StableIsotope Labeling with Amino Acids in Cell Culture (SILAC)-based strategy for proteome-wide thermodynamic analysis of protein-ligand binding interactions

Regulation of gene expression through protein-metabolite interactions

Organisms have to adapt to changes in their environment. Cellular adaptation requires sensing, signalling and ultimately the activation of cellular programs. Metabolites are environmental signals that are sensed by proteins, such as metabolic enzymes, protein kinases and nuclear receptors. Recent studies have discovered novel metabolite sensors that function as gene regulatory proteins such as chromatin associated factors or RNA binding proteins. Due to their function in regulating gene expression, metabolite-induced allosteric control of these proteins facilitates a crosstalk between metabolism and gene expression. Here we discuss the direct control of gene regulatory processes by metabolites and recent progresses that expand our abilities to systematically characterize metabolite-protein interaction networks. Obtaining a profound map of such networks is of great interest for aiding metabolic disease treatment and drug target identification.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Stability-Based Proteomics for Investigation of Structured RNA-Protein Interactions

RNA-protein interactions are essential to RNA function throughout biology. Identifying the protein interactions associated with a specific RNA, however, is currently hindered by the need for RNA labeling or costly tiling-based approaches. Conventional strategies, which commonly rely on affinity pull-down approaches, are also skewed to the detection of high affinity interactions and frequently miss weaker interactions that may be biologically important. Reported here is the first adaptation of stability-based mass spectrometry methods for the global analysis of RNA-protein interactions. The stability of proteins from rates of oxidation (SPROX) and thermal protein profiling (TPP) methods are used to identify the protein targets of three RNA ligands, the MALAT1 triple helix (TH), a viral stem loop (SL), and an unstructured RNA (PolyU), in LNCaP nuclear lysate. The 315 protein hits with RNA-induced conformational and stability changes detected by TPP and/or SPROX were enriched in previously annotated RNA-binding proteins and included new proteins for hypothesis generation. Also demonstrated are the orthogonality of the SPROX and TPP approaches and the utility of the domain-specific information available with SPROX. This work establishes a novel platform for the global discovery and interrogation of RNA-protein interactions that is generalizable to numerous biological contexts and RNA targets.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Datasets Built on FragPipe

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage the fast, sensitive, and accurate search and label-free quantification algorithms in FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a new data merging scheme (from ions to cut-sites) and a protein-centric multiple hypothesis correction scheme, collectively enabling processed LiP-MS datasets to be more robust and less redundant. These improvements substantially strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. As a final feature, FLiPPR facilitates the collection of structural metadata to identify correlations between experiments and structural features. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Comparative Analysis of Protein Folding Stability-Based Profiling Methods for Characterization of Biological Phenotypes

Recently, a new suite of mass spectrometry-based proteomic methods has been developed that enables evaluation of protein folding stability on the proteomic scale. These methods utilize chemical and thermal denaturation approaches (SPROX and TPP, respectively) as well as proteolysis strategies (DARTS, LiP, and PP) to assess protein folding stability. The analytical capabilities of these technique have been well-established for protein target discovery applications. However, less is known about the relative advantages and disadvantages of using these different strategies to characterize biological phenotypes. Reported here is a comparative study of SPROX, TPP, LiP, and conventional protein expression level measurements using both a mouse model of aging and a mammalian cell culture model of breast cancer. Analyses on proteins in brain tissue cell lysates derived from 1- and 18-month-old mice (n = 4-5 at each time point) and on proteins in cell lysates derived from the MCF-7 and MCF-10A cell lines revealed a majority of the differentially stabilized protein hits in each phenotype analysis had unchanged expression levels. In both phenotype analyses, TPP generated the largest number and fraction of differentially stabilized protein hits. Only a quarter of all the protein hits identified in each phenotype analysis had a differential stability that was detected using multiple techniques. This work also reports the first peptide-level analysis of TPP data, which was required for the correct interpretation of the phenotype analyses performed here. Studies on selected protein stability hits also uncovered phenotype-related functional changes.

Challenges and Perspectives in Target Identification and Mechanism Illustration for Chinese Medicine

Chinese medicine (CM) is an important resource for human life understanding and discovery of drugs. However, due to the unclear pharmacological mechanism caused by unclear target, research and international promotion of many active components have made little progress in the past decades of years. CM is mainly composed of multi-ingredients with multi-targets. The identification of targets of multiple active components and the weight analysis of multiple targets in a specific pathological environment, that is, the determination of the most important target is the main obstacle to the mechanism clarification and thus hinders its internationalization. In this review, the main approach to target identification and network pharmacology were summarized. And BIBm (Bayesian inference modeling), a powerful method for drug target identification and key pathway determination was introduced. We aim to provide a new scientific basis and ideas for the development and international promotion of new drugs based on CM.

The Knowns and Unknowns in Protein-Metabolite Interactions

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions.

Discovery of the Xenon-Protein Interactome Using Large-Scale Measurements of Protein Folding and Stability

The intermolecular interactions of noble gases in biological systems are associated with numerous biochemical responses, including apoptosis, inflammation, anesthesia, analgesia, and neuroprotection. The molecular modes of action underlying these responses are largely unknown. This is in large part due to the limited experimental techniques to study protein-gas interactions. The few techniques that are amenable to such studies are relatively low-throughput and require large amounts of purified proteins. Thus, they do not enable the large-scale analyses that are useful for protein target discovery. Here, we report the application of stability of proteins from rates of oxidation (SPROX) and limited proteolysis (LiP) methodologies to detect protein-xenon interactions on the proteomic scale using protein folding stability measurements. Over 5000 methionine-containing peptides and over 5000 semi-tryptic peptides, mapping to ∼1500 and ∼950 proteins, respectively, in the yeast proteome, were assayed for Xe-interacting activity using the SPROX and LiP techniques. The SPROX and LiP analyses identified 31 and 60 Xe-interacting proteins, respectively, none of which were previously known to bind Xe. A bioinformatics analysis of the proteomic results revealed that these Xe-interacting proteins were enriched in those involved in ATP-driven processes. A fraction of the protein targets that were identified are tied to previously established modes of action related to xenon's anesthetic and organoprotective properties. These results enrich our knowledge and understanding of biologically relevant xenon interactions. The sample preparation protocols and analytical methodologies developed here for xenon are also generally applicable to the discovery of a wide range of other protein-gas interactions in complex biological mixtures, such as cell lysates.

An update of label-free protein target identification methods for natural active products

Natural active products (NAPs) are derived from chemical substances found in nature that have biological activity and medicinal potential. Screening and revealing the protein targets of NAPs is an indispensable link in the pharmacological and toxicological understanding of NAPs. Proteins are the main factors executing cell functions, and cells rely on the function of proteins to complete various activities in the life cycle. The important mechanism of action of drugs is to regulate cell biological activities by interacting with proteins and other macromolecules. At present, the classic way to screen protein targets is based on the molecular label tracing method, which has a long cycle and changes the molecular structure and pharmacological effects of NAPs. Due to the shortcomings of molecular labelling methods, in recent years, scientists have tried to develop a variety of label-free protein target identification methods for NAPs and have made a certain amount of progress. This article reviews the current protein target identification methods for NAPs with the aim of providing a reference for research on NAP protein targets.

Chemoproteomic-enabled characterization of small GTPase Rab1a as a target of an N-arylbenzimidazole ligand's rescue of Parkinson's-associated cell toxicity

The development of phenotypic models of Parkinson's disease (PD) has enabled screening and identification of phenotypically active small molecules that restore complex biological pathways affected by PD toxicity.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

Hydroxyl Radical Protein Footprinting: A Mass Spectrometry-Based Structural Method for Studying the Higher Order Structure of Proteins

Hydroxyl radical protein footprinting (HRPF) coupled to mass spectrometry has been successfully used to investigate a plethora of protein-related questions. The method, which utilizes hydroxyl radicals to oxidatively modify solvent-accessible amino acids, can inform on protein interaction sites and regions of conformational change. Hydroxyl radical-based footprinting was originally developed to study nucleic acids, but coupling the method with mass spectrometry has enabled the study of proteins. The method has undergone several advancements since its inception that have increased its utility for more varied applications such as protein folding and the study of biotherapeutics. In addition, recent innovations have led to the study of increasingly complex systems including cell lysates and intact cells. Technological advances have also increased throughput and allowed for better control of experimental conditions. In this review, we provide a brief history of the field of HRPF and detail recent innovations and applications in the field.

A Comparison of Two Stability Proteomics Methods for Drug Target Identification in OnePot 2D Format

Stability proteomics techniques that do not require drug modifications have emerged as an attractive alternative to affinity purification methods in drug target engagement studies. Two representative techniques include the chemical-denaturation-based SPROX (Stability of Proteins from Rates of Oxidation), which utilizes peptide-level quantification and thermal-denaturation-based TPP (Thermal Proteome Profiling), which utilizes protein-level quantification. Recently, the "OnePot" strategy was adapted for both SPROX and TPP to increase the throughput. When combined with the 2D setup which measures both the denaturation and the drug dose dimensions, the OnePot 2D format offers improved analysis specificity with higher resource efficiency. However, a systematic evaluation of the OnePot 2D format and a comparison between SPROX and TPP are still lacking. Here, we performed SPROX and TPP to identify protein targets of a well-studied pan-kinase inhibitor staurosporine with K562 lysate, in curve-fitting and OnePot 2D formats. We found that the OnePot 2D format provided ∼10× throughput, achieved ∼1.6× protein coverage and involves more straightforward data analysis. We also compared SPROX with the current "gold-standard" stability proteomics technique TPP in the OnePot 2D format. The protein coverage of TPP is ∼1.5 fold of SPROX; however, SPROX offers protein domain-level information, identifies comparable numbers of kinase hits, has higher signal (R value), and requires ∼3× less MS time. Unique SPROX hits encompass higher-molecular-weight proteins, compared to the unique TPP hits, and include atypical kinases. We also discuss hit stratification and prioritization strategies to promote the efficiency of hit followup.

Application of omics- and multi-omics-based techniques for natural product target discovery

Natural products continue to be an unparalleled source of pharmacologically active lead compounds because of their unprecedented structures and unique biological activities. Natural product target discovery is a vital component of natural product-based medicine translation and development and is required to understand and potentially reduce mechanisms that may be associated with off-target side effects and toxicity. Omics-based techniques, including genomics, transcriptomics, proteomics, metabolomics, and bioinformatics, have become recognized as effective tools needed to construct innovative strategies to discover natural product targets. Although considerable progress has been made, the successful discovery of natural product targets remains a challenging time-consuming process that has come to increasingly rely on the effective integration of multi-omics-based technologies to create emerging panomics (a.k.a., integrative omics, pan-omics, multiomics)-based strategies. This review summarizes a series of successful studies regarding the application of integrative omics-based methods in natural product target discovery. The advantages and disadvantages of each technique are discussed, with a particular focus on the systematic integration of multi-omics strategies. Further, emerging micro-scale single-cell-based techniques are introduced, especially to deal with minute natural product samples.

Conservation of metabolic regulation by phosphorylation and non-covalent small-molecule interactions

Here, we review extant observations of protein phosphorylation and small-molecule interactions in metabolism and ask which of their specific regulatory functions are conserved in Escherichia coli and Homo sapiens. While the number of phosphosites is dramatically higher in humans, the number of metabolite-protein interactions remains largely constant. Moreover, we found the regulatory logic of metabolite-protein interactions, and in many cases also the effector molecules, to be conserved. Post-translational regulation through phosphorylation does not appear to replace this regulation in human but rather seems to add additional opportunities for fine-tuning and more complex responses. The abundance of metabolite-protein interactions in metabolism, their conserved cross-species abundance, and the apparent conservation of regulatory logic across enormous phylogenetic distance demonstrate their relevance for maintaining cellular homeostasis in these ancient biological processes.

Protein Folding Stability Changes Across the Proteome Reveal Targets of Cu Toxicity in E. coli

The ability of metal ionophores to induce cellular metal hyperaccumulation endows them with potent antimicrobial activity; however, the targets and mechanisms behind these outcomes are not well understood. This work describes the first utilization of proteome-wide measurements of protein folding stability in combination with protein expression level analysis to identify protein targets of copper, thereby providing new insight into ionophore-induced copper toxicity in E. coli. The protein folding stability analysis employed a one-pot protocol for pulse proteolysis (PP) in combination with a semi-tryptic peptide enrichment strategy for proteolysis procedures (STEPP) to generate stability profiles for proteins in cell lysates derived from E. coli exposed to copper with and without two ionophores, the antimicrobial agent pyrithione and its β-lactamase-activated prodrug, PcephPT. As part of this work, the above cell lysates were also subject to protein expression level analysis using conventional quantitative bottom-up proteomic methods. The protein folding stability and expression level profiles generated here enabled the effects of ionophore vs copper to be distinguished and revealed copper-driven stability changes in proteins involved in processes spanning metabolism, translation, and cell redox homeostasis. The 159 differentially stabilized proteins identified in this analysis were significantly more numerous (∼3×) than the 53 proteins identified with differential expression levels. These results illustrate the unique information that protein stability measurements can provide to decipher metal-dependent processes in drug mode of action studies.

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no "one size fits all" approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

Target identification and validation of natural products with label-free methodology: A critical review from 2005 to 2020

Natural products (NPs) have been an important source of therapeutic drugs in clinic use and contributed many chemical probes for research. The usefulness of NPs is however often marred by the incomplete understanding of their direct cellular targets. A number of experimental methods for drug target identification have been developed over the years. One class of methods, termed "label-free" methodology, exploits the energetic and biophysical features accompanying the association of macromolecules with drugs and other compounds in their native forms. Herein we review the working principles, assay implementations, and key applications of the most important approaches, and also give examples where they have been applied to NPs. We also assess the key advantages and limitations of each method. Furthermore, we address when and how the label-free methodology can be particularly useful considering some of the unique features of NP chemistry and bioactivation.

Direct label-free methods for identification of target proteins in agrochemicals

Green agrochemicals are important guarantee for food production and security, and target protein identification is the most important basis for development of novel agrochemicals. Affinity chromatography methods for immobilization of agrochemicals have been widely used to identify and confirm new targets. However, this method often requires modification of the active molecules which can affect or damage its biological activity, and biomacromolecules, particularly most natural products, are hard to be modified either. In order to overcome the shortcomings of molecular modification, label-free technology has been developed based on evaluating responses to thermal or proteolytic treatments. Combined with the chemical biology technology and molecular biology technology, it has been used in the development of drugs and agrochemicals. Herein, common methods of label-free technology for identification of direct target of agrochemicals are reviewed, including the principle, advantages, limitations and applications in the research of agrochemicals in the last decade. And the methods for validation of candidate targets obtained by the label-free methods are also reviewed, which are important to obtain the accurate and reliable targets. Combined application of these methods will greatly reduce the experimental costs and shorten the period for the new target identification and validation by improving its accuracy, which will provide a systematic solution for new ecological agrochemicals research and development.

A Simplified Thermal Proteome Profiling Approach to Screen Protein Targets of a Ligand

Thermal proteome profiling is a powerful energetic-based chemical proteomics method to reveal the ligand-protein interaction. However, the costly multiplexed isotopic labeling reagent, mainly Multiplexed isobaric tandem mass tag (TMT), and the long mass spectrometric time limits the wide application of this method. Here a simple and cost-effective strategy by using dimethyl labeling technique instead of TMT labeling is reported to quantify proteins and by using the peptides derived from the same protein to determine significantly changed proteins in one LC-MS run. This method is validated by identifying the known targets of methotrexate and geldanamycin. In addition, several potential off-targets involved in detoxification of reactive oxygen species pathway are also discovered for geldanamycin. This method is further applied to map the interactome of adenosine triphosphate (ATP) in the 293T cell lysate by using ATP analogue, adenylyl imidodiphosphate (AMP-PNP), as the ligand. As a result, a total of 123 AMP-PNP-sensitive proteins are found, of which 59 proteins are stabilized by AMP-PNP. Approximately 53% and 20% of these stabilized candidate protein targets are known as ATP and RNA binding proteins. Overall, above results demonstrated that this approach could be a valuable platform for the unbiased target proteins identification with reduced reagent cost and mass spectrometric time.

Chaperone action of a cofactor in protein folding

Previously, we have reported that ATP accelerates the folding and unfolding of Escherichia coli glyceraldehyde-3-phosphate dehydrogenase (GAPDH), which is a glycolytic enzyme utilizing NAD⁺ as a cofactor. Because ATP and NAD⁺ share the ADP moiety, we hypothesized that NAD⁺ also accelerates the folding of GAPDH and that the common structural motif between ATP and NAD⁺ is responsible for the chaperone activity. After confirming that NAD⁺ indeed accelerates the folding of GAPDH, we examined the chaperone activity of the structural fragments of NAD⁺ (ADP, AMP, adenosine, and nicotinamide monophosphate). Our finding showed that ADP and AMP significantly speed up the folding of GAPDH, while adenosine and nicotinamide monophosphate do not. ADP and AMP also dramatically speed up the unfolding of GAPDH by selectively stabilizing a transition state in which GAPDH has a partially unfolded conformation. Similar to the previously reported effect of ATP on the equilibrium unfolding of GAPDH, a partially unfolded intermediate also accumulates in the presence of ADP and AMP. Based on the effect of the structural fragments of NAD⁺ on the folding of GAPDH, we identified that AMP is the structural determinant of the chaperone activity of ATP and NAD⁺ . Also, we propose a plausible model to explain how NAD⁺ accelerates the folding of GAPDH through a stepwise development of molecular interactions with the protein.

Comparative Analysis of Mass-Spectrometry-Based Proteomic Methods for Protein Target Discovery Using a One-Pot Approach

Recently, several mass-spectrometry- and protein-denaturation-based proteomic methods have been developed to facilitate protein target discovery efforts in drug mode-of-action studies. These methods, which include the stability of proteins from rates of oxidation (SPROX), pulse proteolysis (PP), chemical denaturation and protein precipitation (CPP), and thermal proteome profiling (TPP) techniques, have been used in an increasing number of applications in recent years. However, while the advantages and disadvantages to using these different techniques have been reviewed, the analytical characteristics of these methods have not been directly compared. Reported here is such a direct comparison using the well-studied immunosuppressive drug, cyclosporine A (CsA), and the proteins in a yeast cell lysate. Also described is a one-pot strategy that can be utilized with each technique to streamline data acquisition and analysis. We find that there are benefits to utilizing all four strategies for protein target discovery including increased proteomic coverage and reduced false positive rates that approach 0%. Moreover, the one-pot strategy described here makes such an experiment feasible, because of the 10-fold reduction in reagent costs and instrument time it affords.

Mass spectrometric approaches for profiling protein folding and stability

Protein stability reports on protein homeostasis, function, and binding interactions, such as to other proteins, metabolites and drugs. As such, there is a pressing need for technologies that can report on protein stability. The ideal technique could be applied in vitro or in vivo systems, proteome-wide, independently of matrix, under native conditions, with residue-level resolution, and on protein at endogenous levels. Mass spectrometry has rapidly become a preferred technology for identifying and quantifying proteins. As such, it has been increasingly incorporated into methodologies for interrogating protein stability and folding. Although no single technology can satisfy all desired applications, several emerging approaches have shown outstanding success at providing biological insight into the stability of the proteome. This chapter outlines some of these recent emerging technologies.

Emerging strategies for the identification of protein-metabolite interactions

Interactions between biological molecules enable life. The significance of a cell-wide understanding of molecular complexes is thus obvious. In comparison to protein-protein interactions, protein-metabolite interactions remain under-studied. However, this has been gradually changing due to technological progress. Here, we focus on the interactions between ligands and receptors, the triggers of signalling events. While the number of small molecules with proven or proposed signalling roles is rapidly growing, most of their protein receptors remain unknown. Conversely, there are numerous signalling proteins with predicted ligand-binding domains for which the identities of the metabolite counterparts remain elusive. Here, we discuss the current biochemical strategies for identifying protein-metabolite interactions and how they can be used to characterize known metabolite regulators and identify novel ones.

Protein profiling and pseudo-parallel reaction monitoring to monitor a fusion-associated conformational change in hemagglutinin

Influenza infection requires viral escape from early endosomes into the cytosol, which is enabled by an acid-induced irreversible conformational transformation in the viral protein hemagglutinin. Despite the direct relationship between this conformational change and infectivity, label-free methods for characterizing this and other protein conformational changes in biological mixtures are limited. While the chemical reactivity of the protein backbone and side-chain residues is a proxy for protein conformation, coupling this reactivity to quantitative mass spectrometry is a challenge in complex environments. Herein, we evaluate whether electrophilic amidination coupled with pseudo-parallel reaction monitoring is an effective label-free approach to detect the fusion-associated conformational transformation in recombinant hemagglutinin (rHA). We identified rHA peptides that are differentially amidinated between the pre- and post-fusion states, and validated that this difference relies upon the fusion-associated conformational switch. We further demonstrate that we can distinguish the fusion profile in a matrix of digested cellular lysate. This fusion assay can be used to evaluate fusion competence for modified HA. Graphical abstract.

Identification of bioactive metabolites using activity metabolomics

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.

Chemo-Selection Strategy for Limited Proteolysis Experiments on the Proteomic Scale

Described here is a chemo-selective enrichment strategy, termed the semitryptic peptide enrichment strategy for proteolysis procedures (STEPP), to isolate the semitryptic peptides generated in mass spectrometry-based proteome-wide applications of limited proteolysis methods. The strategy involves reacting the ε-amino groups of lysine side chains and any N-termini created in the limited proteolysis reaction with isobaric mass tags. A subsequent digestion of the sample with trypsin and the chemo-selective reaction of the newly exposed N-termini of the tryptic peptides with N-hydroxysuccinimide (NHS)-activated agarose resin removes the tryptic peptides from solution, leaving only the semitryptic peptides with one nontryptic cleavage site generated in the limited proteolysis reaction for subsequent LC-MS/MS analysis. As part of this work, the STEPP technique is interfaced with two different proteolysis methods, including the pulse proteolysis (PP) and limited proteolysis (LiP) methods. The STEPP-PP workflow is evaluated in two proof-of-principle experiments involving the proteins in a yeast cell lysate and two well-studied drugs, cyclosporin A and geldanamycin. The STEPP-LiP workflow is evaluated in a proof-of-principle experiment involving the proteins in two cell culture models of human breast cancer, MCF-7 and MCF-10A cell lines. The STEPP protocol increased the number of semitryptic peptides detected in the LiP and PP experiments by 5- to 10-fold. The STEPP protocol not only increases the proteomic coverage, but also increases the amount of structural information that can be gleaned from limited proteolysis experiments. Moreover, the protocol also enables the quantitative determination of ligand binding affinities.

Proteome-Wide Structural Biology: An Emerging Field for the Structural Analysis of Proteins on the Proteomic Scale

Over the past decade, a suite of new mass-spectrometry-based proteomics methods has been developed that now enables the conformational properties of proteins and protein-ligand complexes to be studied in complex biological mixtures, from cell lysates to intact cells. Highlighted here are seven of the techniques in this new toolbox. These techniques include chemical cross-linking (XL-MS), hydroxyl radical footprinting (HRF), Drug Affinity Responsive Target Stability (DARTS), Limited Proteolysis (LiP), Pulse Proteolysis (PP), Stability of Proteins from Rates of Oxidation (SPROX), and Thermal Proteome Profiling (TPP). The above techniques all rely on conventional bottom-up proteomics strategies for peptide sequencing and protein identification. However, they have required the development of unconventional proteomic data analysis strategies. Discussed here are the current technical challenges associated with these different data analysis strategies as well as the relative analytical capabilities of the different techniques. The new biophysical capabilities that the above techniques bring to bear on proteomic research are also highlighted in the context of several different application areas in which these techniques have been used, including the study of protein ligand binding interactions (e.g., protein target discovery studies and protein interaction network analyses) and the characterization of biological states.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Proteome-Wide Characterization of Phosphorylation-Induced Conformational Changes in Breast Cancer

Because of the close link between protein function and protein folding stability, knowledge about phosphorylation-induced protein folding stability changes can lead to a better understanding of the functional effects of protein phosphorylation. Here, the stability of proteins from rates of oxidation (SPROX) and limited proteolysis (LiP) techniques are used to compare the conformational properties of proteins in two MCF-7 cell lysates including one that was and one that was not dephosphorylated with alkaline phosphatase. A total of 168 and 251 protein hits were identified with dephosphorylation-induced stability changes using the SPROX and LiP techniques, respectively. Many protein hits are previously known to be differentially phosphorylated or differentially stabilized in different human breast cancer subtypes, suggesting that the phosphorylation-induced stability changes detected in this work are disease related. The SPROX hits were enriched in proteins with aminoacyl-tRNA ligase activity. These enriched protein hits included many aminoacyl-tRNA synthetases (aaRSs), which are known from previous studies to have their catalytic activity modulated by phosphorylation. The SPROX results revealed that the magnitudes of the destabilizing effects of dephoshporylation on the different aaRSs were directly correlated with their previously reported aminoacylation activity change upon dephosphorylation. This substantiates the close link between protein folding and function.

Discovery of Tamoxifen and N-Desmethyl Tamoxifen Protein Targets in MCF-7 Cells Using Large-Scale Protein Folding and Stability Measurements

The proteins in an MCF-7 cell line were probed for tamoxifen (TAM) and n-desmethyl tamoxifen (NDT) induced stability changes using the Stability of Proteins from Rates of Oxidation (SPROX) technique in combination with two different quantitative proteomics strategies, including one based on SILAC and one based on isobaric mass tags. Over 1000 proteins were assayed for TAM- and NDT-induced protein stability changes, and a total of 163 and 200 protein hits were identified in the TAM and NDT studies, respectively. A subset of 27 high-confidence protein hits were reproducibly identified with both proteomics strategies and/or with multiple peptide probes. One-third of the high-confidence hits have previously established experimental links to the estrogen receptor, and nearly all of the high-confidence hits have established links to breast cancer. One high-confidence protein hit that has known estrogen receptor binding properties, Y-box binding protein 1 (YBX1), was further validated as a direct binding target of TAM using both the SPROX and pulse proteolysis techniques. Proteins with TAM- and/or NDT-induced expression level changes were also identified in the SILAC-SPROX experiments. These proteins with expression level changes included only a small fraction of those with TAM- and/or NDT-induced stability changes.

ATP alters protein folding and function of Escherichia coli uridine phosphorylase

Uridine phosphorylase is one of the critical enzymes in the pyrimidine salvage pathway. Cells regenerate uridine for nucleotide metabolism by incorporating uracil with ribose-1-phosphate with this enzyme. Recent studies indicate that Escherichia coli uridine phosphorylase is destabilized in the presence of ATP. However, the mechanism underlying the destabilization process and its influence on uridine phosphorylase function remain to be established. Here, we comprehensively investigated the effects of ATP on protein folding and function of Escherichia coli uridine phosphorylase. Our results demonstrate that ATP apparently decreases the stability of uridine phosphorylase in a concentration-dependent manner. Additionally, simply increasing the level of ATP led to a reduction of enzymatic activity to complete inhibition. Further studies showed that uridine phosphorylase accumulates as a partially unfolded state in the presence of ATP. Moreover, ATP specifically accelerated the unfolding rate of uridine phosphorylase with no observable effects on the refolding process. Our preliminary findings suggest that ATP can alter the protein folding and function of enzymes via apparent destabilization. This mechanism may be significant for proteins functioning under conditions of high levels of ATP, such as cancer cell environments.

Proteomic and network analysis of human serum albuminome by integrated use of quick crosslinking and two-step precipitation

Affinity- and chemical-based methods are usually employed to prepare human serum albuminome; however, these methods remain technically challenging. Herein, we report the development of a two-step precipitation (TSP) method by combined use of polyethylene glycol (PEG) and ethanol. PEG precipitation was newly applied to remove immunoglobulin G for albuminome preparation, which is simple, cost effective, efficient and compatible with downstream ethanol precipitation. Nonetheless, chemical extraction using TSP may disrupt weak and transient protein interactions with human serum albumin (HSA) leading to an incomplete albuminome. Accordingly, rapid fixation based on formaldehyde crosslinking (FC) was introduced into the TSP procedure. The developed FC-TSP method increased the number of identified proteins, probably by favouring real-time capture of weakly bound proteins in the albuminome. A total of 171 proteins excluding HSA were identified from the fraction obtained with FC-TSP. Further interaction network and cluster analyses revealed 125 HSA-interacting proteins and 14 highly-connected clusters. Compared with five previous studies, 55 new potential albuminome proteins including five direct and 50 indirect binders were only identified by our strategy and 12 were detected as common low-abundance proteins. Thus, this new strategy has the potential to effectively survey the human albuminome, especially low-abundance proteins of clinical interest.

Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using SILAC-SPROX

Proteomic methods for disease state characterization and biomarker discovery have traditionally utilized quantitative mass spectrometry methods to identify proteins with altered expression levels in disease states. Here we report on the large-scale use of protein folding stability measurements to characterize different subtypes of breast cancer using the stable isotope labeling with amino acids in cell culture and stability of proteins from rates of oxidation (SILAC-SPROX) technique. Protein folding stability differences were studied in a comparison of two luminal breast cancer subtypes, luminal-A and -B (i.e., MCF-7 and BT-474 cells, respectively), and in a comparison of a luminal-A and basal subtype of the disease (i.e., MCF-7 and MDA-MB-468 cells, respectively). The 242 and 445 protein hits identified with altered stabilities in these comparative analyses included a large fraction with no significant expression level changes. This suggests thermodynamic stability measurements create a new avenue for protein biomarker discovery. A number of the identified protein hits are known from other biochemical studies to play a role in tumorigenesis and cancer progression. This not only substantiates the biological significance of the protein hits identified using the SILAC-SPROX approach, but it also helps elucidate the molecular basis for their disregulation and/or disfunction in cancer.

Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using Limited Proteolysis

Conformational changes in proteins can lead to disease. Thus, methods for identifying conformational changes in proteins can further improve our understanding and facilitate detection of disease states. Here we combine limited proteolysis (LiP) with Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) to characterize breast cancer-related conformational changes in proteins on the proteomic scale. Studied here are the conformational properties of proteins in two cell culture models of breast cancer, including the MCF-10A and MCF-7 cell lines. The SILAC-LiP approach described here identified ∼200 proteins with cell-line-dependent conformational changes, as determined by their differential susceptibility to proteolytic digestion using the nonspecific protease, proteinase K. The protease susceptibility profiles of the proteins in these cell lines were compared to thermodynamic stability and expression level profiles previously generated for proteins in these same breast cancer cell lines. The comparisons revealed that there was little overlap between the proteins with protease susceptibility changes and the proteins with thermodynamic stability and/or expression level changes. Thus, the large-scale conformational analysis described here provides unique insight into the molecular basis of the breast cancer phenotypes in this study.

Discovery of Age-Related Protein Folding Stability Differences in the Mouse Brain Proteome

Described here is the application of thermodynamic stability measurements to study age-related differences in the folding and stability of proteins in a rodent model of aging. Thermodynamic stability profiles were generated for 809 proteins in brain cell lysates from mice, aged 6 (n = 7) and 18 months (n = 9) using the Stability of Proteins from Rates of Oxidation (SPROX) technique. The biological variability of the protein stability measurements was low and within the experimental error of SPROX. A total of 83 protein hits were detected with age-related stability differences in the brain samples. Remarkably, the large majority of the brain protein hits were destabilized in the old mice, and the hits were enriched in proteins that have slow turnover rates (p < 0.07). Furthermore, 70% of the hits have been previously linked to aging or age-related diseases. These results help validate the use of thermodynamic stability measurements to capture relevant age-related proteomic changes and establish a new biophysical link between these proteins and aging.

Targeted Mass Spectrometry-Based Approach for Protein-Ligand Binding Analyses in Complex Biological Mixtures Using a Phenacyl Bromide Modification Strategy

The characterization of protein folding stability changes on the proteomic scale is useful for protein-target discovery and for the characterization of biological states. The Stability of Proteins from Rates of Oxidation (SPROX) technique is one of several mass spectrometry-based techniques recently established for the making proteome-wide measurements of protein folding and stability. A critical part of proteome-wide applications of SPROX is the identification and quantitation of methionine-containing peptides. Demonstrated here is a targeted mass spectrometry-based proteomics strategy for the detection and quantitation of methionine-containing peptides in SPROX experiments. The strategy involves the use of phenacyl bromide (PAB) for the targeted detection and quantitation of methionine-containing peptides in SPROX using selective reaction monitoring (SRM) on a triple quadrupole mass spectrometer (QQQ-MS). As proof-of-principle, the known binding interaction of Cyclosporine A with cyclophilin A protein in a yeast cell lysate is successfully detected and quantified using a targeted SRM workflow. Advantages of the described workflow over other SPROX protocols include a 20-fold reduction in the amount of total protein needed for analysis and the ability to work with the endogenous proteins in a given sample (e.g., stabile isotope labeling with amino acids in cell culture is not necessary).

Energetic Coupling between Ligand Binding and Dimerization in Escherichia coli Phosphoglycerate Mutase

Energetic coupling of two molecular events in a protein molecule is ubiquitous in biochemical reactions mediated by proteins, such as catalysis and signal transduction. Here, we investigate energetic coupling between ligand binding and folding of a dimer using a model system that shows three-state equilibrium unfolding of an exceptional quality. The homodimeric Escherichia coli cofactor-dependent phosphoglycerate mutase (dPGM) was found to be stabilized by ATP in a proteome-wide screen, although dPGM does not require or utilize ATP for enzymatic function. We investigated the effect of ATP on the thermodynamic stability of dPGM using equilibrium unfolding. We found that, in the absence of ATP, dPGM populates a partially unfolded, monomeric intermediate during equilibrium unfolding. However, addition of 1.0 mM ATP drastically reduces the population of the intermediate by selectively stabilizing the native dimer. Using a computational ligand docking method, we predicted ATP binds to the active site of the enzyme using the triphosphate group. By performing equilibrium unfolding and isothermal titration calorimetry with active-site variants of dPGM, we confirmed that active-site residues are involved in ATP binding. Our findings show that ATP promotes dimerization of the protein by binding to the active site, which is distal from the dimer interface. This cooperativity suggests an energetic coupling between the active site and the dimer interface. We also propose a structural link to explain how ligand binding to the active site is energetically coupled with dimerization.

Characterization of the Saccharomyces cerevisiae ATP-Interactome using the iTRAQ-SPROX Technique

The stability of proteins from rates of oxidation (SPROX) technique was used in combination with an isobaric mass tagging strategy to identify adenosine triphosphate (ATP) interacting proteins in the Saccharomyces cerevisiae proteome. The SPROX methodology utilized in this work enabled 373 proteins in a yeast cell lysate to be assayed for ATP interactions (both direct and indirect) using the non-hydrolyzable ATP analog, adenylyl imidodiphosphate (AMP-PNP). A total of 28 proteins were identified with AMP-PNP-induced thermodynamic stability changes. These protein hits included 14 proteins that were previously annotated as ATP-binding proteins in the Saccharomyces Genome Database (SGD). The 14 non-annotated ATP-binding proteins included nine proteins that were previously found to be ATP-sensitive in an earlier SPROX study using a stable isotope labeling with amino acids in cell culture (SILAC)-based approach. A bioinformatics analysis of the protein hits identified here and in the earlier SILAC-SPROX experiments revealed that many of the previously annotated ATP-binding protein hits were kinases, ligases, and chaperones. In contrast, many of the newly discovered ATP-sensitive proteins were not from these protein classes, but rather were hydrolases, oxidoreductases, and nucleic acid-binding proteins. Graphical Abstract ᅟ.

Label-free technologies for target identification and validation

Chemical probes have been instrumental in revealing new targets and confirming target engagement. However, substantial effort and resources are required to design and synthesize these probes. In contrast, label-free technologies have the advantage of bypassing the need for chemical probes. Here we highlight the recent developments in label-free methods and discuss the pros and cons of each approach.

Advances in identification and validation of protein targets of natural products without chemical modification

This review focuses on and reports case studies of the latest advances in target protein identification methods for label-free natural products. The integration of newly developed technologies will provide new insights and highlight the value of natural products for use as biological probes and new drug candidates.

Global analysis of protein folding thermodynamics for disease state characterization

Current methods for the large-scale characterization of disease states generally rely on the analysis of gene and/or protein expression levels. These existing methods fail to detect proteins with disease-related functions and unaltered expression levels. Here we describe the large-scale use of thermodynamic measurements of protein folding and stability for the characterization of disease states. Using the Stable Isotope Labeling with Amino Acids in Cell Culture and Stability of Proteins from Rates of Oxidation (SILAC-SPROX) technique, we assayed ∼800 proteins for protein folding and stability changes in three different cell culture models of breast cancer including the MCF-10A, MCF-7, and MDA-MB-231 cell lines. The thermodynamic stability profiles generated here created distinct molecular markers to differentiate the three cell lines, and a significant fraction (∼45%) of the differentially stabilized proteins did not have altered expression levels. Thus, the differential thermodynamic profiling strategy reported here created novel molecular signatures of breast cancer and provided additional insight into the molecular basis of the disease. Our results establish the utility of protein folding and stability measurements for the study of disease processes, and they suggest that such measurements may be useful for biomarker discovery in disease.