药物靶标蛋白筛选的化学蛋白质组学研究进展

药物或生物活性物质通过与靶蛋白结合而发挥功能,研究表明,大多数药物具有多个作用靶点,药物靶标的发现有助于药物前体的筛选和作用机制的研究,同时对其耐药性等副作用的解决方案提供理论指导.基于生物质谱技术的蛋白质组学可对蛋白质进行高通量的定性定量分析,为药物靶标的筛选提供了全新的平台.本文综述了基于固载药物和游离药物模式的药物靶标蛋白筛选相关方法和应用研究的最新进展,为基于生物质谱技术的化学蛋白质组学研究提供参考.     

G蛋白偶联受体计算研究的进展和前瞻

G蛋白偶联受体(G protein-coupled receptor,GPCR)是含有七个跨膜螺旋的一类重要蛋白,是迄今为止发现的最大的多药物靶标受体超蛋白家族。例如,目前上市药物中有超过30%是以GPCR为靶点的。然而,与GPCR重要性形成强烈反差的是科学界对于其结构与功能的了解非常贫乏,主要原因是通过实验手段来获得GPCR的结构与功能信息极其困难。利用生物信息学方法从基因组规模的数据中识别GPCR并预测三维结构是可行途径之一。基于生物信息学的GPCR研究将为新型药物靶标的筛选和药物的开发提供一定的帮助。本文论述了几种较为典型的GPCR计算方法,并基于已有研究提出可能的创新性研究策略来解决GPCR蛋白识别、跨膜区定位、以及结构和功能预测等问题。     

光亲和标记技术在药物发现中的应用

功能蛋白质组学的研究在药物发现中扮演着重要的角色,而光亲和标记技术是研究功能蛋白质组学的主要策略之一,它主要有两个方面的应用：靶标蛋白的确定和活性小分子配体与靶标蛋白作用模式的揭示,这些信息为药物的发现提供了强有力的支持。     

乙肝病毒X蛋白结合蛋白最新研究进展

乙肝病毒X蛋白结合蛋白(HBXIP)是肝细胞癌变过程的一个关键因素,它能在动物肌肉组织和恶性肿瘤组织中过表达。近年来研究显示:HBXIP能与人体内多种蛋白结合。本文综述了HBXIP蛋白参与细胞凋亡与增殖、细胞周期进程、中心体复制、肿瘤细胞迁移等过程,以期为以HBXIP蛋白为靶标的新的抗乙肝病毒等药物设计提供基础。     

化学蛋白质组学技术在药物靶标鉴定中的应用

药物开发过程面临多重挑战,而靶标确证是其中的重要一环。如何运用多种研究方法发现和确认小分子药物的靶标是目前研究人员的主要工作内容之一。化学蛋白质组学整合了细胞生物学、合成化学和生物质谱等多门学科,为药物的靶标筛选提供了新平台。本文对近年来发展的基于生物质谱的化学蛋白质组学药物靶标鉴定技术进行了总结,结合具体应用分析其优缺点,并对该类技术的发展和应用进行总结和展望。     

基因表达时间延迟调控关系识别软件ITdGR

基因表达调控网络的深入研究有利于分子药物靶标的发现以及推新药的研发,是未来生物医学研究的重要内容。针对基因表达调控的时间延迟问题,我们初步设计开发了一套基于基因表达谱数据识别基因表达时间延迟调控关系的软件ITdGR(Identification of Time-delayed Gene Regulations)。并已经成功地将该软件应用于酿酒酵母细胞周期的基因表达谱数据中,识别出的调控关系与已有的知识相符。该软件为基因调控网络重构以及基因表达动态研究提供了一个方便和快捷的工具。     

基于网络特征的药物靶蛋白识别

蛋白质很少孤立得发挥作用,往往通过网络中彼此互作来共同行使功能.因此分析药物靶蛋白在生物学网络中的性质将十分有助于从信息学角度理解药物的作用机制.但目前尚无研究对药物靶蛋白在人类蛋白质互作网络中的拓扑特性给与具体的分析和描述.本文首先将药物靶蛋白映射到人类蛋白质互作网络中,进而分析了药物作用靶蛋白在互作网络中的5种拓扑指标,并与互作网络中全蛋白质组集合及非药物靶点集合的拓扑指标进行了对比.结果显示,药物靶蛋白之间具有更高的连通性,信息能够得到更快得传递.基于这些拓扑特征,将互作网络中的所有蛋白进行排序.发现排序在前100位的蛋白中有48个是Drugbank中记录的药物靶点,另外的52个蛋白中有9个蛋白已在TTD,Matador等数据库中被记录为药物靶点,还有部分蛋白通过文献检索被证实为药物靶点.     

G蛋白偶联受体异源二聚化及其药理学意义

G蛋白偶联受体(G-protein coupled receptors, GPCRs)是细胞膜表面最大的跨膜蛋白超家族受体,在细胞的信号转导中发挥着重要的作用,是重要的药物靶标。目前越来越多的证据表明GPCRs的异源二聚化可以产生不同于单体的信号通路及功能,从而大大增加了有效的药物靶标数量,因此研究GPCRs的异源二聚化激活机制具有重要的药理学意义。本文就以上内容的最新研究进展进行综述,为相关科研人员研究GPCRs异源二聚化提供理论基础。     

新型冠状病毒(SARS-CoV-2)蛋白靶位的生信分析

目前新型冠状病毒肺炎(COVID-19)疫情仍在全球肆虐,但尚无针对该病毒的治疗特效药.在此背景,以美国化学文摘社(Chemical Abstracts Service,CAS)提供的SARS-CoV-2病毒及宿主蛋白靶标为研究对象,运用基因功能富集、蛋白网络等方法进行生物信息分析.结果发现,人网格蛋白介导型内吞和依赖...     

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.     

AlphaFold2 protein structure prediction: Implications for drug discovery

The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three-dimensional structure of proteins has made protein targets more accessible to the drug design process. Here, we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines.     

Crystal structure of the Leishmania major MIX protein: a scaffold protein that mediates protein-protein interactions

Infection by Leishmania and Trypanosoma causes severe disease and can be fatal. The reduced effectiveness of current treatments is largely due to drug resistance, hence the urgent need to develop new drugs, preferably against novel targets. We have recently identified a mitochondrial membrane‐anchored protein, designated MIX, which occurs exclusively in these parasites and is essential for virulence. We have determined the crystal structure of Leishmania major MIX to a resolution of 2.4 Å. MIX forms an all α‐helical fold comprising seven α‐helices that fold into a single domain. The distribution of helices is similar to a number of scaffold proteins, namely HEAT repeats, 14‐3‐3, and tetratricopeptide repeat proteins, suggesting that MIX mediates protein–protein interactions. Accordingly, using copurification and mass spectroscopy we were able to identify several proteins that may interact with MIX in vivo. Being parasite specific, MIX is a promising new drug target and, thus, the structure and potential interacting partners provide a basis for structure‐guided drug discovery.     

Combinatorial chemistry defines general properties of linkers for the optimal display of peptide ligands for binding soluble protein targets to TentaGel microscopic beads

Affinity chromatography and the binding of soluble target proteins to novel or known ligands attached to solid supports are important phenomena to basic and applied research. Satisfactory display of a ligand for the acceptor protein is critical for successful binding to occur. Here we describe the application of combinatorial chemistry to systematically explore the properties of linkers used to present peptide ligands to various protein targets. Our main interest is in drug discovery, and our results probably explain, in large part, the disappointing efficiency of an early drug discovery method known as the "Selectide Process" (Lam, K. S., et al. (1991) Nature 358, 82-84). Interestingly, for all seven protein targets studied, a cationic feature was found to be a common theme for optimal linkers displaying peptide ligands on TentaGel beads, and this is not likely to be caused by ionic exchange mechanisms.     

Target network analysis of adiponectin,a multifaceted adipokine

Application of network analysis to dissect the potential molecular mechanisms of biological processes and complicated diseases has been the new trend in biology and medicine in recent years. Among which, the protein–protein interactions (PPI) networks attract interests of most researchers. Adiponectin, a cytokine secreted from adipose tissue, participates in a number of metabolic processes, including glucose regulation and fatty acid metabolism and involves in a series of complicated diseases from head to toe. Hundreds of proteins including many identified and potential drug targets have been reported to be involved in adiponectin related signaling pathways, which comprised a complicated regulation network. Therapeutic target database (TTD) provides extensive information about the known and explored therapeutic protein targets and the signaling pathway information. In this study, adiponectin associated drug targets based PPI was constructed and its topological properties were analyzed, which might provide some insight into the dissection of adiponectin action mechanisms and promote adiponectin signaling based drug target identification and drug discovery. J. Cell. Biochem. 114: 1145–1152, 2013. © 2012 Wiley Periodicals, Inc.     

Distinctive Behaviors of Druggable Proteins in Cellular Networks

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.     

Thermal proteome profiling: unbiased assessment of protein state through heat-induced stability changes

In recent years, phenotypic-based screens have become increasingly popular in drug discovery. A major challenge of this approach is that it does not provide information about the mechanism of action of the hits. This has led to the development of multiple strategies for target deconvolution. Thermal proteome profiling (TPP) allows for an unbiased search of drug targets and can be applied in living cells without requiring compound labeling. TPP is based on the principle that proteins become more resistant to heat-induced unfolding when complexed with a ligand, e.g., the hit compound from a phenotypic screen. The melting proteome is also sensitive to other intracellular events, such as levels of metabolites, post-translational modifications and protein-protein interactions. In this review, we describe the principles of this approach, review the method and its developments, and discuss its current and future applications. While proteomics has generally focused on measuring relative protein concentrations, TPP provides a novel approach to gather complementary information on protein stability not present in expression datasets. Therefore, this strategy has great potential not only for drug discovery, but also for answering fundamental biological questions.     

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.     

Selecting targets for therapeutic validation through differential protein expression using chromatography-mass spectrometry

The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors: level in steady-state may not be correlated with actual encoded protein levels; differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study. In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.     

Analysis of protein features and machine learning algorithms for prediction of druggable proteins

Background: Computational tools have been widely used in drug discovery process since they reduce the time and cost. Prediction of whether a protein is druggable is fundamental and crucial for drug research pipeline. Sequence based protein function prediction plays vital roles in many research areas. Training data, protein features selection and machine learning algorithms are three indispensable elements that drive the successfulness of the models. Methods: In this study, we tested the performance of different combinations of protein features and machine learning algorithms, based on FDA-approved small molecules’ targets, in druggable proteins prediction. We also enlarged the dataset to include the targets of small molecules that were in experiment or clinical investigation. Results: We found that although the 146-d vector used by Li et al. with neuron network achieved the best training accuracy of 91.10%, overlapped 3-gram word2vec with logistic regression achieved best prediction accuracy on independent test set (89.55%) and on newly approved-targets. Enlarged dataset with targets of small molecules in experiment and clinical investigation were trained. Unfortunately, the best training accuracy was only 75.48%. In addition, we applied our models to predict potential targets for references in future study. Conclusions: Our study indicates the potential ability of word2vec in the prediction of druggable protein. And the training dataset of druggable protein should not be extended to targets that are lack of verification. The target prediction package could be found on https://github.com/pkumdl/target_prediction.