Thermal proteome profiling: Insights into protein modifications,associations, and functions

Tracking proteins’ biophysical characteristics on a proteome-wide scale can provide valuable information on their functions and interactions. Thermal proteome profiling (TPP) is a multiplexed quantitative proteomics approach that measures changes in protein thermal stability—a key biophysical property—across different cellular states. Developed in 2014, as a target-deconvolution assay for drugs and other small molecules, TPP has since evolved to a system-level biochemical omics technique providing insights into context-dependent changes in protein states. In this review, we summarise key advances in the experimental and data analysis pipeline that have aided this transformation and discuss the recent developments and applications of TPP.     

Arabidopsis thaliana proteomics: from proteome to genome

Proteomics has become an important approach for investigating cellular processes and network functions. Significant improvements have been made during the last few years in technologies for high-throughput proteomics, both at the level of data analysis software and mass spectrometry hardware. As proteomics technologies advance and become more widely accessible, efforts of cataloguing and quantifying full proteomes are underway to complement other genomics approaches, such as RNA and metabolite profiling. Of particular interest is the application of proteome data to improve genome annotation and to include information on post-translational protein modifications with the annotation of the corresponding gene. This type of analysis requires a paradigm shift because amino acid sequences must be assigned to peptides without relying on existing protein databases. In this review, advances and current limitations of full proteome analysis are briefly highlighted using the model plant Arabidopsis thaliana as an example. Strategies to identify peptides are also discussed on the basis of MS/MS data in a protein database-independent approach.     

热蛋白质组学分析：一种全面评估蛋白质状态的技术

热蛋白质组学分析（thermal proteome profiling,TPP）是细胞热漂移测定（cellular thermal shift assay,CETSA）与定量质谱（quantitative mass spectrometry,MS）的结合,所以也称为MS-CETSA。热蛋白质组学分析通过测量不同加热温度下细胞或细胞裂解物中可溶蛋白的含量来确定整个蛋白质组的稳定性。蛋白质可以在与药物或代谢物等小分子、核酸或其他蛋白质相互作用或在翻译后修饰时改变其热稳定性,而热蛋白质组学分析可以根据有无配体结合蛋白质的热稳定性差异来确定靶蛋白。目前热蛋白质组学分析已成功应用于识别药物的靶点和脱靶点,探究蛋白质-代谢物和蛋白质-蛋白质的相互作用。总体上,国内对这个技术的了解仍然欠缺,对此,文中对热蛋白质组学分析的原理、方法、应用以及优势与局限性进行了综述。     

基于深度学习的蛋白质建模与设计

随着蛋白质序列及结构数据的大量累积,在获得了大量描述性信息之后如何有效利用海量数据,从已有数据中高效提取信息并且应用到下游任务当中就成为了研究者亟待解决的问题。蛋白质的设计可使新蛋白的研发不再受限于实验条件,这对药物靶点预测、新药研发和材料设计等领域具有重要意义。深度学习作为一种高效的数据特征提取方法,可以通过它对蛋白质数据进行建模,进而加入先验信息对蛋白质进行设计。故此基于深度学习的蛋白质设计就成为一个具有广阔前景的研究领域。文中主要阐述基于深度学习的蛋白质序列与结构数据的建模和设计方法。详述该方法的策略、原理、适用范围、应用实例。讨论了深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Practical points in urinary proteomics

During the proteomic era, one of the most rapidly growing areas in biomedical research is biomarker discovery, particularly using proteomic technologies. Urinary proteomics has become one of the most attractive subdisciplines in clinical proteomics, as the urine is an ideal source for the discovery of noninvasive biomarkers for human diseases. However, there are several barriers to the success of the field and urinary proteome analysis is not a simple task because the urine has low protein concentration, high levels of salts or other interfering compounds, and more importantly, high degree of variations (both intra-individual and inter-individual variabilities). This article provides step-by-step practical points to perform urinary proteome analysis, covering detailed information for study design, sample collection, sample storage, sample preparation, proteomic analysis, and data interpretation. The discussion herein should stimulate further discussion and refinement to develop guidelines and standardizations for urinary proteome study.     

Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs

Truly comprehensive proteome analysis is highly desirable in systems biology and biomarker discovery efforts. But complete proteome characterization has been hindered by the dynamic range and detection sensitivity of experimental designs, which are not adequate to the very wide range of protein abundances. Experimental designs for comprehensive analytical efforts involve separation followed by mass spectrometry-based identification of digested proteins. Because results are generally reported as a collection of identifications with no information on the fraction of the proteome that was missed, they are difficult to evaluate and potentially misleading. Here we address this problem by taking a holistic view of the experimental design and using computer simulations to estimate the success rate for any given experiment. Our approach demonstrates that simple changes in typical experimental designs can enhance the success rate of proteome analysis by five- to tenfold.     

Exploring the diversity of plant proteome

The tremendous functional, spatial, and temporal diversity of the plant proteome is regulated by multiple factors that continuously modify protein abundance, modifications, interactions, localization, and activity to meet the dynamic needs of plants. Dissecting the proteome complexity and its underlying genetic variation is attracting increasing research attention. Mass spectrometry (MS)-based proteomics has become a powerful approach in the global study of protein functions and their relationships on a systems level. Here, we review recent breakthroughs and strategies adopted to unravel the diversity of the proteome, with a specific focus on the methods used to analyze posttranslational modifications (PTMs), protein localization, and the organization of proteins into functional modules. We also consider PTM crosstalk and multiple PTMs temporally regulating the life cycle of proteins. Finally, we discuss recent quantitative studies using MS to measure protein turnover rates and examine future directions in the study of the plant proteome.     

Anti-infectives: can cellular screening deliver?

In an era of emerging and reemerging infectious diseases, and increasing multidrug resistance, the need to identify novel therapy is imperative. Unfortunately, the recent shift of the drug discovery paradigm from cellular screening to target-based approaches has not delivered the anticipated benefits. A recent renaissance of the traditional cell-based approach, on the other hand, has yielded several clinical candidates. Three successful examples are illustrated in this review, namely spiroindolone, thiazolidinone, and diarylquinoline for the treatment of malaria, hepatitis C virus, and tuberculosis, respectively. We describe in detail their identification, mechanism of action (MoA), and common features in the chemical structures. The challenges of the cell-based approach for anti-infective drug discovery are also discussed. We propose a shift from standard libraries to synthetic natural-product-like compound collections to improve the success of phenotypic lead finding and to facilitate the validation of hits.     

Development of efficient protein extraction methods for shotgun proteome analysis of formalin-fixed tissues

There are vast archives of formalin-fixed tissues spanning many conceivable conditions such as different diseases, time courses, and different treatment and allowing acquisition of the necessary numbers of samples to carry out biomarker discovery study. However, the conventional protein analysis approach is not applicable for the analysis of proteins in the formalin-fixed tissue because the formalin fixation process resulted in the cross-linking of proteins, and thus, intact proteins cannot be efficiently extracted. In this study, several protocols were investigated to extract proteins from formalin-fixed mouse liver tissue for shotgun proteome analysis. It was found that incubation of tissue in a lysis buffer containing 6 M guanidine hydrochloride at high temperature led to the highest protein yield and the largest number of proteins identified. The peptides and proteins identified from formalin-fixed tissue were first comprehensively compared with those identified from frozen-fresh tissue. It was found that a majority of peptides identified from fixed tissue were unmodified and proteome coverage for the analysis of fixed tissue was not obviously compromised by the formalin fixation process. Valuable proteome information could be obtained by shotgun proteome analysis of formalin-fixed tissue, which presents a new approach for disease biomarker discovery.     

Applications of cell-based phage display panning to proteomic analysis

In the post-genomic era, proteomics together with genomic tools have led to powerful new strategies in basic and clinical research. These combined “omics” technologies are being integrated into the drug target discovery process. Unlike the genome, the proteome is a highly dynamic entity that requires techniques capable of analyzing on selected populations of proteins in specific biological conditions that reflect the proteins’ functional characteristics. Antibodies have become one of the most important reagents for the analysis of selected populations of proteins, and the application of phage-display antibody libraries to high-throughput antibody generation against large numbers of various antigens provides a tool for proteome-wide protein expression analysis. In this review, we will discuss the utility of phage-display antibodies in proteomics applications, specifically for the discovery of novel disease markers and therapeutic targets.     

Current trends in computational inference from mass spectrometry-based proteomics

Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.     

Thermal proteome profiling in bacteria: probing protein state in vivo

Increasing antibiotic resistance urges for new technologies for studying microbes and antimicrobial mechanism of action. We adapted thermal proteome profiling (TPP) to probe the thermostability of Escherichia coli proteins in vivo. E. coli had a more thermostable proteome than human cells, with protein thermostability depending on subcellular location—forming a high‐to‐low gradient from the cell surface to the cytoplasm. While subunits of protein complexes residing in one compartment melted similarly, protein complexes spanning compartments often had their subunits melting in a location‐wise manner. Monitoring the E. coli meltome and proteome at different growth phases captured changes in metabolism. Cells lacking TolC, a component of multiple efflux pumps, exhibited major physiological changes, including differential thermostability and levels of its interaction partners, signaling cascades, and periplasmic quality control. Finally, we combined in vitro and in vivo TPP to identify targets of known antimicrobial drugs and to map their downstream effects. In conclusion, we demonstrate that TPP can be used in bacteria to probe protein complex architecture, metabolic pathways, and intracellular drug target engagement.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

药物蛋白质组学与药物发现

21世纪,科学家面临着从基因组到蛋白质组的转变,蛋白质组学是基因组和药物发现的效率。药物蛋白质组学研究不仅有助于发现治疗的可能靶点,也将明显提高药物发现的效率。药物蛋白质组学的研究内容,在临床前包括发现新的治疗靶点和发现针对所有靶点的全部化合物,在临床研究方面应包括药物作用的特异蛋白作为诊断和治疗的标志,或以蛋白质谱的差异来分类者。本文主要综述了蛋白质组学在药物靶点的发现和确认,以有药物发现过程中最有关的技术物研究进展。     

The progress made in determining the Mycobacterium tuberculosis structural proteome

Mycobacterium tuberculosis is a highly infectious pathogen that is still responsible for millions of deaths annually. Effectively treating this disease typically requires a course of antibiotics, most of which were developed decades ago. These drugs are, however, not effective against persistent tubercle bacilli and the emergence of drug-resistant stains threatens to make many of them obsolete. The identification of new drug targets, allowing the development of new potential drugs, is therefore imperative. Both proteomics and structural biology have important roles to play in this process, the former as a means of identifying promising drug targets and the latter allowing understanding of protein function and protein-drug interactions at atomic resolution. The determination of M. tuberculosis protein structures has been a goal of the scientific community for the last decade, who have aimed to supply a large amount of structural data that can be used in structure-based approaches for drug discovery and design. Only since the genome sequence of M. tuberculosis has been available has the determination of large numbers of tuberculosis protein structures been possible. Currently, the molecular structures of 8.5% of all the pathogen's protein-encoding ORFs have been determined. In this review, we look at the progress made in determining the M. tuberculosis structural proteome and the impact this has had on the development of potential new drugs, as well as the discovery of the function of crucial mycobaterial proteins.     

Proteomics in the post-genome age.

The genome sequencing effort has helped spawn the burgeoning field of proteomics. This review article examines state-of-the-art proteomics methods that are helping change the discovery paradigm in a variety of biological disciplines and, in particular, protein biochemistry. The review discusses both classical and novel methods to perform high-throughput qualitative and quantitative "global" as well as targeted proteome analysis of complex biological systems. From a drug discovery standpoint, the synergy between genomics and proteomics will help elucidate disease mechanisms, identify novel drug targets, and identify surrogate biomarkers that could be used to conduct clinical trials.     

Ligand-Target Prediction by Structural Network Biology Using nAnnoLyze

Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development.     

Biomarker discovery: proteome fractionation and separation in biological samples.

Proteomics, analogous with genomics, is the analysis of the protein complement present in a cell, organ, or organism at any given time. While the genome provides information about the theoretical status of the cellular proteins, the proteome describes the actual content, which ultimately determines the phenotype. The broad application of proteomic technologies in basic science and clinical medicine has the potential to accelerate our understanding of the molecular mechanisms underlying disease and may facilitate the discovery of new drug targets and diagnostic disease markers. Proteomics is a rapidly developing and changing scientific discipline, and the last 5 yr have seen major advances in the underlying techniques as well as expansion into new applications. Core technologies for the separation of proteins and/or peptides are one- and two-dimensional gel electrophoresis and one- and two-dimensional liquid chromatography, and these are coupled almost exclusively with mass spectrometry. Proteomic studies have shown that the most effective analysis of even simple biological samples requires subfractionation and/or enrichment before protein identification by mass spectrometry. Selection of the appropriate technology or combination of technologies to match the biological questions is essential for maximum coverage of the selected subproteome and to ensure both the full interpretation and the downstream utility of the data. In this review, we describe the current technologies for proteome fractionation and separation of biological samples, based on our lab workflow for biomarker discovery and validation.     

泛素、泛素链和蛋白质泛素化研究进展

蛋白质泛素化是以泛素单体和泛素链作为信号分子,共价修饰细胞内其他蛋白质的一种翻译后修饰形式。不同蛋白质底物、同一底物的不同氨基酸修饰位点以及同一位点上泛素链连接方式的不同均可导致细胞效应的差异。蛋白质泛素化在真核细胞内广泛存在,除了介导蛋白质的26S蛋白酶体降解途径之外,还广泛参与了基因转录、蛋白质翻译、信号传导、细胞周期控制以及生长发育等几乎所有的生命活动过程。泛素链的形成及其修饰过程的任何失调均可导致生物体内环境的紊乱,从而产生严重的疾病。文中结合实验室研究,综述了泛素的发现历史、基因特点、晶体结构,特别是泛素链的组装过程、结构、功能以及与人类相关疾病关系的新进展,可为这些疾病的治疗靶点和药物靶标的研究提供思路。