Thermal proteome profiling for interrogating protein interactions

Harmonizing biosynthesis with post-ingestive modifications to understand the ecological functions of plant natural products

Covering: up to 2022The recent dramatic advances in our understanding of the biosynthetic pathways that produce diverse bouquets of plant-derived natural products have far surpassed our understanding of the function of these compounds for plants: how they influence a plant's Darwinian fitness in nature. Our understanding of their mechanisms, the life-processes targeted by these compounds, is similarly poorly resolved. Many plant specialized metabolites (PSMs) are further modified after ingestion by herbivores, and these post-ingestive modifications are frequently essential for PSM function. Here we summarize the biosynthesis and functional mechanisms of 17-hydroxygeranyllinalool diterpene glycosides in the ecological model plant Nicotiana attenuata, and summarize the post-ingestive modifications known from other two-component PSMs. We propose that parallel comparisons of plant natural product biosynthetic pathways and insect post-ingestive metabolism of the same plant tissues ("frassomics") will facilitate the often-elusive identification of the molecular targets of these effective chemical defenses, contribute to elucidations of post-ingestive metabolite interactions in insect guts, and predicate the rapid evolutions of resistance against insecticides inspired by PSMs. We highlight the value of conducting these parallel investigations at the level of the entire metabolome so as to include the multiple interacting pathways in both natural product biosynthesis as well as their post-ingestive processing. We introduce the concept of frass metabolite QTL (fmQTL) analysis that integrates powerful forward genetic approaches with frassomics, and suggest that insect-guided high-throughput forward- and reverse-genetics approaches in natural habitats will advance our understanding of PSM biosynthesis and function.

Proteome-wide analysis of stress response to temperature in Sulfolobus islandicus

Sulfolobus islandicus is thermophilic archaea that live in an extreme environment of 75 °C-80 °C and pH 2-3. Currently, the molecular mechanism of archaeal adaptation to high temperatures and the stability of proteins at high temperatures are still unclear. This study utilizes proteomics to analyze the differential expression of S. islandicus proteins at different temperatures. We found that ribosomes, glycolysis, nucleotide metabolism, RNA metabolism, transport system, and sulfur metabolism are all affected by temperature. Methylation modification of some proteins changed with temperature. Thermal proteome profiling (TPP) was used to analyze the thermal stability of proteins under 65 °C-85 °C growth conditions. It is suggested that the T_m values of proteins are mainly distributed around the optimum growth temperature (OGT). The proteins in the glycolysis pathway had high thermal stability. Meanwhile, proteins related to DNA replication and translation showed low thermal stability. The protein thermal stability of S. islandicus cultured under 65 °C and 85 °C was higher than that of 75 °C. Our study reveals that S. islandicus may adapt to temperature changes by regulating protein synthesis and carbon metabolism pathways, changing post-translational modifications, and improving protein stability at the same time. SIGNIFICANCE: The molecular mechanism of archaeal adaptation to high temperatures and the stability of proteins at high temperatures are still unclear. Our proteomics study identified 477 differentially expressed proteins of S. islandicus at different temperatures, suggesting that ribosomes, glycolysis, nucleotide metabolism, RNA metabolism, transport system, and sulfur metabolism are affected by temperature. Meanwhile, we found that methylation modification of some proteins changed with temperature. To evaluate the thermal stability of the proteome, we performed thermal proteome profiling to analyze the Tm of proteins under 65 °C-85 °C growth conditions. T_m values of proteins are mainly distributed around the optimum growth temperature. The proteins in the glycolysis pathway had high thermal stability. Meanwhile, proteins related to DNA replication and translation showed low thermal stability. Our study reveals that S. islandicus may adapt to temperature changes by regulating protein synthesis and carbon metabolism pathways, changing post-translational modifications, and improving protein stability at the same time.

FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis

Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets, especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner. This method was built based on a hypothesis that cells under a status of homeostasis would take responses to drug perturbation by activating feedback loops. Therefore, the drug interacting targets can be predicted by analyzing the network responses after drug perturbation. We evaluated this method using the expression data of estrogen stimulation, gene manipulation and drug perturbation and validated its good performance to identify the annotated drug targets. Using STAT3 as a target protein, we applied this method to drug perturbation data of 500 natural compounds and predicted five compounds with STAT3 interacting activities. Experimental assay validated the STAT3-interacting activities of four compounds. Overall, our evaluation suggests that FL-DTD predicts the drug interacting targets with good accuracy and can be used for drug target discovery.

Thermal Proteome Profiling Reveals Distinct Target Selectivity for Differentially Oxidized Oxysterols

Oxysterols are produced physiologically by many species; however, their distinct roles in regulating human physiology have not been studied systematically. The role of differing oxidation states and sites in mediating their biological functions is also unclear. As oxysterols have been associated with atherosclerosis, neurodegeneration, and cancer, a better understanding of their protein targets is desirable. To address this, we mapped the oxysterol interactome with three A- and B-ring oxidized sterols as well as 25-hydroxy cholesterol using thermal proteome profiling, validating selected targets with the cellular thermal shift assay and isothermal dose response fingerprinting. This revealed that the site of oxidation has a profound impact on target selectivity, with each oxysterol possessing an almost unique set of target proteins. Overall, targets clustered in pathways relating to vesicular transport and phosphoinositide metabolism, suggesting that while individual oxysterols bind to a unique set of proteins, the processes they modulate are highly interconnected.

PCfun: a hybrid computational framework for systematic characterization of protein complex function

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes—or macromolecular machines—are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

Small Molecule Arranged Thermal Proximity Coaggregation (smarTPCA)-A Novel Approach to Characterize Protein-Protein Interactions in Living Cells by Similar Isothermal Dose-Responses

Chemical biology and the application of small molecules has proven to be a potent perturbation strategy, especially for the functional elucidation of proteins, their networks, and regulators. In recent years, the cellular thermal shift assay (CETSA) and its proteome-wide extension, thermal proteome profiling (TPP), have proven to be effective tools for identifying interactions of small molecules with their target proteins, as well as off-targets in living cells. Here, we asked the question whether isothermal dose-response (ITDR) CETSA can be exploited to characterize secondary effects downstream of the primary binding event, such as changes in post-translational modifications or protein-protein interactions (PPI). By applying ITDR-CETSA to MAPK14 kinase inhibitor treatment of living HL-60 cells, we found similar dose-responses for the direct inhibitor target and its known interaction partners MAPKAPK2 and MAPKAPK3. Extension of the dose-response similarity comparison to the proteome wide level using TPP with compound concentration range (TPP-CCR) revealed not only the known MAPK14 interaction partners MAPKAPK2 and MAPKAPK3, but also the potentially new intracellular interaction partner MYLK. We are confident that dose-dependent small molecule treatment in combination with ITDR-CETSA or TPP-CCR similarity assessment will not only allow discrimination between primary and secondary effects, but will also provide a novel method to study PPI in living cells without perturbation by protein modification, which we named "small molecule arranged thermal proximity coaggregation" (smarTPCA).

Hidden information on protein function in censuses of proteome foldedness

Methods that assay protein foldedness with proteomics have generated censuses of apparent protein folding stabilities in biological milieu. However, different censuses poorly correlate with each other. Here, we show that the reason for this is that methods targeting foldedness through monitoring amino acid sidechain reactivity also detect changes in conformation and ligand binding, which can be a substantial fraction of the data. We show that the reactivity of only one quarter of cysteine or methionine sidechains in proteins in a urea denaturation curve of mammalian cell lysate can be confidently explained by a two-state unfolding isotherm. Contrary to that expected from unfolding, up to one third of the cysteines decreased reactivity. These cysteines were enriched in proteins with functions relating to unfolded protein stress. One protein, chaperone HSPA8, displayed changes arising from ligand and cofactor binding. Unmasking this hidden information using the approaches outlined here should improve efforts to understand both folding and the remodeling of protein function directly in complex biological settings.

Proteomics can define features of proteome foldedness by assessing the reactivity of surface exposed amino acids. Here, the authors show that such exposure patterns yield insight to structural changes in chaperones as they bind to unfolded proteins in urea-denatured mammalian cell lysate.

Validating Small Molecule Chemical Probes for Biological Discovery

Small molecule chemical probes are valuable tools for interrogating protein biological functions and relevance as a therapeutic target. Rigorous validation of chemical probe parameters such as cellular potency and selectivity is critical to unequivocally linking biological and phenotypic data resulting from treatment with a chemical probe to the function of a specific target protein. A variety of modern technologies are available to evaluate cellular potency and selectivity, target engagement, and functional response biomarkers of chemical probe compounds. Here, we review these technologies and the rationales behind using them for the characterization and validation of chemical probes. In addition, large-scale phenotypic characterization of chemical probes through chemical genetic screening is increasingly leading to a wealth of information on the cellular pharmacology and disease involvement of potential therapeutic targets. Extensive compound validation approaches and integration of phenotypic information will lay foundations for further use of chemical probes in biological discovery. Expected final online publication date for the Annual Review of Biochemistry, Volume 91 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Challenges and Opportunities for Bayesian Statistics in Proteomics

Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

Thermal Proteome Profiling Reveals the O-GlcNAc-Dependent Meltome

Posttranslational modifications alter the biophysical properties of proteins and thereby influence cellular physiology. One emerging manner by which such modifications regulate protein functions is through their ability to perturb protein stability. Despite the increasing interest in this phenomenon, there are few methods that enable global interrogation of the biophysical effects of posttranslational modifications on the proteome. Here, we describe an unbiased proteome-wide approach to explore the influence of protein modifications on the thermodynamic stability of thousands of proteins in parallel. We apply this profiling strategy to study the effects of O-linked N-acetylglucosamine (O-GlcNAc), an abundant modification found on hundreds of proteins in mammals that has been shown in select cases to stabilize proteins. Using this thermal proteomic profiling strategy, we identify a set of 72 proteins displaying O-GlcNAc-dependent thermostability and validate this approach using orthogonal methods targeting specific proteins. These collective observations reveal that the majority of proteins influenced by O-GlcNAc are, surprisingly, destabilized by O-GlcNAc and cluster into distinct macromolecular complexes. These results establish O-GlcNAc as a bidirectional regulator of protein stability and provide a blueprint for exploring the impact of any protein modification on the meltome of, in principle, any organism.

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.

Assessing target engagement using proteome-wide solvent shift assays

Recent advances in mass spectrometry (MS) have enabled quantitative proteomics to become a powerful tool in the field of drug discovery, especially when applied toward proteome-wide target engagement studies. Similar to temperature gradients, increasing concentrations of organic solvents stimulate unfolding and precipitation of the cellular proteome. This property can be influenced by physical association with ligands and other molecules, making individual proteins more or less susceptible to solvent-induced denaturation. Herein, we report the development of proteome-wide solvent shift assays by combining the principles of solvent-induced precipitation (Zhang et al., 2020) with modern quantitative proteomics. Using this approach, we developed solvent proteome profiling (SPP), which is capable of establishing target engagement through analysis of SPP denaturation curves. We readily identified the specific targets of compounds with known mechanisms of action. As a further efficiency boost, we applied the concept of area under the curve analysis to develop solvent proteome integral solubility alteration (solvent-PISA) and demonstrate that this approach can serve as a reliable surrogate for SPP. We propose that by combining SPP with alternative methods, like thermal proteome profiling, it will be possible to increase the absolute number of high-quality melting curves that are attainable by either approach individually, thereby increasing the fraction of the proteome that can be screened for evidence of ligand binding.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Interleukin-2-inducible T-cell kinase (Itk) signaling regulates potent noncanonical regulatory T cells

Regulatory T cells (Tregs) play an important role in controlling autoimmunity and limiting tissue damage and inflammation. IL2-inducible T cell kinase (Itk) is part of the Tec family of tyrosine kinases and is a critical component of T cell receptor mediated signaling. Here, we showed that either genetic ablation of Itk signaling or inhibition of Itk signaling pathways resulted in increased frequency of "noncanonical" CD4+ CD25- FOXP3+ Tregs (ncTregs), as well as of "canonical" CD4+ CD25+ FOXP3+ Tregs (canTregs). Using in vivo models, we showed that ncTregs can avert the formation of acute graft-versus-host disease (GVHD), in part by reducing conventional T cell proliferation, proinflammatory cytokine production, and tissue damage. This reduction in GVHD occurred without disruption of graft-versus-leukaemia (GVL) effects. RNA sequencing revealed that a number of effector, cell adhesion, and migration molecules were upregulated in Itk-/- ncTregs. Furthermore, disrupting the SLP76: ITK interaction using a specific peptide inhibitor led to enhanced Treg development in both mouse and primary human cells. This peptide inhibitor also significantly reduced inflammatory cytokine production in primary GVHD patient samples and mouse T cells without causing cell death or apoptosis. We provide evidence that specifically targeting Itk signaling could be a therapeutic strategy to treat autoimmune disorders.

Mammalian Chemical Genomics towards Identifying Targets and Elucidating Modes-of-Action of Bioactive Compounds

The step of identifying target molecules and elucidating the mode of action of bioactive compounds is a major bottleneck for drug discovery from phenotypic screening. Genetic screening for genes that affect drug sensitivity or phenotypes of mammalian cultured cells is a powerful tool to obtain clues to their modes of action. Chemical genomic screening systems for comprehensively identifying such genes or genetic pathways have been established using shRNA libraries for RNA interference-mediated mRNA knockdown or sgRNA libraries for CRISPR/Cas9-mediated gene knockout. The combination of chemical genomic screening in mammalian cells with other approaches such as biochemical searches for target molecules, phenotypic profiling, and yeast genetics provides a systematic way to elucidate the mode of action by converging various pieces of information regarding target molecules, target pathways, and synthetic lethal pathways.

Thermal Proteome Profiling to Identify Protein-ligand Interactions in the Apicomplexan Parasite Toxoplasma gondii

Toxoplasma gondii is a single-celled eukaryotic parasite that chronically infects a quarter of the global population. In recent years, phenotypic screens have identified compounds that block parasite replication. Unraveling the pathways and molecular mechanisms perturbed by such compounds requires target deconvolution. In parasites, such deconvolution has been achieved via chemogenomic approaches-for example, directed evolution followed by whole-genome sequencing or genome-wide knockout screens. As a proteomic alternative that directly probes the physical interaction between compound and protein, thermal proteome profiling (TPP), also known as the cellular thermal shift assay (CETSA), recently emerged as a method to identify small molecule-target interactions in living cells and cell extracts in a variety of organisms, including unicellular eukaryotic pathogens. Ligand binding induces a thermal stability shift-stabilizing or destabilizing proteins that change conformationally in response to the ligand-that can be measured by mass spectrometry (MS). Cells are incubated with different concentrations of ligand and heated, causing thermal denaturation of proteins. The soluble protein is extracted and quantified with multiplexed, quantitative MS, resulting in thousands of thermal denaturation profiles. Proteins engaging the ligand can be identified by their compound-dependent thermal shift. The protocol provided here can be used to identify ligand-target interactions and assess the impact of environmental or genetic perturbations on the thermal stability of the proteome in T. gondii and other eukaryotic pathogens. Graphic abstract: Thermal proteome profiling for target identification in the apicomplexan parasite T. gondii.

Transcriptional and Post-Transcriptional Polar Effects in Bacterial Gene Deletion Libraries

Single-gene deletions can affect the expression levels of other genes in the same operon in bacterial genomes. Here, we used proteomics for 133 Escherichia coli gene deletion mutants and transcriptome sequencing (RNA-seq) data from 71 mutants to probe the extent of transcriptional and post-transcriptional effects of gene deletions in operons. Transcriptional effects were common on genes located downstream of the deletion and were consistent across all operon members, with nearly 40% of operons showing greater than 2-fold up- or downregulation. Surprisingly, we observed an additional post-transcriptional effect that leads to the downregulation of the gene located directly downstream of the targeted gene. This effect was correlated with their intergenic distance, despite the ribosome binding site of the gene downstream remaining intact during library construction. Overall, the data presented can guide future library construction and highlight the importance of follow-up experiments for assessing direct effects of single-gene deletions in operons. IMPORTANCE Single-gene deletion libraries have allowed genome-wide characterization of gene function and interactions. While each mutant intends to disrupt the function of a single gene, it can unintentionally target other genes, such as those located in the same operon as the deletion. The extent to which such polar effects occur in deletion libraries has not been assessed. In this work, we use proteomics and transcriptomics data to show that transcript level changes lead to nearly 40% of deletions in operons affecting the protein levels of genes located downstream by at least 2-fold. Furthermore, we observed a post-transcriptional effect on the gene located directly downstream of the deletion. These results can guide the design of future gene deletion libraries and emphasizes the importance of follow-up work when linking genotypes to phenotypes.

Quantitative Proteomics Using Isobaric Labeling: A Practical Guide

In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.

Metabolic channeling: predictions, deductions, and evidence

With the elucidation of myriad anabolic and catabolic enzyme-catalyzed cellular pathways crisscrossing each other, an obvious question arose: how could these networks operate with maximal catalytic efficiency and minimal interference? A logical answer was the postulate of metabolic channeling, which in its simplest embodiment assumes that the product generated by one enzyme passes directly to a second without diffusion into the surrounding medium. This tight coupling of activities might increase a pathway's metabolic flux and/or serve to sequester unstable/toxic/reactive intermediates as well as prevent their access to other networks. Here, we present evidence for this concept, commencing with enzymes that feature a physical molecular tunnel, to multi-enzyme complexes that retain pathway substrates through electrostatics or enclosures, and finally to metabolons that feature collections of enzymes assembled into clusters with variable stoichiometric composition. Lastly, we discuss the advantages of reversibly assembled metabolons in the context of the purinosome, the purine biosynthesis metabolon.

Drug Target Identification in Tissues by Thermal Proteome Profiling

Drug target deconvolution can accelerate the drug discovery process by identifying a drug's targets (facilitating medicinal chemistry efforts) and off-targets (anticipating toxicity effects or adverse drug reactions). Multiple mass spectrometry-based approaches have been developed for this purpose, but thermal proteome profiling (TPP) remains to date the only one that does not require compound modification and can be used to identify intracellular targets in living cells. TPP is based on the principle that the thermal stability of a protein can be affected by its interactions. Recent developments of this approach have expanded its applications beyond drugs and cell cultures to studying protein-drug interactions and biological phenomena in tissues. These developments open up the possibility of studying drug treatment or mechanisms of disease in a holistic fashion, which can result in the design of better drugs and lead to a better understanding of fundamental biology. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Chemical Probes for Understudied Kinases: Challenges and Opportunities

Over 20 years after the approval of the first-in-class protein kinase inhibitor imatinib, the biological function of a significant fraction of the human kinome remains poorly understood while most research continues to be focused on few well-validated targets. Given the strong genetic evidence for involvement of many kinases in health and disease, the understudied fraction of the kinome holds a large and unexplored potential for future therapies. Specific chemical probes are indispensable tools to interrogate biology enabling proper preclinical validation of novel kinase targets. In this Perspective, we highlight recent case studies illustrating the development of high-quality chemical probes for less-studied kinases and their application in target validation. We spotlight emerging techniques and approaches employed in the generation of chemical probes for protein kinases and beyond and discuss the associated challenges and opportunities.

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.

Chemoproteomics for Plasmodium Parasite Drug Target Discovery

Emerging Plasmodium parasite drug resistance is threatening progress towards malaria control and elimination. While recent efforts in cell-based, high-throughput drug screening have produced first-in-class drugs with promising activities against different Plasmodium life cycle stages, most of these antimalarial agents have elusive mechanisms of action. Though challenging to address, target identification can provide valuable information to facilitate lead optimization and preclinical drug prioritization. Recently, proteome-wide methods for direct assessment of drug-protein interactions have emerged as powerful tools in a number of systems, including Plasmodium. In this review, we will discuss current chemoproteomic strategies that have been adapted to antimalarial drug target discovery, including affinity- and activity-based protein profiling and the energetics-based techniques thermal proteome profiling and stability of proteins from rates of oxidation. The successful application of chemoproteomics to the Plasmodium blood stage highlights the potential of these methods to link inhibitors to their molecular targets in more elusive Plasmodium life stages and intracellular pathogens in the future.

C81-evoked inhibition of the TNFR1-NFκB pathway during inflammatory processes for stabilization of the impaired vascular endothelial barrier for leukocytes

Chronic inflammation-related diseases are characterized by persistent leukocyte infiltration into the underlying tissue. The vascular endothelium plays a major role in this pathophysiological condition. Only few therapeutic strategies focus on the vascular endothelium as a major target for an anti-inflammatory approach. In this study, we present the natural compound-derived carbazole derivative C81 as chemical modulator interfering with leukocyte-endothelial cell interactions. An in vivo assay employing intravital microscopy to monitor leukocyte trafficking after C81 treatment in postcapillary venules of a murine cremaster muscle was performed. Moreover, in vitro assays using HUVECs and monocytes were implemented. The impact of C81 on cell adhesion molecules and the NFκB signaling cascade was analyzed in vitro in endothelial cells. Effects of C81 on protein translation were determined by incorporation of a puromycin analog-based approach and polysome profiling. We found that C81 significantly reduced TNF-activated leukocyte trafficking in postcapillary venules. Similar results were obtained in vitro when C81 reduced leukocyte-endothelial cell interactions by down-regulating cell adhesion molecules. Focusing on the NFκB signaling cascade, we found that C81 reduced the activation on multiple levels of the cascade through promoted IκBα recovery by attenuation of IκBα ubiquitination and through reduced protein levels of TNFR1 caused by protein translation inhibition. We suggest that C81 might represent a promising lead compound for interfering with inflammation-related processes in endothelial cells by down-regulation of IκBα ubiquitination on the one hand and inhibition of translation on the other hand without exerting cytotoxic effects.

WIDENING THE BOTTLENECK OF PHOSPHOPROTEOMICS: EVOLVING STRATEGIES FOR PHOSPHOPEPTIDE ENRICHMENT

Phosphorylation is a form of protein posttranslational modification (PTM) that regulates many biological processes. Whereas phosphoproteomics is a scientific discipline that identifies and quantifies the phosphorylated proteome using mass spectrometry (MS). This task is extremely challenging as ~30% of the human proteome is phosphorylated; and each phosphoprotein may exist as multiple phospho-isoforms that are present in low abundance and stoichiometry. Hence, phosphopeptide enrichment techniques are indispensable to (phospho)proteomics laboratories. These enrichment methods encompass widely-adopted techniques such as (i) affinity-based chromatography; (ii) ion exchange and mixed-mode chromatography (iii) enrichment with phospho-specific antibodies and protein domains, and (iv) functionalized polymers and other less common but emerging technologies such as hydroxyapatite chromatography and precipitation with inorganic ions. Here, we review these techniques, their history, continuous development and evaluation. Besides, we outline associating challenges of phosphoproteomics that are linked to experimental design, sample preparation, and proteolytic digestion. In addition, we also discuss about the future outlooks in phosphoproteomics, focusing on elucidating the noncanonical phosphoproteome and deciphering the "dark phosphoproteome". © 2020 John Wiley & Sons Ltd.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

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.

The Synergistic Effects of 5-Aminosalicylic Acid and Vorinostat in the Treatment of Ulcerative Colitis

Background: The drug 5-aminosalicylic acid (5-ASA) is the first-line therapy for the treatment of patients with mild-to-moderate ulcerative colitis (UC). However, in some cases, 5-ASA cannot achieve the desired therapeutic effects. Therefore, patients have to undergo therapies that include corticosteroids, monoclonal antibodies or immunosuppressants, which are expensive and may be accompanied by significant side effects. Synergistic drug combinations can achieve greater therapeutic effects than individual drugs while contributing to combating drug resistance and lessening toxic side effects. Thus, in this study, we sought to identify synergistic drugs that can act synergistically with 5-ASA. Methods: We started our study with protein-metabolite analysis based on peroxisome proliferator-activated receptor gamma (PPARG), the therapeutic target of 5-ASA, to identify more additional potential drug targets. Then, we further evaluated the possibility of their synergy with PPARG by integrating Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis, pathway-pathway interaction analysis, and semantic similarity analysis. Finally, we validated the synergistic effects with in vitro and in vivo experiments. Results: The combination of 5-ASA and vorinostat (SAHA) showed lower toxicity and mRNA expression of p65 in human colonic epithelial cell lines (Caco-2 and HCT-116), and more efficiently alleviated the symptoms of dextran sulfate sodium (DSS)-induced colitis than treatment with 5-ASA and SAHA alone. Conclusion: SAHA can exert effective synergistic effects with 5-ASA in the treatment of UC. One possible mechanism of synergism may be synergistic inhibition of the nuclear factor kappa B (NF-kB) signaling pathway. Moreover, the metabolite-butyric acid may be involved.

Boosting Detection of Low-Abundance Proteins in Thermal Proteome Profiling Experiments by Addition of an Isobaric Trigger Channel to TMT Multiplexes

The study of low-abundance proteins is a challenge to discovery-based proteomics. Mass spectrometry (MS) applications, such as thermal proteome profiling (TPP), face specific challenges in the detection of the whole proteome as a consequence of the use of nondenaturing extraction buffers. TPP is a powerful method for the study of protein thermal stability, but quantitative accuracy is highly dependent on consistent detection. Therefore, TPP can be limited in its amenability to study low-abundance proteins that tend to have stochastic or poor detection by MS. To address this challenge, we incorporated an affinity-purified protein complex sample at submolar concentrations as an isobaric trigger channel into a mutant TPP (mTPP) workflow to provide reproducible detection and quantitation of the low-abundance subunits of the cleavage and polyadenylation factor (CPF) complex. The inclusion of an isobaric protein complex trigger channel increased detection an average of 40× for previously detected subunits and facilitated detection of CPF subunits that were previously below the limit of detection. Importantly, these gains in CPF detection did not cause large changes in melt temperature (Tm) calculations for other unrelated proteins in the samples, with a high positive correlation between Tm estimates in samples with and without isobaric trigger channel addition. Overall, the incorporation of an affinity-purified protein complex as an isobaric trigger channel within a tandem mass tag (TMT) multiplex for mTPP experiments is an effective and reproducible way to gather thermal profiling data on proteins that are not readily detected using the original TPP or mTPP protocols.

Rtpca: an R package for differential thermal proximity coaggregation analysis

Summary

Rtpca is an R package implementing methods for inferring protein–protein interactions (PPIs) based on thermal proteome profiling experiments of a single condition or in a differential setting via an approach called thermal proximity coaggregation. It offers user-friendly tools to explore datasets for their PPI predictive performance and easily integrates with available R packages.

Availability and implementation

Rtpca is available from Bioconductor (https://bioconductor.org/packages/Rtpca).

Supplementary information

Supplementary data are available at Bioinformatics online.

Network-based modeling of herb combinations in traditional Chinese medicine

Traditional Chinese medicine (TCM) has been practiced for thousands of years for treating human diseases. In comparison to modern medicine, one of the advantages of TCM is the principle of herb compatibility, known as TCM formulae. A TCM formula usually consists of multiple herbs to achieve the maximum treatment effects, where their interactions are believed to elicit the therapeutic effects. Despite being a fundamental component of TCM, the rationale of combining specific herb combinations remains unclear. In this study, we proposed a network-based method to quantify the interactions in herb pairs. We constructed a protein–protein interaction network for a given herb pair by retrieving the associated ingredients and protein targets, and determined multiple network-based distances including the closest, shortest, center, kernel, and separation, both at the ingredient and at the target levels. We found that the frequently used herb pairs tend to have shorter distances compared to random herb pairs, suggesting that a therapeutic herb pair is more likely to affect neighboring proteins in the human interactome. Furthermore, we found that the center distance determined at the ingredient level improves the discrimination of top-frequent herb pairs from random herb pairs, suggesting the rationale of considering the topologically important ingredients for inferring the mechanisms of action of TCM. Taken together, we have provided a network pharmacology framework to quantify the degree of herb interactions, which shall help explore the space of herb combinations more effectively to identify the synergistic compound interactions based on network topology.

Inflect: Optimizing Computational Workflows for Thermal Proteome Profiling Data Analysis

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein-ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

Towards a mechanistic understanding of reciprocal drug-microbiome interactions

Broad-spectrum antibiotics target multiple gram-positive and gram-negative bacteria, and can collaterally damage the gut microbiota. Yet, our knowledge of the extent of damage, the antibiotic activity spectra, and the resistance mechanisms of gut microbes is sparse. This limits our ability to mitigate microbiome-facilitated spread of antibiotic resistance. In addition to antibiotics, non-antibiotic drugs affect the human microbiome, as shown by metagenomics as well as in vitro studies. Microbiome-drug interactions are bidirectional, as microbes can also modulate drugs. Chemical modifications of antibiotics mostly function as antimicrobial resistance mechanisms, while metabolism of non-antibiotics can also change the drugs' pharmacodynamic, pharmacokinetic, and toxic properties. Recent studies have started to unravel the extensive capacity of gut microbes to metabolize drugs, the mechanisms, and the relevance of such events for drug treatment. These findings raise the question whether and to which degree these reciprocal drug-microbiome interactions will differ across individuals, and how to take them into account in drug discovery and precision medicine. This review describes recent developments in the field and discusses future study areas that will benefit from systems biology approaches to better understand the mechanistic role of the human gut microbiota in drug actions.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

Chemoproteomic-enabled phenotypic screening

The ID of disease-modifying, chemically accessible targets remains a central priority of modern therapeutic discovery. The phenotypic screening of small-molecule libraries not only represents an attractive approach to identify compounds that may serve as drug leads but also serves as an opportunity to uncover compounds with novel mechanisms of action (MoAs). However, a major bottleneck of phenotypic screens continues to be the ID of pharmacologically relevant target(s) for compounds of interest. The field of chemoproteomics aims to map proteome-wide small-molecule interactions in complex, native systems, and has proved a key technology to unravel the protein targets of pharmacological modulators. In this review, we discuss the application of modern chemoproteomic methods to identify protein targets of phenotypic screening hits and investigate MoAs, with a specific focus on the development of chemoproteomic-enabled compound libraries to streamline target discovery.

Recent advances in proteome-wide label-free target deconvolution for bioactive small molecules

Small-molecule drugs modulate biological processes and disease states through engagement of target proteins in cells. Assessing drug-target engagement on a proteome-wide scale is of utmost importance in better understanding the molecular mechanisms of action of observed beneficial and adverse effects, as well as in developing next generation tool compounds and drugs with better efficacies and specificities. However, systematic assessment of drug-target engagement has been an arduous task. With the continuous development of mass spectrometry-based proteomics instruments and techniques, various chemical proteomics approaches for drug target deconvolution (i.e., the identification of molecular target for drugs) have emerged. Among these, the label-free target deconvolution approaches that do not involve the chemical modification of compounds of interest, have gained increased attention in the community. Here we provide an overview of the basic principles and recent biological applications of the most important label-free methods including the cellular thermal shift assay, pulse proteolysis, chemical denaturant and protein precipitation, stability of proteins from rates of oxidation, drug affinity responsive target stability, limited proteolysis, and solvent-induced protein precipitation. The state-of-the-art technical implications and future outlook for the label-free approaches are also discussed.

SARS-CoV-2 infection remodels the host protein thermal stability landscape

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global threat to human health and has compromised economic stability. In addition to the development of an effective vaccine, it is imperative to understand how SARS-CoV-2 hijacks host cellular machineries on a system-wide scale so that potential host-directed therapies can be developed. In situ proteome-wide abundance and thermal stability measurements using thermal proteome profiling (TPP) can inform on global changes in protein activity. Here we adapted TPP to high biosafety conditions amenable to SARS-CoV-2 handling. We discovered pronounced temporal alterations in host protein thermostability during infection, which converged on cellular processes including cell cycle, microtubule and RNA splicing regulation. Pharmacological inhibition of host proteins displaying altered thermal stability or abundance during infection suppressed SARS-CoV-2 replication. Overall, this work serves as a framework for expanding TPP workflows to globally important human pathogens that require high biosafety containment and provides deeper resolution into the molecular changes induced by SARS-CoV-2 infection.

Mass spectrometry-based protein‒protein interaction techniques and their applications in studies of DNA damage repair

Proteins are major functional units that are tightly connected to form complex and dynamic networks. These networks enable cells and organisms to operate properly and respond efficiently to environmental cues. Over the past decades, many biochemical methods have been developed to search for protein-binding partners in order to understand how protein networks are constructed and connected. At the same time, rapid development in proteomics and mass spectrometry (MS) techniques makes it possible to identify interacting proteins and build comprehensive protein‒protein interaction networks. The resulting interactomes and networks have proven informative in the investigation of biological functions, such as in the field of DNA damage repair. In recent years, a number of proteins involved in DNA damage response and DNA repair pathways have been uncovered with MS-based protein‒protein interaction studies. As the technologies for enriching associated proteins and MS become more sophisticated, the studies of protein‒protein interactions are entering a new era. In this review, we summarize the strategies and recent developments for exploring protein‒protein interaction. In addition, we discuss the application of these tools in the investigation of protein‒protein interaction networks involved in DNA damage response and DNA repair.

The functional proteome landscape of Escherichia coli

Recent developments in high-throughput reverse genetics^1,2 have revolutionized our ability to map gene function and interactions^3–6. The power of these approaches lies on their ability to discover functionally-associated genes, which elicit similar phenotypic changes across multiple perturbations (chemical, environmental, or genetic) when knocked out^7–9. However, due to the large number of perturbations, these approaches have been limited to growth or morphological readouts¹⁰. Here, we have used a high-content biochemical readout, thermal proteome profiling¹¹, to measure proteome-wide abundance and thermal stability of 121 genetic perturbations in Escherichia coli. We observed that thermal stability, and therefore the state and interactions of essential proteins is commonly modulated, opening up the possibility to study a protein group that is particularly inaccessible to genetics. We show that functionally-associated proteins have coordinated abundance and thermal stability changes across perturbations, due to their co-regulation and physical interactions (with proteins, metabolites, or co-factors). Finally, we provide mechanistic insights into previously determined growth phenotypes¹² that go beyond the deleted gene. These data, available at http://ecoliTPP.shiny.embl.de, represent a rich resource for inferring protein functions and interactions.

Proposing a minimal set of metrics and methods to predict probabilities of amyloidosis disease and onset age in individuals

Many amyloid-driven pathologies have both genetic and stochastic components where assessing risk of disease development requires a multifactorial assessment where many of the variables are poorly understood. Risk of transthyretin-mediated amyloidosis is enhanced by age and mutation of the transthyretin (TTR) gene, but amyloidosis is not directly initiated by mutated TTR proteins. Nearly all of the 150+ known mutations increase dissociation of the homotetrameric protein structure and increase the probability of an individual developing a TTR amyloid disease late in life. TTR amyloidosis is caused by dissociated monomers that are destabilized and refold into an amyloidogenic form. Therefore, monomer concentration, monomer proteolysis rate, and structural stability are key variables that may determine the rate of development of amyloidosis. Here we develop a unifying biophysical model that quantifies the relationships among these variables in plasma and suggest the probability of an individual developing a TTR amyloid disease can be estimated. This may allow quantification of risk for amyloidosis and provide the information necessary for development of methods for early diagnosis and prevention. Given the similar observation of genetic and sporadic amyloidoses for other diseases, this model and the measurements to assess risk may be applicable to more proteins than just TTR.

A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. However, current data analysis strategies use preset thresholds that can lead to suboptimal sensitivity/specificity tradeoffs and limited comparability across datasets. Here, we present a method based on statistical hypothesis testing on curves, which provides control of the false discovery rate. We apply it to several datasets probing epigenetic drugs and a metabolite. This leads us to detect off-target drug engagement, including the finding that the HDAC8 inhibitor PCI-34051 and its analog BRD-3811 bind to and inhibit leucine aminopeptidase 3. An implementation is available as an R package from Bioconductor (https://bioconductor.org/packages/TPP2D). We hope that our method will facilitate prioritizing targets from thermal profiling experiments.

2D-thermal proteome profiling (2D-TPP) is a powerful assay for probing interactions of proteins with small molecules in their native context. Here the authors provide a statistical method for false discovery rate controlled analysis for 2D-TPP applications.

Thermal Proteome Profiling and Meltome Analysis of a Thermophilic Bacterial Strain, Geobacillus thermoleovorans ARTRW1: Toward Industrial Applications

Thermophilic microorganisms that thrive in extreme environments are of great importance because they express heat-resistant enzymes with the potential to serve as biocatalysts in industrial applications. Thermal proteome profiling (TPP) is a multiplexed quantitative mass spectrometry method for analyses of structural information and melting behavior of thousands of proteins, simultaneously determining the thermal denaturation profiles of each protein. We report, in this study, TPP applied to a thermophilic bacterial proteome, a recently isolated strain of Geobacillus thermoleovorans named as ARTRW1. The proteome was investigated in terms of thermostable enzymes that are relevant to industrial applications. In this study, we present the thermostability profiles of its 868 proteins. The majority of G. thermoleovorans proteome was observed to melt between 62.5°C and 72°C, with melting point (T_m) mean value of 68.1°C ± 6.6°C. Unfolding characteristics of several enzymes, including amylase, protease, and lipase, were demonstrated which are highly informative in terms of their applicability to specific industrial processes. A significant correlation was observed between protein melting temperature and the structural features such as molecular weight and abundance, whereas correlations were modest or weak in relation to the α-helix structure percentages. Taken together, we demonstrated a system-wide melting profile analysis of a thermal proteome and listed proteins with elevated T_m values that are highly promising for applications in medicine, food engineering, and cosmetics in particular. The extracted T_m values were found similar to those obtained by biophysical methods applied to purified proteins. TPP analysis has significant industrial and biomedical potentials to accelerate thermophilic enzyme research and innovation.

Overcoming cancer therapeutic bottleneck by drug repurposing

Ever present hurdles for the discovery of new drugs for cancer therapy have necessitated the development of the alternative strategy of drug repurposing, the development of old drugs for new therapeutic purposes. This strategy with a cost-effective way offers a rare opportunity for the treatment of human neoplastic disease, facilitating rapid clinical translation. With an increased understanding of the hallmarks of cancer and the development of various data-driven approaches, drug repurposing further promotes the holistic productivity of drug discovery and reasonably focuses on target-defined antineoplastic compounds. The "treasure trove" of non-oncology drugs should not be ignored since they could target not only known but also hitherto unknown vulnerabilities of cancer. Indeed, different from targeted drugs, these old generic drugs, usually used in a multi-target strategy may bring benefit to patients. In this review, aiming to demonstrate the full potential of drug repurposing, we present various promising repurposed non-oncology drugs for clinical cancer management and classify these candidates into their proposed administration for either mono- or drug combination therapy. We also summarize approaches used for drug repurposing and discuss the main barriers to its uptake.

Thermal proteome profiling for interrogating protein interactions

Thermal proteome profiling (TPP) is based on the principle that, when subjected to heat, proteins denature and become insoluble. Proteins can change their thermal stability upon interactions with small molecules (such as drugs or metabolites), nucleic acids or other proteins, or upon post-translational modifications. TPP uses multiplexed quantitative mass spectrometry-based proteomics to monitor the melting profile of thousands of expressed proteins. Importantly, this approach can be performed in vitro, in situ, or in vivo. It has been successfully applied to identify targets and off-targets of drugs, or to study protein-metabolite and protein-protein interactions. Therefore, TPP provides a unique insight into protein state and interactions in their native context and at a proteome-wide level, allowing to study basic biological processes and their underlying mechanisms.