|
结合自由能和线性降维预测生物蛋白-蛋白质相互作用
Biological Protein-protein Interaction Prediction using Binding Free Energies and Linear Dimensionality Reduction |
|
| |
| 课程网址: |
http://videolectures.net/prib2010_rueda_bpip/
|
| 主讲教师: |
Luis Rueda
|
| 开课单位: |
温莎大学
|
| 开课时间: |
2010-10-14
|
| 课程语种: |
英语
|
| 中文简介: |
理解和分类蛋白质蛋白质相互作用（PPI）的一个重要问题是表征它们的界面以区分瞬时和专性复合物。我们提出了一种分类方法来区分这两种类型的复合物。我们的方法使用相互作用中存在的残基的接触和结合自由能，这是分类器的输入特征。每个复合体共提取了282个特征，并且通过最近提出的降维（LDR）方法进行分类，包括众所周知的Fisher判别分析和两个异方差方法。瞬态和专性蛋白质复合物的标准基准测试结果表明，LDR方法实现了非常高的分类准确度（超过78％），优于各种支持向量机和最近邻分类器。对所提出的方法和对不同特征子集的实验的额外见解表明，溶剂化能量可用于分类，导致与使用相互作用的完全结合自由能相当的性能。
|
| 课程简介: |
An important issue in understanding and classifying protein-protein interactions (PPI) is to characterize their interfaces in order to discriminate between transient and obligate complexes. We propose a classification approach to discriminate between these two types of complexes. Our approach uses contact and binding free energies of the residues present in the interaction, which are the input features for the classifiers. A total of 282 features are extracted for each complex, and the classification is performed via recently proposed dimensionality reduction (LDR) methods, including the well-know Fisher’s discriminant analysis and two heteroscedastic approaches. The results on a standard benchmark of transient and obligate protein complexes show that LDR approaches achieve a very high classification accuracy (over 78%), outperforming various support vector machines and nearest-neighbor classifiers. An additional insight on the proposed approach and experiments on different subsets of features shows that solvation energies can be used in the classification, leading to a performance comparable to using the full binding free energies of the interaction.
|
| 关 键 词: |
蛋白质; 分类器; 降维
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2019-09-14：lxf
|
| 阅读次数: |
56
|
|
图 书 馆