Proteomic characteristics of the liver and skeletal muscle in the Chinese tree shrew (Tupaia belangeri chinensis)

Valid animal models are useful for studying the pathophysiology of specific disorders, such as neural disease, diabetes and cancer. Previous molecular phylogeny studies indicate that the tree shrew is in the same order as (or a close sister to) primates, and thus may be an ideal model in which to study human disease. In this study, the proteome of liver and muscle tissue in tree the shrew was identified by combining peptide fractionation and LC-MS/MS identification. In total, 2146 proteins were detected, including 1759 proteins in liver samples and 885 proteins in skeletal muscle samples from the tree shrew. Further sub-source analysis revealed that nearly half of the identified proteins (846 proteins and 418 proteins) were derived from human database. In this study, we are the first to describe the characteristics of the proteome from the liver and skeletal muscle of the tree shrew. Phylogenetic tree analysis based on these proteomic data showed that the tree shrew is closer to primates (human) than to glires (the mouse and rat).     

Predict potential drug targets from the ion channel proteins based on SVM

The identification of molecular targets is a critical step in the drug discovery and development process. Ion channel proteins represent highly attractive drug targets implicated in a diverse range of disorders, in particular in the cardiovascular and central nervous systems. Due to the limits of experimental technique and low-throughput nature of patch-clamp electrophysiology, they remain a target class waiting to be exploited. In our study, we combined three types of protein features, primary sequence, secondary structure and subcellular localization to predict potential drug targets from ion channel proteins applying classical support vector machine (SVM) method. In addition, our prediction comprised two stages. In stage 1, we predicted ion channel target proteins based on whole-genome target protein characteristics. Firstly, we performed feature selection by Mann-Whitney U test, then made predictions to identify potential ion channel targets by SVM and designed a new evaluating indicator Q to prioritize results. In stage 2, we made a prediction based on known ion channel target protein characteristics. Genetic algorithm was used to select features and SVM was used to predict ion channel targets. Then, we integrated results of two stages, and found that five ion channel proteins appeared in both prediction results including CGMP-gated cation channel beta subunit and Gamma-aminobutyric acid receptor subunit alpha-5, etc., and four of which were relative to some nerve diseases. It suggests that these five proteins are potential targets for drug discovery and our prediction strategies are effective.     

Focusing in on structural genomics: the University of Queensland structural biology pipeline

The flood of new genomic sequence information together with technological innovations in protein structure determination have led to worldwide structural genomics (SG) initiatives. The goals of SG initiatives are to accelerate the process of protein structure determination, to fill in protein fold space and to provide information about the function of uncharacterized proteins. In the long-term, these outcomes are likely to impact on medical biotechnology and drug discovery, leading to a better understanding of disease as well as the development of new therapeutics. Here we describe the high throughput pipeline established at the University of Queensland in Australia. In this focused pipeline, the targets for structure determination are proteins that are expressed in mouse macrophage cells and that are inferred to have a role in innate immunity. The aim is to characterize the molecular structure and the biochemical and cellular function of these targets by using a parallel processing pipeline. The pipeline is designed to work with tens to hundreds of target gene products and comprises target selection, cloning, expression, purification, crystallization and structure determination. The structures from this pipeline will provide insights into the function of previously uncharacterized macrophage proteins and could lead to the validation of new drug targets for chronic obstructive pulmonary disease and arthritis.     

Exploration of Target Spaces in the Human Genome for Protein and Peptide Drugs

After decades of development, protein and peptide drugs have now grown into a major drug class in the marketplace. Target identification and validation are crucial for the discovery of protein and peptide drugs, and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection. However, owing to the developmental history in the pharmaceutical industry, previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs, while studies related to protein and peptide drugs are lacking. Here, we systematically explore the target spaces in the human genome specifically for protein and peptide drugs. Compared with other proteins, both successful protein and peptide drug targets have many special characteristics, and are also significantly different from those of small-molecule drugs in many aspects. Based on these features, we develop separate effective genome-wide target prediction models for protein and peptide drugs. Finally, a user-friendly web server, Predictor Of Protein and PeptIde drugs’ therapeutic Targets (POPPIT) (http://poppit.ncpsb.org.cn/), is established, which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.     

Identification of potential apicoplast associated therapeutic targets in human and animal pathogen Toxoplasma gondii ME49

Toxoplasma gondii ME49 is an obligatory intracellular apicomplexa parasite that causes toxoplasmosis in humans, domesticated and wild animals. Waterborne outbreaks of acute toxoplasmosis worldwide reinforce the transmission of Toxoplasma gondii ME49 to humans through contaminated water and may have a greater epidemiological impact than previously believed. In the quest for drug and vaccine target identification subtractive genomics involving subtraction between the host and pathogen genome has been implemented for enlisting essential pathogen specific proteins. Using this approach, our analysis on both human and Toxoplasma gondii ME49 reveals that out of 7987 protein coding sequences of the pathogen, 950 represent essential non human-homologous proteins. Subcellular localization prediction & comparative-biochemical pathway analysis of these essential proteins gives a list of apicoplast-associated proteins having unique pathogen-specific metabolic pathway. These apicoplast-associated enzymes involved in fatty acid biosynthesis pathway of Toxoplasma gondii ME49, may be used as potential drug targets, as the pathway is vital for the protozoan's survival. Structure prediction of drug target proteins was done using fold based recognition method. Screening of the functional inhibitors against these novel targets may result in discovery of novel therapeutic compounds that can be effective against Toxoplasma gondii ME49. ABBREVIATIONS: DEG - Database of Essential Gene, KEGG - Kyoto Encyclopaedia of Genes and Genomes, KAAS - KEGG Automated Annotation Server, PFP - Protein Function Prediction, COG - Cluster of Orthologous Genes.     

基于SVM 的药物靶点预测方法及其应用

目的:基于已知药物靶点和潜在药物靶点蛋白的一级结构相似性,结合SVM技术研究新的有效的药物靶点预测方法。方法:构造训练样本集,提取蛋白质序列的一级结构特征,进行数据预处理,选择最优核函数,优化参数并进行特征选择,训练最优预测模型,检验模型的预测效果。以G蛋白偶联受体家族的蛋白质为预测集,应用建立的最优分类模型对其进行潜在药物靶点挖掘。结果:基于SVM所建立的最优分类模型预测的平均准确率为81.03%。应用最优分类器对构造的G蛋白预测集进行预测,结果发现预测排位在前20的蛋白质中有多个与疾病相关。特别的,其中有两个G蛋白在治疗靶点数据库(TTD)中显示已作为临床试验的药物靶点。结论:基于SVM和蛋白质序列特征的药物靶点预测方法是有效的,应用该方法预测出的潜在药物靶点能够为发现新的药靶提供参考。     

树鼩干扰素家族的基本构成及分子特征分析

干扰素(IFN)是在"危险信号"刺激下,由细胞分泌的具有抗病毒、抗肿瘤、抑制细胞增殖和免疫调节等多重作用的糖蛋白家族,在机体免疫系统中具有重要地位。树鼩作为多种人类疾病研究模型的前景已受到广泛关注,但对其IFN家族的研究尚属空白。该研究在现有的树鼩全基因组数据基础上,应用大片段核酸序列比对、基因预测等方法,对树鼩IFN家族的基本构成和分子特征进行预测和分析。结果显示,树鼩具有I型IFN:α(5个亚型)、β、ω、κ、ε、δ;II型IFN-γ;III型IFN:IFN-λ1、λ2/3,所编码的氨基酸序列及蛋白空间结构与其它哺乳动物具有较高的相似性,但在半胱氨酸位置和N糖基化个数上具有部分差异。该研究以全基因组数据对树鼩IFN家族信息进行系统挖掘和分析,为树鼩IFN的基因克隆以及其在感染免疫学中的作用和机理研究奠定了基础。     

Tree shrew (Tupaia belangeri) as a novel laboratory disease animal model

The tree shrew (Tupaia belangen) is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents.In addition,advantages such as small size,easy breeding,and rapid reproduction make the tree shrew an ideal subject for the study of human disease.Numerous tree shrew disease models have been generated in biological and medical studies in recent years.Here we summarize current tree shrew disease models,including models of infectious diseases,cancers,depressive disorders,drug addiction,myopia,metabolic diseases,and immune-related diseases.With the success of tree shrew transgenic technology,this species will be increasingly used in biological and medical studies in the future.     

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.     

In silico identification of common putative drug targets in <Emphasis Type="Italic">Leptospira interrogans</Emphasis>

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.     

Establishment of basal cell carcinoma animal model in Chinese tree shrew (Tupaia belangeri chinensis)

Basal cell carcinoma (BCC) is the most common skin cancer worldwide,with incidence rates continuing to increase.Ultraviolet radiation is the major environmental risk factor and dysregulation of the Hedgehog (Hh) signaling pathway has been identified in most BCCs.The treatment of locally advancedand metastatic BBCs is still a challenge and requires a better animal model than the widely used rodents for drug development and testing.Chinese tree shrews (Tupaia belangeri chinensis) are closely related to primates,bearing many physiological and biochemical advantages over rodents for characterizing human diseases.Here,we successfully established a Chinese tree shrew BCC model by infecting tail skins with lentiviral SmoA1,an active form of Smoothened (Smo) used to constitutively activate the Hh signaling pathway.The pathological characteristics were verified by immunohistochemical analysis.Interestingly,BCC progress was greatly enhanced by the combined usage of lentiviral SmoA1 and shRNA targeting Chinese tree shrew p53.This work provides a useful animal model for further BCC studies and future drug discoveries.     

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.     

Creating animal models,why not use the Chinese tree shrew (Tupaia belangeri chinensis)?

The Chinese tree shrew (Tupaia belangeri chinensis),a squirrel-like and rat-sized mammal,has a wide distribution in Southeast Asia,South and Southwest China and has many unique characteristics that make it suitable for use as an experimental animal.There have been many studies using the tree shrew (Tupaia belangen) aimed at increasing our understanding of fundamental biological mechanisms and for the modeling of human diseases and therapeutic responses.The recent release of a publicly available annotated genome sequence of the Chinese tree shrew and its genome database (www.treeshrewdb.org) has offered a solid base from which it is possible to elucidate the basic biological properties and create animal models using this species.The extensive characterization of key factors and signaling pathways in the immune and nervous systems has shown that tree shrews possess both conserved and unique features relative to primates.Hitherto,the tree shrew has been successfully used to create animal models for myopia,depression,breast cancer,alcohol-induced or non-alcoholic fatty liver diseases,herpes simplex virus type 1 (HSV-1) and hepatitis C virus (HCV) infections,to name a few.The recent successful genetic manipulation of the tree shrew has opened a new avenue for the wider usage of this animal in biomedical research.In this opinion paper,I attempt to summarize the recent research advances that have used the Chinese tree shrew,with a focus on the new knowledge obtained by using the biological properties identified using the tree shrew genome,a proposal for the genome-based approach for creating animal models,and the genetic manipulation of the tree shrew.With more studies using this species and the application of cutting-edge gene editing techniques,the tree shrew will continue to be under the spot light as a viable animal model for investigating the basis of many different human diseases.     

树鼩载脂蛋白E的cDNA和蛋白质结构分析

以从树肝脏mRNA逆转录得到的Ⅰ链cDNA为模板 ,运用SMARTRACEPCR技术 ,扩增得到树载脂蛋白E(apoE)cDNA序列 ,并推导出apoE蛋白质的氨基酸序列 .利用分子生物学软件包PCGENE对氨基酸序列和二级结构进行分析和比较 .结果表明 ,树apoEcDNA序列 (作为新基因已被GenBank接收 ,登录号为AF 30 3830 )由 1138bp构成 ,其中 5′非翻译区 6 4bp ,3′非翻译区 135bp ,939bp组成一个完整开放阅读框架 ,与人apoEcDNA的同源性为 86 % .编码 313个氨基酸组成的apoE前体 ,包含 18个氨基酸构成的信号肽和 2 95个氨基酸组成的成熟蛋白 .与人apoE氨基酸序列的同源性为 78% .树apoE与人及其它种属动物apoE在氨基酸组成上相近 ,但比人apoE少4个氨基酸 ,比动脉粥样硬化易感动物家兔apoE多 2个氨基酸 .经Garnier法预测 ,树apoE蛋白二级结构与人apoE相似 ,螺旋构象 (helical) 6 9 9% ,伸展构象 (extended) 16 6 % ,转角构象 (turn)6 0 % ,无规则卷曲 (coil) 7 6 % .     

A network flow approach to predict drug targets from microarray data,disease genes and interactome network - case study on prostate cancer

Background

Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases.

Methods

We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets.

Results

We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method.

Conclusions

Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.

     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

Understanding the yeast host cell response to recombinant membrane protein production

Membrane proteins are drug targets for a wide range of diseases. Having access to appropriate samples for further research underpins the pharmaceutical industry's strategy for developing new drugs. This is typically achieved by synthesizing a protein of interest in host cells that can be cultured on a large scale, allowing the isolation of the pure protein in quantities much higher than those found in the protein's native source. Yeast is a popular host as it is a eukaryote with similar synthetic machinery to that of the native human source cells of many proteins of interest, while also being quick, easy and cheap to grow and process. Even in these cells, the production of human membrane proteins can be plagued by low functional yields; we wish to understand why. We have identified molecular mechanisms and culture parameters underpinning high yields and have consolidated our findings to engineer improved yeast host strains. By relieving the bottlenecks to recombinant membrane protein production in yeast, we aim to contribute to the drug discovery pipeline, while providing insight into translational processes.     

CASP11 – An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline

In silico prediction of a protein’s tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three ‘assisted’ protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.     

Strategic genome-scale prioritization of unique drug targets: A case study of Streptococcus gordonii

The current reach of genomics extends facilitated identification of microbial virulence factors, a primary objective for antimicrobial drug and vaccine design. Many putative proteins are yet to be identified which can act as potent drug targets. There is lack and limitation of methods which appropriately combine several omics ways for putative and new drug target identification. The study emphasizes a combined bioinformatic and theoretical method of screening unique and putative drug targets, lacking similarity with experimentally reported essential genes and drug targets. Synteny based comparison was carried out with 11 streptococci considering S. gordonii as reference genome. It revealed 534 non-homologous genes of which 334 were putative. Similarity search against host proteome, metabolic pathway annotation and subcellular localization predication identified 16 potent drug targets. This is a first attempt of several combinational approaches of similarity search with target protein structural features for screening drug targets, yielding a pipeline which can be substantiated to other human pathogens.     

RNA as a small molecule druggable target

Small molecule drugs have readily been developed against many proteins in the human proteome, but RNA has remained an elusive target for drug discovery. Increasingly, we see that RNA, and to a lesser extent DNA elements, show a persistent tertiary structure responsible for many diverse and complex cellular functions. In this digest, we have summarized recent advances in screening approaches for RNA targets and outlined the discovery of novel, drug-like small molecules against RNA targets from various classes and therapeutic areas. The link of structure, function, and small-molecule Druggability validates now for the first time that RNA can be the targets of therapeutic agents.