本文選題：P-gp + 定量構(gòu)效關(guān)系��； 參考：《重慶大學(xué)》2014年碩士論文

【摘要】：由P-糖蛋白(P-glycoprotein, P-gp)過度表達(dá)導(dǎo)致的腫瘤細(xì)胞的多藥耐藥性(multidrug resistance, MDR)，是腫瘤化療失敗的主要原因。因此，P-gp抑制劑預(yù)測(cè)和篩選已經(jīng)成為腫瘤藥物研發(fā)的一個(gè)重要內(nèi)容。論文以P-gp為研究對(duì)象，采用定量構(gòu)效關(guān)系、同源模建、分子對(duì)接和分子動(dòng)力學(xué)模擬等相關(guān)理論方法，對(duì)P-gp抑制劑和底物進(jìn)行了分類預(yù)測(cè)以及作用機(jī)理研究。論文研究結(jié)果對(duì)P-gp抑制劑相關(guān)藥物研發(fā)具有重要的理論意義和應(yīng)用價(jià)值。 主要研究?jī)?nèi)容和研究結(jié)果： ①以小鼠P-gp晶體結(jié)構(gòu)為模板構(gòu)建了兩個(gè)P-gp同源模型，并采用Amber力場(chǎng)對(duì)同源模型進(jìn)行了能量?jī)?yōu)化。模型質(zhì)量評(píng)價(jià)結(jié)果表明，，本文構(gòu)建的P-gp同源模型在結(jié)構(gòu)和能量上合理、可靠。 ②采用支持向量機(jī)(Support Vector Machine, SVM)和分子對(duì)接兩種方法進(jìn)行了P-gp抑制劑的預(yù)測(cè)研究。根據(jù)10-折交互驗(yàn)證結(jié)果，得到了僅包含3個(gè)描述子的最優(yōu)分類模型，其對(duì)857個(gè)訓(xùn)練集樣本的預(yù)測(cè)準(zhǔn)確度、敏感度、特異度和馬修斯相關(guān)系數(shù)分別為0.840、0.873、0.813和0.683；對(duì)418個(gè)測(cè)試集樣本的預(yù)測(cè)準(zhǔn)確度、敏感度、特異度和馬修斯相關(guān)系數(shù)分別為0.868、0.938、0.738和0.704。Surflex-dock分子對(duì)接結(jié)果表明：對(duì)接得分可較好評(píng)價(jià)P-gp抑制劑/非抑制劑的相互作用，并具有較好的預(yù)測(cè)能力。進(jìn)一步研究顯示，最優(yōu)SVM模型和分子對(duì)接兩種預(yù)測(cè)方法對(duì)77.3%的樣本具有一致性的預(yù)測(cè)結(jié)果，其預(yù)測(cè)準(zhǔn)確度達(dá)到了0.931。與此同時(shí)，對(duì)接結(jié)果也顯示Phe699、Phe303、Phe695、Phe945和Phe39是P-gp抑制劑的重要結(jié)合位點(diǎn)。 ③采用SVM和分子對(duì)接方法構(gòu)建P-gp底物的分類模型。根據(jù)10-折交互驗(yàn)證結(jié)果，得到包含5個(gè)描述子的最優(yōu)分類模型，其對(duì)1731個(gè)訓(xùn)練集樣本的預(yù)測(cè)準(zhǔn)確度、敏感度、特異度和馬修斯相關(guān)系數(shù)分別為0.69、0.85、0.47和0.34，對(duì)193個(gè)測(cè)試集樣本的預(yù)測(cè)準(zhǔn)確度、敏感度、特異度和馬修斯相關(guān)系數(shù)分別為0.71、0.84、0.49和0.35。分子對(duì)接結(jié)果表明：Surflex-dock對(duì)接得分不能有效地區(qū)分P-gp底物和非底物。進(jìn)一步研究顯示：P-gp底物和非底物的結(jié)合位點(diǎn)無顯著性差異。 ④在P-gp同源模型基礎(chǔ)上，進(jìn)一步構(gòu)建了P-gp的跨膜模型，并在此基礎(chǔ)上進(jìn)行了10ns的分子動(dòng)力學(xué)模擬，以考察細(xì)胞膜和配體對(duì)P-gp構(gòu)象的影響。研究結(jié)果顯示：經(jīng)10ns的分子動(dòng)力學(xué)模擬，P-gp的跨膜模型在能量和結(jié)構(gòu)上達(dá)到平衡狀態(tài)�；趧�(dòng)力學(xué)的最低能量構(gòu)象，論文重新對(duì)1929個(gè)P-gp底物/非底物進(jìn)行了對(duì)接研究。研究結(jié)果顯示：Surflex-dock對(duì)接得分仍無法有效區(qū)分P-gp底物和非底物。究其原因可能是P-gp底物結(jié)構(gòu)和作用模式多樣性造成的。
[Abstract]:The multidrug resistance (MDR) caused by the overexpression of P-gp is the main reason for the failure of chemotherapy. Therefore, the prediction and screening of P-gp inhibitors have become an important content in the development of tumor drugs. In this paper, using quantitative structure-activity relationship, homology modeling, molecular docking and molecular dynamics simulation, the P-gp inhibitors and substrates were classified and predicted and the mechanism of their action was studied. The results of this paper have important theoretical significance and application value for the research and development of P-gp inhibitor-related drugs. The main research contents and results are as follows: 1 two P-gp homologous models were constructed using mouse P-gp crystal structure as template, and the energy of the homologous models was optimized by Amber force field. The model quality evaluation results show that the P-gp homologous model constructed in this paper is reasonable and reliable in structure and energy. 2 the prediction of P-gp inhibitors was studied by support vector machine support Vector machine and molecular docking. Based on the results of 10-fold interactive verification, an optimal classification model containing only three descriptors is obtained. The prediction accuracy and sensitivity of 857 training set samples are obtained. The correlation coefficients of specificity and Matthews were 0.840 ~ 0.873n 0.813 and 0.683, respectively. The predictive accuracy and sensitivity of 418 test samples were analyzed. The correlation coefficients of specificity and Matthews were 0.868 / 0.938 and 0.738, respectively. The results of docking showed that the docking score could evaluate the interaction between P-gp inhibitors and non-inhibitors, and had a good predictive ability. Further studies show that the optimal SVM model and molecular docking methods have consistent prediction results for 77.3% samples, and the prediction accuracy is 0.931. At the same time, the docking results also showed that Phe699, Phe303, Phe695Phe945 and Phe39 were important binding sites for P-gp inhibitors. (3) the classification model of P-gp substrate was constructed by SVM and molecular docking. According to the results of 10-fold interactive verification, an optimal classification model containing five descriptors is obtained. The prediction accuracy and sensitivity for 1731 training set samples are obtained. The predictive accuracy, sensitivity, specificity and Matthews correlation coefficient of 193 test samples were 0.71 ~ 0.84 ~ 0.49 and 0.35, respectively. The correlation coefficients of specificity and Matthews were 0. 69 and 0. 34, respectively. The predictive accuracy, sensitivity, specificity and Matthews correlation coefficient of 193 test samples were 0. 71 ~ 0. 84 and 0. 35, respectively. The results of molecular docking show that the docking score of 1: Surflex-dock can not effectively distinguish P-gp substrate from non-substrate. Further studies showed that there was no significant difference in the binding sites between substrates and non-substrates. (4) based on the P-gp homology model, the transmembrane model of P-gp was further constructed, and the molecular dynamics simulation of 10ns was carried out to investigate the effect of cell membrane and ligand on the conformation of P-gp. The results show that the transmembrane model of P-gp is balanced in energy and structure by 10ns molecular dynamics simulation. Based on the lowest energy conformation of dynamics, the docking of 1929 P-gp substrates / non substrates was studied again. The results show that the P-gp substrates and the non-substrates can not be effectively distinguished by a: Surflex-dock docking score. The reason may be the diversity of P-gp substrate structure and mode of action.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R91

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