prediction of two aspects. Finally, this paper briefly introduced the main content of the work and the results obtained.
The second chapter puts forward two kinds of new amino acid preference representation model, respectively, and atoms of contact as the statistical object, different from the traditional use of residues as the statistical object model. The ligand binding pocket pocket global preference test results show that the recognition method based on atomic and contact model is better than the model based on residues based on the atom based. Due to the combination of the existence of the so-called hot spots on the site, we define the preference value of local maximum area as a hot pocket, the local preference attribute value preference on behalf of the pocket, and then formed a pocket recognition method based on local preference based on pocket ligand binding pocket size property. The results of analysis showed that the two attributes can promote each other, greatly improve the recognition ability; compared with some published literature on forecasting methods, our method achieved when quasi phase Accuracy and simplicity of calculation.
The third chapter, differences in protein ligand binding sites and protein protein binding sites in the geometric and physical and chemical properties based on, we propose two residue attribute definition model, single and multi block residue attribute definition model. Residue characteristics derived from residues attribute definition model, using the random forest algorithm is constructed with residue classification predictor. In addition, we also propose a new clustering method to discover and predict binding sites. These methods were applied to predict protein-protein and protein ligand binding residues. Using the same data set and success criteria, single residue attribute definition model the random forest classifier in protein ligand binding site prediction accuracy is better than Q-SiteFinder based on SCREEN, Morita's and method three methods; also, the balance of accuracy and CC (Co Rrelation Coefficient) results show that, based on the multi block residue attribute definition model of random forest classifier in protein. Protein binding residues prediction ability is superior to Yan, Wang and Chen and Jeong method; in the protein-protein binding site prediction, based on multi block residue attribute definition model predictor are better than that of Bradford and Westhead's method, Bradford and Needham's method and Higa and Tozzi's method.
The fourth chapter, the random forest protein binding site prediction method for computer-aided molecular docking based for protein ligand docking, the previous prediction method of random forest end using the way to narrow the search space. The conformation of the docking results show that the prediction method to forecast method is better than the popular software Accelrys Discovery binding sites in the Studio in the auxiliary docking area. In protein-protein docking, random forest forecast methods used in the back-end, as a scoring function to select the near natural conformation, docking experiments show that based on the scoring model of information design prediction in recognition of near natural conformation and ZDOCK scoring functions have their own advantages and are complementary.
The last part of the paper makes a summary of the work of this paper and looks forward to the follow-up research.
This study was supported by the method of molecular drug optimization design of grid computing projects of the National Natural Science Foundation (No.10772042) ", 863 national science and technology project" research and development of new drugs network (No.2006AA01A124) "and" national key basic research and development plan > Project "Research on new method for protein interaction and dynamic behavior simulation (No.2009CB918501)" of China.

【學位授予單位】：大連理工大學
【學位級別】：博士
【學位授予年份】：2012
【分類號】：R341

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本文編號：1602028