Drug screening assay based on the interaction of intact Keap1 and Nrf2 proteins in cancer cells

Background

The Nrf2–Keap1 interaction is the major regulatory pathway for cytoprotective responses against oxidative and electrophilic stresses. Keap1, a substrate protein of a Cul3-dependent E3 ubiquitin ligase complex, is a negative regulator of Nrf2. The use of chemicals to regulate the interaction between Keap1 and Nrf2 has been proposed as a strategy for the chemoprevention of degenerative diseases and cancers.

Identification of genes related to proliferative diabetic retinopathy through RWR algorithm based on protein–protein interaction network

Proliferative diabetic retinopathy (PDR) is one of the most common complications of diabetes and can lead to blindness. Proteomic studies have provided insight into the pathogenesis of PDR and a series of PDR-related genes has been identified but are far from fully characterized because the experimental methods are expensive and time consuming. In our previous study, we successfully identified 35 candidate PDR-related genes through the shortest-path algorithm. In the current study, we developed a computational method using the random walk with restart (RWR) algorithm and the protein–protein interaction (PPI) network to identify potential PDR-related genes. After some possible genes were obtained by the RWR algorithm, a three-stage filtration strategy, which includes the permutation test, interaction test and enrichment test, was applied to exclude potential false positives caused by the structure of PPI network, the poor interaction strength, and the limited similarity on gene ontology (GO) terms and biological pathways. As a result, 36 candidate genes were discovered by the method which was different from the 35 genes reported in our previous study. A literature review showed that 21 of these 36 genes are supported by previous experiments. These findings suggest the robustness and complementary effects of both our efforts using different computational methods, thus providing an alternative method to study PDR pathogenesis.     

基于Cytoscape下斑马鱼衰老基因-蛋白质的模块化分析

近20年来,斑马鱼逐渐成为研究人类基因功能的重要模型动物。同时,通过对斑马鱼参考基因组序列和10 000多个蛋白编码基因的鉴定,表明斑马鱼至少与人类基因有75%的同源性,进一步验证了斑马鱼基因组序列可以作为衰老的研究模型。此外,其良好保守的分子和细胞生理学的广泛特征使斑马鱼成为揭示衰老、疾病和修复的潜在机制的极好模型。但是斑马鱼衰老的分子机制很少发生分子间的相互作用,因此蛋白质-蛋白相互作用(PPI)网络是非常可取的。本实验描述了斑马鱼这种生物衰老机制的模型,其涵盖了与衰老相关的87种蛋白质之间的767种相互作用。这不仅包含准确预测的PPI,还包含从文献收集以及实验所得的那些分子相互作用。同时,将这些分子相互作用模块化,形成模块化,找到11个中心基因,分析预测其衰老过程。希望能帮助研究斑马鱼的学者研究其衰老过程,提供一些假说和帮助。     

两种实验技术在蛋白质-蛋白质相互作用检测中的应用

蛋白质是生命活动的主要承担者.蛋白质种类繁多,结构多样,具有十分广泛的生物学功能,可作为载体蛋白、酶蛋白和信号肽等参与调控细胞内的各种代谢活动.生物体内蛋白质与其他分子的相互作用,尤其是蛋白质-蛋白质之间的相互作用,是蛋白质行使这些重要生物学功能的基础.通过研究可以相互作用的蛋白质形成的各种复合体,对揭示蛋白质的功能,...     

评估蛋白质相互作用可信度的生物信息学方法

随着基因组规模的高通量实验鉴定技术和计算预测方法的发展,出现了大量蛋白质相互作用数据,但大规模蛋白质相互作用数据中的较高比例的假阳性影响了相互作用数据的质量。生物信息学方法能够从已有的数据和知识出发,通过计算方法系统评估大规模蛋白质相互作用的可信度。本文从过程模型设计、数据集构建、特征选择与综合属性抽取、一些算法使用、实例概述等方面介绍了生物信息学方法评估蛋白质相互作用可信度的研究特点与进展。     

Sampling strategy for protein complex prediction using cluster size frequency

In this paper we propose a Markov chain Monte Carlo sampling method for predicting protein complexes from protein–protein interactions (PPIs). Many of the existing tools for this problem are designed more or less based on a density measure of a subgraph of the PPI network. This kind of measures is less effective for smaller complexes. On the other hand, it can be found that the number of complexes of a size in a database of protein complexes follows a power-law. Thus, most of the complexes are small-sized. For example, in CYC2008, a database of curated protein complexes of yeast, 42% of the complexes are heterodimeric, i.e., a complex consisting of two different proteins. In this work, we propose a protein complex prediction algorithm, called PPSampler (Proteins' Partition Sampler), which is designed based on the Metropolis–Hastings algorithm using a parameter representing a target value of the relative frequency of the number of predicted protein complexes of a particular size. In a performance comparison, PPSampler outperforms other existing algorithms. Furthermore, about half of the predicted clusters that are not matched with any known complexes in CYC2008 are statistically significant by Gene Ontology terms. Some of them can be expected to be true complexes.     

A novel complexity-to-diversity strategy for the diversity-oriented synthesis of structurally diverse and complex macrocycles from quinine

Recent years have witnessed a global decline in the productivity and advancement of the pharmaceutical industry. A major contributing factor to this is the downturn in drug discovery successes. This can be attributed to the lack of structural (particularly scaffold) diversity and structural complexity exhibited by current small molecule screening collections.Macrocycles have been shown to exhibit a diverse range of biological properties, with over 100 natural product-derived examples currently marketed as FDA-approved drugs. Despite this, synthetic macrocycles are widely considered to be a poorly explored structural class within drug discovery, which can be attributed to their synthetic intractability.Herein we describe a novel complexity-to-diversity strategy for the diversity-oriented synthesis of novel, structurally complex and diverse macrocyclic scaffolds from natural product starting materials. This approach exploits the inherent structural (including functional) and stereochemical complexity of natural products in order to rapidly generate diversity and complexity. Readily-accessible natural product-derived intermediates serve as structural templates which can be divergently functionalized with different building blocks to generate a diverse range of acyclic precursors. Subsequent macrocyclisation then furnishes compounds that are each based around a distinct molecular scaffold. Thus, high levels of library scaffold diversity can be rapidly achieved. In this proof-of-concept study, the natural product quinine was used as the foundation for library synthesis, and six novel structurally diverse, highly complex and functionalized macrocycles were generated.     

Protein interaction network for Alzheimer's disease using computational approach

Alzheimer''s disease (AD) is the most common form of dementia. It is the sixth leading cause of death in old age people. Despite recent advances in the field of drug design, the medical treatment for the disease is purely symptomatic and hardly effective. Thus there is a need to understand the molecular mechanism behind the disease in order to improve the drug aspects of the disease. We provided two contributions in the field of proteomics in drug design. First, we have constructed a protein-protein interaction network for Alzheimer''s disease reviewed proteins with 1412 interactions predicted among 969 proteins. Second, the disease proteins were given confidence scores to prioritize and then analyzed for their homology nature with respect to paralogs and homologs. The homology persisted with the mouse giving a basis for drug design phase. The method will create a new drug design technique in the field of bioinformatics by linking drug design process with protein-protein interactions via signal pathways. This method can be improvised for other diseases in future.     

Sam domain-based stapled peptides: Structural analysis and interaction studies with the Sam domains from the EphA2 receptor and the lipid phosphatase Ship2

Sam (Sterile alpha motif) domains represent small helical protein-protein interaction modules which play versatile functions in different cellular processes. The Sam domain from the EphA2 receptor binds the Sam domain of the lipid phosphatase Ship2 and this interaction modulates receptor endocytosis and degradation primarily generating pro-oncogenic effects in cell. To identify molecule antagonists of the EphA2-Sam/Ship2-Sam complex with anti-cancer activity, we focused on hydrocarbon helical stapled peptides. EphA2-Sam and one of its interactors (i.e., the first Sam domain of the adaptor protein Odin) were used as model systems for peptide design. Increase in helicity in the stapled peptides, with respect to the corresponding linear/native-like regions, was proved by structural studies conducted through CD (Circular Dichroism) and NMR (Nuclear Magnetic Resonance). Interestingly, interaction assays by means of NMR, SPR (Surface Plasmon Resonance) and MST (MicroScale Thermophoresis) techniques led to the discovery of a novel ligand of Ship2-Sam.