The proteomic response of Mycobacterium smegmatis to anti-tuberculosis drugs suggests targeted pathways

Mycobacterium smegmatis is a fast-growing model mycobacterial system that shares many features with the pathogenic Mycobacterium tuberculosis while allowing practical proteomics analysis. With the use of shotgun-style mass spectrometry, we provide a large-scale analysis of the M. smegmatis proteomic response to the anti-tuberculosis (TB) drugs isoniazid, ethambutol, and 5-chloropyrazinamide and elucidate the drugs' systematic effects on mycobacterial proteins. A total of 2550 proteins were identified with approximately 5% false-positive identification rate across 60 experiments, representing approximately 40% of the M. smegmatis proteome ( approximately 6500 proteins). Protein differential expression levels were estimated from the shotgun proteomics data, and 485 proteins showing altered expression levels in response to drugs were identified at a 99% confidence level. Proteomic comparison of anti-TB drug responses shows that translation, cell cycle control, and energy production are down-regulated in all three drug treatments. In contrast, systems related to the drugs' targets, such as lipid, amino acid, and nucleotide metabolism, show specific protein expression changes associated with a particular drug treatment. We identify proteins involved in target pathways for the three drugs and infer putative targets for 5-chloropyrazinamide.     

Thermodynamic analysis of protein-ligand interactions in complex biological mixtures using a shotgun proteomics approach

Shotgun proteomics protocols are widely used for the identification and/or quantitation of proteins in complex biological samples. Described here is a shotgun proteomics protocol that can be used to identify the protein targets of biologically relevant ligands in complex protein mixtures. The protocol combines a quantitative proteomics platform with a covalent modification strategy, termed Stability of Proteins from Rates of Oxidation (SPROX), which utilizes the denaturant dependence of hydrogen peroxide-mediated oxidation of methionine side chains in proteins to assess the thermodynamic properties of proteins and protein-ligand complexes. The quantitative proteomics platform involves the use of isobaric mass tags and a methionine-containing peptide enhancement strategy. The protocol is evaluated in a ligand binding experiment designed to identify the proteins in a yeast cell lysate that bind the well-known enzyme cofactor, β-nicotinamide adenine dinucleotide (NAD+). The protocol is also used to investigate the protein targets of resveratrol, a biologically active ligand with less well-understood protein targets. A known protein target of resveratrol, cytosolic aldehyde dehydrogenase, was identified in addition to six other potential new proteins targets including four that are associated with the protein translation machinery, which has previously been implicated as a target of resveratrol.     

Experimental evaluation of protein identification by an LC/MALDI/on-target digestion approach

Tryptic digestion of proteins continues to be a workhorse of proteomics. Traditional tryptic digestion requires several hours to generate an adequate protein digest. A number of enhanced accelerated digestion protocols have been developed in recent years. Nonetheless, a need still exists for new digestion strategies that meet the demands of proteomics for high-throughput and rapid detection and identification of proteins. We performed an evaluation of direct tryptic digestion of proteins on a MALDI target plate and the potential for integrating RP HPLC separation of protein with on-target tryptic digestion in order to achieve a rapid and effective identification of proteins in complex biological samples. To this end, we used a Tempo HPLC/MALDI target plate deposition hybrid instrument (ABI). The technique was evaluated using a number of soluble and membrane proteins and an MRC5 cell lysate. We demonstrated that direct deposition of proteins on a MALDI target plate after reverse-phase HPLC separation and subsequent tryptic digestion of the proteins on the target followed by MALDI TOF/TOF analysis provided substantial data (intact protein mass, peptide mass and peptide fragment mass) that allowed a rapid and unambiguous identification of proteins. The rapid protein separation and direct deposition of fractions on a MALDI target plate provided by the RP HPLC combined with off-line interfacing with the MALDI MS is a unique platform for rapid protein identification with improved sequence coverage. This simple and robust approach significantly reduces the sample handling and potential loss in large-scale proteomics experiments. This approach allows combination of peptide mass fingerprinting (PMF), MS/MS peptide fragment fingerprinting (PPF) and whole protein MS for both protein identification and structural analysis of proteins.     

Chemical proteomics and its impact on the drug discovery process

Despite the rapid growth of postgenomic data and fast-paced technology advancement, drug discovery is still a lengthy and difficult process. More effective drug design requires a better understanding of the interaction between drug candidates and their targets/off-targets in various situations. The ability of chemical proteomics to integrate a multiplicity of disciplines enables the direct analysis of protein activities on a proteome-wide scale, which has enormous potential to facilitate drug target elucidation and lead drug verification. Over recent years, chemical proteomics has experienced rapid growth and provided a valuable method for drug target identification and inhibitor discovery. This review introduces basic concepts and technologies of different popular chemical proteomic approaches. It also covers the essential features and recent advances of each approach while underscoring their potentials in drug discovery and development.     

Inhibition of Leishmania donovani promastigote DNA topoisomerase I and human monocyte DNA topoisomerases I and II by antimonial drugs and classical antitopoisomerase agents

We have compared the inhibitor sensitivities of DNA topoisomerase I (TOPI) from Leishmania donovani promastigotes and TOPs I and II of human monocytes using pentavalent and trivalent antimonials (SbV, SbIII) and classical TOP inhibitors. Bis-benzimidazoles (Hoechst-33258 and -33342) were potent inhibitors of both parasite and human TOPI, but Hoechst-33342 was markedly less cytotoxic to promastigotes than to monocytes in vitro. Leishmania donovani was also considerably less sensitive than monocytes to camptothecin, both at enzyme and cellular levels. Sodium stibogluconate (SSG) was the only antimonial to inhibit TOPI, exhibiting a significant (P < 0.05) 3-fold greater potency against the L. donovani enzyme but showed low cytotoxicities against intact promastigotes. The SbV meglumine antimoniate failed to inhibit TOPI and showed negligible cytotoxicities, whereas SbIII drugs were lethal to parasites and monocytes yet poor inhibitors of TOPI. Monocyte TOPII was inhibited by bis-benzimidazoles and insensitive to antimonials and camptothecin. The disparity between the high leishmanicidal activity and low anti-TOPI potency of SbIII indicates that in vivo targeting of L. donovani TOPI by the reductive pathway of antimonial activation is improbable. Nevertheless, the potent direct inhibition of TOPI by SSG and the differential interactions of camptothecin with L. donovani and human TOPI support the possibility of developing parasite-specific derivatives.     

Applicability of Chromatographic Co‐Elution for Antibiotic Target Identification

Identification of the molecular target is a crucial step in evaluating novel antibiotics. To support target identification, a label‐free method based on chromatographic co‐elution has previously been developed. Target identification by chromatographic coelution (TICC) exploits the alteration of the elution profile of target‐bound drug versus free drug in ion exchange (IEX) chromatography to identify potential target proteins from elution fractions. The applicability of TICC for antibiotic research is investigated by evaluating which proteins, that is, putative targets, can be monitored in Bacillus subtilis. Coelution of components of known protein complexes provides a read‐out for how well the native state of proteins is conserved during chromatography. Rifampicin, which targets RNA polymerase, is used in a proof‐of‐concept study.     

Protein interaction network analysis—Approach for potential drug target identification in Mycobacterium tuberculosis

In host-parasite diseases like tuberculosis, non-homologous proteins (enzymes) as drug target are first preference. Most potent drug target can be identified among large number of non-homologous protein through protein interaction network analysis. In this study, the entire promising dimension has been explored for identification of potential drug target. A comparative metabolic pathway analysis of the host Homo sapiens and the pathogen M. tuberculosis H37Rv has been performed with three level of analysis. In first level, the unique metabolic pathways of M. tuberculosis have been identified through its comparative study with H. sapiens and identification of non-homologous proteins has been done through BLAST similarity search. In second level, choke-point analysis has been performed with identified non-homologous proteins of metabolic pathways. In third level, two type of analysis have been performed through protein interaction network. First analysis has been done to find out the most potential metabolic functional associations among all identified choke point proteins whereas second analysis has been performed to find out the functional association of high metabolic interacting proteins to pathogenesis causing proteins. Most interactive metabolic proteins which have highest number of functional association with pathogenesis causing proteins have been considered as potential drug target. A list of 18 potential drug targets has been proposed which are various stages of progress at the TBSGC and proposed drug targets are also studied for other pathogenic strains.As a case study, we have built a homology model of identified drug targets histidinol-phosphate aminotransferase (HisC1) using MODELLER software and various information have been generated through molecular dynamics which will be useful in wetlab structure determination. The generated model could be further explored for insilico docking studies with suitable inhibitors.     

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.     

基于生物信息学方法发现潜在药物靶标

药物靶点通常是在代谢或信号通路中与特定疾病或病理状态有关的关键分子.通过绑定到特定活动区域抑制这个关键分子进行药物设计.确定特定疾病有关的靶标分子是现代新药开发的基础.在药物靶标发现的过程中,生物信息学方法发挥了不可替代的重要的作用,尤其适用于大规模多组学数据的分析.目前,已涌现了许多与疾病相关的数据库资源,基于生物网络特征、多基因芯片、蛋白质组、代谢组数据等建立了多种生物信息学方法发现潜在的药物靶标,并预测靶标可药性和药物副作用.     

Chemical proteomics and its application to drug discovery

The completion of the human genome sequencing project has provided a flood of new information that is likely to change the way scientists approach the study of complex biological systems. A major challenge lies in translating this information into new and better ways to treat human disease. The multidisciplinary science of chemical proteomics can be used to distill this flood of new information. This approach makes use of synthetic small molecules that can be used to covalently modify a set of related enzymes and subsequently allow their purification and/or identification as valid drug targets. Furthermore, such methods enable rapid biochemical analysis and small-molecule screening of targets thereby accelerating the often difficult process of target validation and drug discovery.     

Properties of Protein Drug Target Classes

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.     

Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors

We describe a chemical proteomics approach to profile the interaction of small molecules with hundreds of endogenously expressed protein kinases and purine-binding proteins. This subproteome is captured by immobilized nonselective kinase inhibitors (kinobeads), and the bound proteins are quantified in parallel by mass spectrometry using isobaric tags for relative and absolute quantification (iTRAQ). By measuring the competition with the affinity matrix, we assess the binding of drugs to their targets in cell lysates and in cells. By mapping drug-induced changes in the phosphorylation state of the captured proteome, we also analyze signaling pathways downstream of target kinases. Quantitative profiling of the drugs imatinib (Gleevec), dasatinib (Sprycel) and bosutinib in K562 cells confirms known targets including ABL and SRC family kinases and identifies the receptor tyrosine kinase DDR1 and the oxidoreductase NQO2 as novel targets of imatinib. The data suggest that our approach is a valuable tool for drug discovery.     

Screening for ligands using a generic and high-throughput light-scattering-based assay

Rapid identification of small molecules that interact with protein targets using a generic screening method greatly facilitates the development of therapeutic agents. The authors describe a novel method for performing homogeneous biophysical assays in a high-throughput format. The use of light scattering as a method to evaluate protein stability during thermal denaturation in a 384-well format yields a robust assay with a low frequency of false positives. This novel method leads to the identification of interacting small molecules without the addition of extraneous fluorescent probes. The analysis and interpretation of data is rapid, with sensitivity for protein stability comparable to differential scanning calorimetry. The authors propose potential uses in drug discovery, structural genomics, and functional genomics as a method to evaluate small-molecule interactions, identify natural cofactors that stabilize target proteins, and identify natural substrates and products for previously uncharacterized protein targets.     

A chemical proteomics approach for the identification of accessible antigens expressed in human kidney cancer

A promising avenue toward the development of more selective anticancer drugs consists in the targeted delivery of bioactive molecules to the tumor environment by means of binding molecules specific to tumor-associated markers. We have used a chemical proteomics approach based on the ex vivo perfusion and biotinylation of accessible structures within surgically resected human kidneys with tumor to gain information about accessible and abundant antigens that are overexpressed in human cancer. This procedure led to the selective labeling with biotin of vascular structures. Biotinylated proteins were purified on streptavidin resin and identified using mass spectrometric methodologies, revealing 637 proteins, 184 of which were only found in tumor specimens and 223 of which were only found in portions of normal kidneys. Immunohistochemical and PCR analysis confirmed that several of the putative cancer antigens identified in this study are indeed preferentially expressed in tumors. In conclusion, we have developed a methodology that allows the identification of accessible biomarkers in human tissues. The tumor-associated antigens identified in this study may be suitable targets for antibody-based anticancer therapies. The experimental approach described here should be applicable to other surgical specimens and to other pathologies as well as to the study of basic physiological and immunological processes.     

Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule

Ligand-protein docking has been developed and used in facilitating new drug discoveries. In this approach, docking single or multiple small molecules to a receptor site is attempted to find putative ligands. A number of studies have shown that docking algorithms are capable of finding ligands and binding conformations at a receptor site close to experimentally determined structures. These algorithms are expected to be equally applicable to the identification of multiple proteins to which a small molecule can bind or weakly bind. We introduce a ligand-protein inverse-docking approach for finding potential protein targets of a small molecule by the computer-automated docking search of a protein cavity database. This database is developed from protein structures in the Protein Data Bank (PDB). Docking is conducted with a procedure involving multiple-conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Scoring is conducted by the evaluation of molecular-mechanics energy and, when applicable, by the further analysis of binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Testing results on two therapeutic agents, 4H-tamoxifen and vitamin E, showed that 50% of the computer-identified potential protein targets were implicated or confirmed by experiments. The application of this approach may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity of a drug or drug candidate. Proteins 2001;43:217-226.     

Accessing target information by virtual parallel screening—The impact on natural product research

Secondary metabolites exhibit an astonishing multitude of functionalities and enormous chemical diversity and these qualities are responsible for their favoured selection as drug leads. The complex process of finding natural products’ bioactivities is largely based on trial and error, and is therefore risky, time- and cost-intensive. In recent decades, computer-assisted techniques have emerged as promising tools to manage the huge amount of available structural data of macromolecular targets and compounds annotated to specific functions, and to extract knowledge from these data for the prediction of new events. The novel concept of virtual parallel screening aims to access a pharmacological profile for each compound screened using an array of macromolecular targets. Providing putative ligand–target interactions, this in silico multitarget application meets the requirements for natural product research in a complementary way. It enables (i) a fast identification of potential targets (target fishing), (ii) insight into a putative molecular mechanism, and (iii) an estimation of the bioactivity profile which allows for prioritizing experimental investigations. The first application examples in natural product research are described.     

Comprehensive proteomic analysis of membrane proteins in Toxoplasma gondii

Toxoplasma gondii (T. gondii) is an obligate intracellular protozoan parasite that is an important human and animal pathogen. Experimental information on T. gondii membrane proteins is limited, and the majority of gene predictions with predicted transmembrane motifs are of unknown function. A systematic analysis of the membrane proteome of T. gondii is important not only for understanding this parasite's invasion mechanism(s), but also for the discovery of potential drug targets and new preventative and therapeutic strategies. Here we report a comprehensive analysis of the membrane proteome of T. gondii, employing three proteomics strategies: one-dimensional gel liquid chromatography-tandem MS analysis (one-dimensional gel electrophoresis LC-MS/MS), biotin labeling in conjunction with one-dimensional gel LC-MS/MS analysis, and a novel strategy that combines three-layer "sandwich" gel electrophoresis with multidimensional protein identification technology. A total of 2241 T. gondii proteins with at least one predicted transmembrane segment were identified and grouped into 841 sequentially nonredundant protein clusters, which account for 21.8% of the predicted transmembrane protein clusters in the T. gondii genome. A large portion (42%) of the identified T. gondii membrane proteins are hypothetical proteins. Furthermore, many of the membrane proteins validated by mass spectrometry are unique to T. gondii or to the Apicomplexa, providing a set of gene predictions ripe for experimental investigation, and potentially suitable targets for the development of therapeutic strategies.     

Identification of Phosphoribosyl-AMP cyclohydrolase,as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis

Brucella melitensis is a pathogenic Gram-negative bacterium which is known for causing zoonotic diseases (Brucellosis). The organism is highly contagious and has been reported to be used as bioterrorism agent against humans. Several antibiotics and vaccines have been developed but these antibiotics have exhibited the sign of antibiotic resistance or ineffective at lower concentrations, which imposes an urgent need to identify the novel drugs/drug targets against this organism. In this work, metabolic pathways analysis has been performed with different filters such as non-homology with humans, essentially of genes and choke point analysis, leading to identification of novel drug targets. A total of 18 potential drug target proteins were filtered out and used to develop the high confidence protein–protein interaction network The Phosphoribosyl-AMP cyclohydrolase (HisI) protein has been identified as potential drug target on the basis of topological parameters. Further, a homology model of (HisI) protein has been developed using Modeller with multiple template (1W6Q (48%), 1ZPS (55%), and 2ZKN (48%)) approach and validated using PROCHECK and Verify3D. The virtual high throughput screening (vHTS) using DockBlaster tool has been performed against 16,11,889 clean fragments from ZINC database. Top 500 molecules from DockBlaster were docked using Vina. The docking analysis resulted in ZINC04880153 showing the lowest binding energy (?9.1 kcal/mol) with the drug target. The molecular dynamics study of the complex HisI-ZINC04880153 was conducted to analyze the stability and fluctuation of ligand within the binding pocket of HisI. The identified ligand could be analyzed in the wet-lab based experiments for future drug discovery.