Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

Prediction of human-Streptococcus pneumoniae protein-protein interactions using logistic regression

Streptococcus pneumoniae is a major cause of mortality in children under five years old. In recent years, the emergence of antibiotic-resistant strains of S. pneumoniae increases the threat level of this pathogen. For that reason, the exploration of S. pneumoniae protein virulence factors should be considered in developing new drugs or vaccines, for instance by the analysis of host-pathogen protein-protein interactions (HP-PPIs). In this research, prediction of protein-protein interactions was performed with a logistic regression model with the number of protein domain occurrences as features. By utilizing HP-PPIs of three different pathogens as training data, the model achieved 57–77 % precision, 64–75 % recall, and 96–98 % specificity. Prediction of human-S. pneumoniae protein-protein interactions using the model yielded 5823 interactions involving thirty S. pneumoniae proteins and 324 human proteins. Pathway enrichment analysis showed that most of the pathways involved in the predicted interactions are immune system pathways. Network topology analysis revealed β-galactosidase (BgaA) as the most central among the S. pneumoniae proteins in the predicted HP-PPI networks, with a degree centrality of 1.0 and a betweenness centrality of 0.451853. Further experimental studies are required to validate the predicted interactions and examine their roles in S. pneumoniae infection.     

Computer-aided design and discovery of protein–protein interaction inhibitors as agents for anti-HIV therapy

Protein–protein interactions (PPI) are involved in most of the essential processes that occur in organisms. In recent years, PPI have become the object of increasing attention in drug discovery, particularly for anti-HIV drugs. Although the use of combinations of existing drugs, termed highly active antiretroviral therapy (HAART), has revolutionized the treatment of HIV/AIDS, problems with these agents, such as the rapid emergence of drug-resistant HIV-1 mutants and serious adverse effects, have highlighted the need for further discovery of new drugs and new targets. Numerous investigations have shown that PPI play a key role in the virus’s life cycle and that blocking or modulating them has a significant therapeutic potential. Here we summarize the recent progress in computer-aided design of PPI inhibitors, mainly focusing on the selection of the drug targets (HIV enzymes and virus entry machinery) and the utilization of peptides and small molecules to prevent a variety of protein–protein interactions (viral–viral or viral–host) that play a vital role in the progression of HIV infection.     

人CtBP2与CCNH相互作用的初步研究

CtBP2(E1AC-terminal binding protein 2)作为辅阻遏物与多种转录因子联系而参与到很多生物过程中,如细胞分化、凋亡、发育和肿瘤发生等,然而其中许多作用机制尚不明了.为了对CtBP2进行深入研究,利用高通量酵母双杂交技术,以人CtBP2为诱饵,与含有1000个人肝基因克隆的酵母双杂交文库进行接合筛选获得了一个与它相互作用的猎物蛋白CCNH(Cyclin H).通过GST-pull down、免疫共沉淀和亚细胞共定位等实验进一步证明了这两个蛋白在体外和体内的相互作用.     

Protein recognition using synthetic surface-targeted agents

The design of synthetic agents to disrupt protein-protein interactions has received relatively little attention in recent years. In this review we describe strategies for targeting different types of protein surfaces using mimetics of protein secondary or tertiary structure. In this way strong and selective binding to a protein surface has be achieved and disruption of clinically important protein-protein interactions has been demonstrated in models of human disease.     

Comparing the biological coherence of network clusters identified by different detection algorithms

Protein-protein interaction networks serve to carry out basic molecular activity in the cell. Detecting the modular structures from the protein-protein interaction network is important for understanding the organization, function and dynamics of a biological system. In order to identify functional neighbor- hoods based on network topology, many network cluster identification algorithms have been devel- oped. However, each algorithm might dissect a network from a different aspect and may provide dif- ferent insight on the network partition. In order to objectively evaluate the performance of four com- monly used cluster detection algorithms: molecular complex detection (MCODE), NetworkBlast, shortest-distance clustering (SDC) and Girvan-Newman (G-N) algorithm, we compared the biological coherence of the network clusters found by these algorithms through a uniform evaluation framework. Each algorithm was utilized to find network clusters in two different protein-protein interaction net- works with various parameters. Comparison of the resulting network clusters indicates that clusters found by MCODE and SDC are of higher biological coherence than those by NetworkBlast and G-N algorithm.     

Study of the effect of atorvastatin on the interaction between ICAM-1 and CD11b by live-cell single-molecule force spectroscopy

The interaction between the cell adhesion molecule CD11b and its ligand ICAM-1 plays an important role in inflammatory responses in the disease of atherosclerosis. Atorvastatin is a commonly prescribed statin drug which has been considered as one of the most potent therapeutic agents for atherosclerosis due to its lipid-lowering effect. Recently, there is a growing body of evidence that atorvastatin has anti-inflammatory effect. We have applied the advanced method of live-cell single-molecule force spectros...     

Thioether‐derived Macrocycle for Peptide Secondary Structure Fixation

Recently, we developed methods to stabilize peptides into various secondary structures, including α‐helix, type III turn and β‐hairpin via proper thioether based macrocyclization. These conformationally constrained peptidomimetics confer enhanced biophysical properties and provide a valuable avenue towards clinically‐relevant therapeutic molecules. In this personal account, thioether‐derived macrocyclization methods developed by our group for stabilization of α‐helix, type‐III β turn and β‐hairpin conformations are discussed.     

利用提升小波的蛋白质相互作用特征提取

利用提升小波从蛋白质序列中提取出它们相互作用的频谱特征,经支持向量机训练学习后,用于预测蛋白质间的相互作用.模拟计算结果表明,在阳性数据和阴性数据平衡的前提下,利用提升小波获取的低维蛋白质相互作用特征向量可以得到较高预测精度.进一步阐述了不同物种的蛋白质相互作用网络有着不同特征,为了得到更准确的预测结果,需要利用不同的方法提取蛋白质相互作用的特征.