以高斯演化方式預測蛋白質-蛋白質嵌合位置

A Gaussian evolutionary method for predicting protein-protein interaction sites

劉康平(Kang-Ping Liu)

指導教授 ： 楊進木

國立交通大學/生物科技學院/生物資訊及系統生物研究所/碩士(2009年)

https://doi.org/10.6842/NCTU.2009.00174

摘要

研究蛋白質-蛋白質之間的交互作用在分子生物的領域中扮演非常重要的任務，一個可行的策略是先辨認及研究蛋白質-蛋白質交互作用的區域，再根據交互作用區域的特性進行分析，並且了解蛋白質的功能。在這篇論文研究中，我們發展了一個適用於預測蛋白質-蛋白質交互作用區域的函式，結合了原子的疏水性以及蛋白質的二級結構的特性，利用高斯演算法進行最佳化後，在測試評量中有不錯的表現。我們使用了104個蛋白質結構進行最佳化函式的訓練，在訓練的資料中我們成功的預測超過半數以上的蛋白質-蛋白質交互作用區域(65.4%)。此外，我們將訓練之後的最佳化函式測試在50個沒有出現在訓練資料內的蛋白質結構中，我們的函式可以成功的預測其中的46%蛋白質-蛋白質交互作用區域，我們相信在函式中使用的參數對於分析蛋白質-蛋白質之間的交互作用是有幫助的，並且可以應用到不同的方法上來預測蛋白質-蛋白質交互作用區域。此外，我們修改著名的蛋白質-配體嵌合工具”GEMDOCK”成為蛋白質-蛋白質嵌合工具，並且使用內建的蛋白質-配體計分程式來計算蛋白質-蛋白質嵌合的結果。我們測試了50個蛋白質結構，並且發現修改後的”GEMDOCK”在搜尋蛋白質-蛋白質嵌合之空間結構仍然保有相當的水準，可是在計算最佳嵌合結構時，原始的蛋白質-配體計分程式對於辨認蛋白質-蛋白質交互作用的情況仍然不足。未來，我們將會整合蛋白質-蛋白質交互作用區塊之預測到”GEMDOCK”中，並且改進蛋白質-配體計分程式成為蛋白質-蛋白質計分程式，以及發展可以改變蛋白質構形的策略，用以解決”unbound”蛋白質-蛋白質嵌合問題。

關鍵字

蛋白質-蛋白質交互作用 ； 交互作用區域 ； 高斯演算法 ； GEMDOCK ； 最佳化

並列摘要

Protein-protein interactions play a pivotal role in modern molecular biology. Therefore, identifying the interface between two interacting proteins is a matter of great scientific and practical interest. In this study, we proposed a Gaussian Evolutionary Method (GEM) to optimal atomic and 2nd structure parameters for predicting protein-protein interaction sites. The training set of GEM consist 104 unbound proteins from PDB, and we are able to predict the location of the interface on 65.4%. In addition, we apply trained GEM to testing set of 50 unbound proteins. Our method can predict 98% proteins among whole testing set and have 46% successfully prediction and 42.3% average specificity. A prediction is assumed successful if over half of the predicted continuous interface patch is indeed interface (specificity). The parameters of GEM may be useful for analysis of protein-protein interfaces, and can apply to different methods for interfaces prediction. Furthermore, we have modified famous protein-ligand docking tool “GEMDOCK” for protein-protein docking using original scoring functions. We tested 50 bound protein-protein docking and found that search strategies of GEMDOCK works well in rigid-body protein-protein docking, however, the scoring functions of protein-ligand docking seems poor to identify correct protein-protein binding conformations. In the future, we will combine protein-protein interaction sites prediction into GEMDOCK and improve scoring function of GEMDOCK for protein-protein docking and develop soft-body protein-protein docking strategies for solving unbound-unbound protein docking problems.

並列關鍵字

Protein-protein interactions ； interface ； Gaussian Evolutionary Method ； GEMDOCK ； optimal

參考文獻

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