統(tǒng)計勢能函數(shù)及其在蛋白質(zhì)結(jié)構(gòu)預測中的應用
發(fā)布時間：2018-09-06 20:11
【摘要】：蛋白質(zhì)結(jié)構(gòu)預測是生物學中熱點問題之一,也是目前最有可能減少已知蛋白編碼序列和已解析蛋白質(zhì)結(jié)構(gòu)數(shù)目之間巨大差距的方法,對蛋白質(zhì)的結(jié)構(gòu)和功能研究非常重要。作為主流的結(jié)構(gòu)預測方法,從頭開始結(jié)構(gòu)預測主要包括兩個方面,即勢能函數(shù)和構(gòu)象搜索算法。勢能函數(shù)是蛋白質(zhì)三級結(jié)構(gòu)預測領(lǐng)域最困難的問題之一。研究表明,傳統(tǒng)上基于物理的勢能函數(shù)在蛋白質(zhì)結(jié)構(gòu)預測中效果較差,而基于統(tǒng)計分布的勢能函數(shù)在計算速度和準確率方面都有較明顯的優(yōu)勢。盡管已有大量的研究,但目前的統(tǒng)計勢能函數(shù)仍然不能滿足預測需求。一方面,對于常用的距離依賴性統(tǒng)計勢能函數(shù)來說,其中最為關(guān)鍵的參考態(tài)目前比較理想化,沒有考慮到肽鏈的真實環(huán)境,因而其效果難以進一步提升。另一方面,當前已經(jīng)有大量的蛋白質(zhì)結(jié)構(gòu)實驗數(shù)據(jù),這為更為復雜的多維統(tǒng)計勢能函數(shù)提供了可能。針對這種現(xiàn)狀,作者首先提出使用蛋白質(zhì)去折疊態(tài)作為參考態(tài)構(gòu)建了距離依賴型統(tǒng)計勢能函數(shù)SPOUSE。由于去折疊態(tài)僅包含肽鏈的基本特性,而極少包含特異性相互作用,因此不僅從理論上將蛋白質(zhì)結(jié)構(gòu)預測和蛋白質(zhì)折疊統(tǒng)一起來,而且測試結(jié)果表明其效果較現(xiàn)有同類函數(shù)有明顯的提升。隨后,作者進一步改進了距離依賴型統(tǒng)計勢能函數(shù),提出了同時基于距離和角度等位置取向信息的多維統(tǒng)計勢能ORDER_AVE。由于ORDER_AVE在一定程度上考慮了多體效應,因而相對于SPOUSE有非常顯著的提升。不僅如此,與其它取向依賴型函數(shù)相比,ORDER_AVE也有更高的識別準確率。與此同時,作者還設計了多種其他統(tǒng)計勢能函數(shù),包括軟核范德華、氫鍵、疏水作用、β折疊股成對作用和接觸能等,并在三個不同類型的蛋白中測試了每種函數(shù)對預測結(jié)果的影響。上述多種能量函數(shù)均被集成到最后的結(jié)構(gòu)預測程序中,而系統(tǒng)的總能量為所有能量函數(shù)的加權(quán)和。最后,在前人工作的基礎(chǔ)上,作者參與設計多種的構(gòu)象搜索算法和技巧,并使用C++編寫了一個蛋白質(zhì)結(jié)構(gòu)預測程序。初步結(jié)果表明,我們的程序?qū)τ讦恋鞍缀挺?β蛋白的預測效果較好。因此,本研究中我們不僅設計了兩個效果優(yōu)異的統(tǒng)計勢能函數(shù),而且基于編寫的程序,我們將持續(xù)在該領(lǐng)域發(fā)揮重要的作用。
[Abstract]:Protein structure prediction is one of the hot topics in biology. It is also the most likely method to reduce the huge gap between the number of known protein coding sequences and the number of resolved protein structures. Potential energy function (PEF) is one of the most difficult problems in the field of protein tertiary structure prediction. The results show that the traditional physical-based PEF is not effective in protein structure prediction, while the statistical distribution-based PEF has obvious advantages in computing speed and accuracy. Although a lot of research has been done, the statistical potential energy function can not meet the demand of prediction. On the one hand, the most important reference state of the distance-dependent statistical potential energy function is currently idealized without considering the real environment of the peptide chain, so its effect is difficult to further improve. A large number of experimental data on protein structure have been obtained, which makes it possible to construct a more complex multidimensional statistical potential energy function. In view of this situation, the authors first proposed to construct a distance-dependent statistical potential energy function SPOUSE using protein unfolded states as reference states. Specific interaction, therefore, not only unifies the prediction of protein structure and protein folding theoretically, but also improves the performance of the proposed method. Subsequently, the distance-dependent statistical potential energy function is further improved, and the location orientation information based on distance and angle is proposed. Multidimensional statistical potential energy ORDER_AVE. Because ORDER_AVE considers the multibody effect to a certain extent, ORDER_AVE has a very significant improvement over SPOUSE. Moreover, ORDER_AVE has a higher recognition accuracy than other orientation-dependent functions. Meanwhile, the author also designs a variety of other statistical potential energy functions, including soft-core Vander De. The effects of each function on the prediction results were tested in three different types of proteins. The various energy functions were integrated into the final structure prediction program, and the total energy of the system was the weighted sum of all the energy functions. Finally, the basis of previous work was given. On this basis, the author participated in the design of a variety of conformation search algorithms and techniques, and used C++ to write a protein structure prediction program. The preliminary results show that our program for alpha protein and alpha/beta protein prediction effect is better. Therefore, in this study, we not only designed two excellent statistical potential energy functions, but also based on the preparation of the program. We will continue to play an important role in this field.
【學位授予單位】：清華大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：Q51
，
本文編號：2227413
[Abstract]:Protein structure prediction is one of the hot topics in biology. It is also the most likely method to reduce the huge gap between the number of known protein coding sequences and the number of resolved protein structures. Potential energy function (PEF) is one of the most difficult problems in the field of protein tertiary structure prediction. The results show that the traditional physical-based PEF is not effective in protein structure prediction, while the statistical distribution-based PEF has obvious advantages in computing speed and accuracy. Although a lot of research has been done, the statistical potential energy function can not meet the demand of prediction. On the one hand, the most important reference state of the distance-dependent statistical potential energy function is currently idealized without considering the real environment of the peptide chain, so its effect is difficult to further improve. A large number of experimental data on protein structure have been obtained, which makes it possible to construct a more complex multidimensional statistical potential energy function. In view of this situation, the authors first proposed to construct a distance-dependent statistical potential energy function SPOUSE using protein unfolded states as reference states. Specific interaction, therefore, not only unifies the prediction of protein structure and protein folding theoretically, but also improves the performance of the proposed method. Subsequently, the distance-dependent statistical potential energy function is further improved, and the location orientation information based on distance and angle is proposed. Multidimensional statistical potential energy ORDER_AVE. Because ORDER_AVE considers the multibody effect to a certain extent, ORDER_AVE has a very significant improvement over SPOUSE. Moreover, ORDER_AVE has a higher recognition accuracy than other orientation-dependent functions. Meanwhile, the author also designs a variety of other statistical potential energy functions, including soft-core Vander De. The effects of each function on the prediction results were tested in three different types of proteins. The various energy functions were integrated into the final structure prediction program, and the total energy of the system was the weighted sum of all the energy functions. Finally, the basis of previous work was given. On this basis, the author participated in the design of a variety of conformation search algorithms and techniques, and used C++ to write a protein structure prediction program. The preliminary results show that our program for alpha protein and alpha/beta protein prediction effect is better. Therefore, in this study, we not only designed two excellent statistical potential energy functions, but also based on the preparation of the program. We will continue to play an important role in this field.
【學位授予單位】：清華大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：Q51
，
本文編號：2227413
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