Multi-epitope immunocapture of huntingtin reveals striatum-selective molecular signatures

Huntington's disease (HD) is a debilitating neurodegenerative disorder affecting an individual's cognitive and motor abilities. HD is caused by a mutation in the huntingtin gene producing a toxic polyglutamine-expanded protein (mHTT) and leading to degeneration in the striatum and cortex. Yet, the molecular signatures that underlie tissue-specific vulnerabilities remain unclear. Here, we investigate this aspect by leveraging multi-epitope protein interaction assays, subcellular fractionation, thermal proteome profiling, and genetic modifier assays. The use of human cell, mouse, and fly models afforded capture of distinct subcellular pools of epitope-enriched and tissue-dependent interactions linked to dysregulated cellular pathways and disease relevance. We established an HTT association with nearly all subunits of the transcriptional regulatory Mediator complex (20/26), with preferential enrichment of MED15 in the tail domain. Using HD and KO models, we find HTT modulates the subcellular localization and assembly of the Mediator. We demonstrated striatal enriched and functional interactions with regulators of calcium homeostasis and chromatin remodeling, whose disease relevance was supported by HD fly genetic modifiers assays. Altogether, we offer insights into tissue- and localization-dependent (m)HTT functions and pathobiology.

Applications of the Cellular Thermal Shift Assay to Drug Discovery in Natural Products: A Review

Natural products play a crucial role in drug discovery because of their structural diversity and biological activity. However, identifying their molecular targets remains a challenge. Traditional target identification approaches such as affinity-based protein profiling and activity-based protein profiling are limited by the need for chemical modification or reactive groups in natural products. The emergence of label-free techniques offers a powerful alternative for studying drug-target engagement in a physiological context. In particular, the cellular thermal shift assay (CETSA) exploits ligand-induced protein stabilization-a phenomenon where ligand binding enhances a protein's thermal stability by reducing conformational flexibility-to assess drug binding without requiring chemical modifications. CETSA's integration with advanced mass spectrometry and high-throughput platforms has dramatically expanded proteome coverage and sensitivity, enabling the simultaneous quantification of thousands of proteins and the identification of low-abundance targets in native cellular environments. This review highlights the application of key CETSA-based methods to target identification in natural products including Western blot-based CETSA, isothermal dose-response CETSA, mass spectrometry-based CETSA, and high-throughput CETSA. Case studies are presented that demonstrate their effectiveness in uncovering the mechanisms of action of different drugs. The current limitations of CETSA-based strategies are also explored, and future improvements to optimize their potential for drug discovery are discussed. Integrating CETSA with complementary approaches can enhance the target identification accuracy and efficiency for natural products and ultimately advance development of therapeutic applications.

Thermal Proteome Profiling Reveals Meltome Upon NLRP3 Inflammasome Activation

NOD-like receptor (NLR) family pyrin domain containing 3 (NLRP3) involves in inflammasome complex assembly and innate immunity. Activation of the NLRP3 inflammasome induces conformational alterations in protein complexes, influencing their interactions with other molecules, which in turn affects protein thermal stability. To investigate the proteome-wide thermal stability alterations induced by NLRP3 inflammasome activation, we conducted a comprehensive analysis of meltome dynamics using thermal proteome profiling. Our analysis identified 337 proteins exhibiting alterations in thermal stability upon NLRP3 inflammasome activation. Subsequently, we validated three proteins by the cellular thermal shift assay. Notably, our findings reveal that the majority of these proteins tend to cluster into distinct macromolecular complexes. Furthermore, we identified FAM120A as a novel NLRP3 binding partner, with its suppression enhancing caspase-1 activation and IL-1β release in response to NLRP3 agonist. Collectively, these data provide a comprehensive framework for understanding the mechanisms of NLRP3 inflammasome activation and underscore the utility of thermal proteome profiling in exploring proteome-wide thermal stability changes during signaling transduction.

TargetSeeker-MS: A Bayesian Inference Approach for Drug-Target Discovery Using Protein Fractionation Coupled to Mass Spectrometry

To understand the mechanism of action of a drug and assess its clinical usefulness and viability, it is imperative that its affinity for its putative targets is determined. When coupled to mass spectrometry (MS), energetics-based protein separation (EBPS) techniques, such as a thermal shift assay, have shown great potential to identify the targets of a drug on a proteome scale. Nevertheless, the computational analyses assessing the confidence of drug-target predictions made by these methods have remained tightly tied to the protocol under which the data were produced. To identify drug targets in data sets produced using different EBPS-MS techniques, we have developed a novel flexible Bayesian inference approach named TargetSeeker-MS. We showed that TargetSeeker-MS identifies known and novel drug targets in Caenorhabditis elegans and HEK 293 samples treated with the fungicide benomyl. We also demonstrated that TargetSeeker-MS' drug-target identifications are reproducible in C. elegans samples that were processed using two different EBPS techniques (thermal shift assay and a differential precipitation of proteins, named DiffPOP). In addition, we validated a novel benomyl target by measuring its altered enzymatic activity upon drug treatment in vitro. TargetSeeker-MS, which is available as a web server (https://targetseeker.scripps.edu/), allows for the rapid, versatile, and confident identification of targets of a drug on a proteome scale, thereby providing a better understanding of its mechanisms and facilitating the evaluation of its clinical viability.

Structural determinants of co-translational protein complex assembly

Protein assembly into functional complexes is critical to life's processes. While complex assembly is classically described as occurring between fully synthesized proteins, recent work showed that co-translational assembly is prevalent in human cells. However, the biological basis for the existence of this process and the identity of protein pairs that assemble co-translationally remain unknown. We show that co-translational assembly is governed by structural characteristics of complexes and involves mutually stabilized subunits. Accordingly, co-translationally assembling subunits are unstable in isolation and exhibit synchronized proteostasis with their partner. By leveraging structural signatures and AlphaFold2-based predictions, we accurately predicted co-translational assembly, including pair identities, at proteome scale and across species. We validated our predictions by ribosome profiling, stoichiometry perturbations, and single-molecule RNA-fluorescence in situ hybridization (smFISH) experiments that revealed co-localized mRNAs. This work establishes a fundamental connection between protein structure and the translation process, highlighting the overarching impact of three-dimensional structure on gene expression, mRNA localization, and proteostasis.

Global analysis of endogenous protein disorder in cells

Disorder and flexibility in protein structures are essential for biological function but can also contribute to diseases, such as neurodegenerative disorders. However, characterizing protein folding on a proteome-wide scale within biological matrices remains challenging. Here we present a method using a bifunctional chemical probe, named TME, to capture in situ, enrich and quantify endogenous protein disorder in cells. TME exhibits a fluorescence turn-on effect upon selective conjugation with proteins with free cysteines in surface-exposed and flexible environments-a distinctive signature of protein disorder. Using an affinity-based proteomic approach, we identify both basal disordered proteins and those whose folding status changes under stress, with coverage to proteins even of low abundance. In lymphoblastoid cells from individuals with Parkinson's disease and healthy controls, our TME-based strategy distinguishes the two groups more effectively than lysate profiling methods. High-throughput TME fluorescence and proteomics further reveal a universal cellular quality-control mechanism in which cells adapt to proteostatic stress by adopting aggregation-prone distributions and sequestering disordered proteins, as illustrated in Huntington's disease cell models.

Studying Target-Engagement of Anti-Infectives by Solvent-Induced Protein Precipitation and Quantitative Mass Spectrometry

Antimicrobial resistance (AMR) poses a serious threat to global health. The rapid emergence of resistance contrasts with the slow pace of antimicrobial development, emphasizing the urgent need for innovative drug discovery approaches. This study addresses a critical bottleneck in early drug development by introducing integral solvent-induced protein precipitation (iSPP) to rapidly assess the target-engagement of lead compounds in extracts of pathogenic microorganisms under close-to-physiological conditions. iSPP measures the change in protein stability against solvent-induced precipitation in the presence of ligands. The iSPP method for bacteria builds upon established SPP procedures and features optimized denaturation gradients and minimized sample input amounts. The effectiveness of the iSPP workflow was initially demonstrated through a multidrug target-engagement study. Using quantitative mass spectrometry (LC-MS/MS), we successfully identified known drug targets of seven different antibiotics in cell extracts of four AMR-related pathogens: the three Gram-negative bacteria Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and the Gram-positive bacterium Staphylococcus aureus. The iSPP method was ultimately applied to demonstrate target-engagement of compounds derived from target-based drug discovery. We employed five small molecules targeting three enzymes in the 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway─a promising focus for anti-infective drug development. The study showcases iSPP adaptability and efficiency in identifying anti-infective drug targets, advancing early-stage drug discovery against AMR.

Spike-In Proteome Enhances Data-Independent Acquisition for Thermal Proteome Profiling

Target deconvolution is essential for elucidating the molecular mechanisms, therapeutic efficacy, and off-target toxicity of small-molecule drugs. Thermal proteome profiling (TPP) is a robust and popular method for identifying drug-protein interactions. Nevertheless, classical implementation of TPP using isobaric labeling of peptides is tedious, time-consuming, and costly. This prompts the adoption of a label-free approach with data-independent acquisition (DIA), but with substantial compromise in protein coverage and precision. To address these shortcomings, we improvised a spike-in proteome strategy for DIA with TPP to counteract the reduction in protein quantity following sample heating. Protein coverage, data completeness, and quantification precision are significantly improved as result. Additionally, a calibration algorithm was developed to correct for spike-in effects on fold changes. The integration of DIA-TPP with the matrix-augmented pooling strategy (MAPS) to increase experiment throughput demonstrates performance comparable to that of existing TMT-TPP-MAPS. With this spike-in proteome strategy, we also successfully identified the thermal stabilization of CA13 by dorzolamide hydrochloride as well as GSTZ1 and tyrosyl-DNA phosphodiesterase 1 of opicapone that eluded detection without spike-in proteome.

One-Pot Time-Induced Proteome Integral Solubility Alteration Assay for Automated and Sensitive Drug-Target Identification

The proteome integral solubility alteration (PISA) assay is widely used for identifying drug targets, but it is labor-intensive and time-consuming and requires a substantial amount of biological sample. Aiming at enabling automation and greatly reducing the sample amount, we developed one-pot time-induced (OPTI)-PISA. Here, we demonstrate OPTI-PISA performance on identifying targets of multiple drugs in cell lysate and scaling down the sample amount to sub-microgram levels, making the PISA method suitable for NanoProteomics. OPTI-PISA can be implemented using only the standard equipment of a proteomics lab.

Characterizing protein-protein interactions with thermal proteome profiling

Thermal proteome profiling (TPP) is an innovative technique that uses the principle of protein thermal stability to identify potential protein interaction partners. Employing quantitative mass spectrometry, TPP measures protein stability across the proteome, offering a comprehensive snapshot of protein interactions in a single experiment. When studying protein-protein interactions (PPI), TPP leverages changes in apparent protein melting temperatures to identify transient and weak interactions that most traditional PPI detection methodologies struggle to measure. This review discusses current TPP methodologies, the challenges of interpreting the resulting complex datasets, and opportunities to deepen and improve PPI networks. By advancing our grasp of intricate protein interactions, TPP promises to illuminate the molecular basis of diseases and drive the discovery of novel therapeutic targets.

Advancing Native Mass Spectrometry Toward Cellular Biology

Protein structure, including various post-translational modifications and higher-order structures, regulates diverse biological functions. Native mass spectrometry (native MS) is a powerful analytical technique used to determine the masses of biomolecules, such as proteins and their complexes, while preserving their native folding in solution. This method provides structural information on the composition of monomers or complexes and the stoichiometry of subunits within each complex, significantly contributing to protein structural analysis. Native MS has evolved to incorporate top-down approaches, enabling the characterization of proteoforms and non-covalent interactions between metabolites or proteins and specific targets. This perspective highlights the advancements in native MS for intracellular proteins and protein complexes, and discusses future research directions toward cellular biology.

Advances in spatial proteomics: Mapping proteome architecture from protein complexes to subcellular localizations

Proteins are responsible for most intracellular functions, which they perform as part of higher-order molecular complexes, located within defined subcellular niches. Localization is both dynamic and context specific and mislocalization underlies a multitude of diseases. It is thus vital to be able to measure the components of higher-order protein complexes and their subcellular location dynamically in order to fully understand cell biological processes. Here, we review the current range of highly complementary approaches that determine the subcellular organization of the proteome. We discuss the scale and resolution at which these approaches are best employed and the caveats that should be taken into consideration when applying them. We also look to the future and emerging technologies that are paving the way for a more comprehensive understanding of the functional roles of protein isoforms, which is essential for unraveling the complexities of cell biology and the development of disease treatments.

Cellular thermal shift assay: an approach to identify and assess protein target engagement

INTRODUCTION

A comprehensive and global knowledge of protein target engagement is of vital importance for mechanistic studies and in drug development. Since its initial introduction, the cellular thermal shift assay (CETSA) has proven to be a reliable and flexible technique that can be widely applied to multiple contexts and has profound applications in facilitating the identification and assessment of protein target engagement.

AREAS COVERED

This review introduces the principle of CETSA, elaborates on western blot-based CETSA and MS-based thermal proteome profiling (TPP) as well as the major applications and prospects of these approaches.

EXPERT OPINION

CETSA primarily evaluates a given ligand binding to a particular target protein in cells and tissues with the protein thermal stabilities analyzed by western blot. When coupling mass spectrometry with CETSA, thermal proteome profiling allows simultaneous proteome-wide experiment that greatly increased the efficiency of target engagement evaluation, and serves as a promising strategy to identify protein targets and off-targets as well as protein-protein interactions to uncover the biological effects. The CETSA approaches have broad applications and potentials in drug development and clinical research.

Exploring the anticancer mechanism of cardiac glycosides using proteome integral solubility alteration approach

BACKGROUND AND AIMS

Cardiac glycosides (CGs), traditionally used for heart failure, have shown potential as anti-cancer agents. This study aims to explore their multifaceted mechanisms in cancer cell biology using proteome integral solubility alteration (PISA), focusing on the interaction with key proteins implicated in cellular metabolism and mitochondrial function.

METHODS

We conducted lysate-based and intact-cell PISA assays on cancer cells treated with CGs (Digoxin, Digitoxin, Ouabain) to analyze protein solubility changes. This was followed by mass spectrometric analysis and bioinformatics to identify differentially soluble proteins (DSPs). Molecular docking simulations were performed to predict protein-CG interactions. Public data including gene expression changes upon CG treatment were re-analyzed for validation.

RESULTS

The PISA assays revealed CGs' broad-spectrum interactions, particularly affecting proteins like PKM2, ANXA2, SLC16A1, GOT2 and GLUD1. Molecular docking confirmed stable interactions between CGs and these DSPs. Re-analysis of public data supported the impact of CGs on cancer metabolism and cell signaling pathways.

CONCLUSION

Our findings suggest that CGs could be repurposed for cancer therapy by modulating cellular processes. The PISA data provide insights into the polypharmacological effects of CGs, warranting further exploration of their mechanisms and clinical potential.

Differential Contributions of Interferon Classes to Host Inflammatory Responses and Restricting Virus Progeny Production

Fundamental to mammalian intrinsic and innate immune defenses against pathogens is the production of Type I and Type II interferons, such as IFN-β and IFN-γ, respectively. The comparative effects of IFN classes on the cellular proteome, protein interactions, and virus restriction within cell types that differentially contribute to immune defenses are needed for understanding immune signaling. Here, a multilayered proteomic analysis, paired with biochemical and molecular virology assays, allows distinguishing host responses to IFN-β and IFN-γ and associated antiviral impacts during infection with several ubiquitous human viruses. In differentiated macrophage-like monocytic cells, we classified proteins upregulated by IFN-β, IFN-γ, or pro-inflammatory LPS. Using parallel reaction monitoring, we developed a proteotypic peptide library for shared and unique ISG signatures of each IFN class, enabling orthogonal confirmation of protein alterations. Thermal proximity coaggregation analysis identified the assembly and maintenance of IFN-induced protein interactions. Comparative proteomics and cytokine responses in macrophage-like monocytic cells and primary keratinocytes provided contextualization of their relative capacities to restrict virus production during infection with herpes simplex virus type-1, adenovirus, and human cytomegalovirus. Our findings demonstrate how IFN classes induce distinct ISG abundance and interaction profiles that drive antiviral defenses within cell types that differentially coordinate mammalian immune responses.

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

Thermal Proteome Profiling Reveals Insight to Antiproliferative and Pro-Apoptotic Effects of Lagunamide A in the Modulation of DNA Damage Repair

Lagunamide A is a biologically active natural product with a yet unidentified molecular mode of action. Cellular studies revealed that lagunamide A is a potent inhibitor of cancer cell proliferation, promotes apoptosis and causes mitochondrial dysfunction. To decipher the cellular mechanism responsible for these effects, we utilized thermal protein profiling (TPP) and identified EYA3 as a stabilized protein in cells upon lagunamide A treatment. EYA3, involved in the DNA damage repair process, was functionally investigated via siRNA based knockdown studies and corresponding effects of lagunamide A on DNA repair were confirmed. Furthermore, we showed that lagunamide A sensitized tumor cells to treatment with the drug doxorubicin highlighting a putative therapeutic strategy.

Discovery of metal-binding proteins by thermal proteome profiling

Metal-binding proteins (MBPs) have various and important biological roles in all living species and many human diseases are intricately linked to dysfunctional MBPs. Here, we report a chemoproteomic method named 'metal extraction-triggered agitation logged by thermal proteome profiling' (METAL-TPP) to globally profile MBPs in proteomes. The method involves the extraction of metals from MBPs using chelators and monitoring the resulting protein stability changes through thermal proteome profiling. Applying METAL-TPP to the human proteome with a broad-spectrum chelator, EDTA, revealed a group of proteins with reduced thermal stability that contained both previously known MBPs and currently unannotated MBP candidates. Biochemical characterization of one potential target, glutamine-fructose-6-phosphate transaminase 2 (GFPT2), showed that zinc bound the protein, inhibited its enzymatic activity and modulated the hexosamine biosynthesis pathway. METAL-TPP profiling with another chelator, TPEN, uncovered additional MBPs in proteomes. Collectively, this study developed a robust tool for proteomic discovery of MBPs and provides a rich resource for functional studies of metals in cell biology.

Systematic identification of structure-specific protein-protein interactions

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

IB-DNQ and Rucaparib dual treatment alters cell cycle regulation and DNA repair in triple negative breast cancer cells

Background

Triple negative breast cancer (TNBC), characterized by the lack of three canonical receptors, is unresponsive to commonly used hormonal therapies. One potential TNBC-specific therapeutic target is NQO1, as it is highly expressed in many TNBC patients and lowly expressed in non-cancer tissues. DNA damage induced by NQO1 bioactivatable drugs in combination with Rucaparib-mediated inhibition of PARP1-dependent DNA repair synergistically induces cell death.

Methods

To gain a better understanding of the mechanisms behind this synergistic effect, we used global proteomics, phosphoproteomics, and thermal proteome profiling to analyze changes in protein abundance, phosphorylation and protein thermal stability.

Results

Very few protein abundance changes resulted from single or dual agent treatment; however, protein phosphorylation and thermal stability were impacted. Histone H2AX was among several proteins identified to have increased phosphorylation when cells were treated with the combination of IB-DNQ and Rucaparib, validating that the drugs induced persistent DNA damage. Thermal proteome profiling revealed destabilization of H2AX following combination treatment, potentially a result of the increase in phosphorylation. Kinase substrate enrichment analysis predicted altered activity for kinases involved in DNA repair and cell cycle following dual agent treatment. Further biophysical analysis of these two processes revealed alterations in SWI/SNF complex association and tubulin / p53 interactions.

Conclusions

Our findings that the drugs target DNA repair and cell cycle regulation, canonical cancer treatment targets, in a way that is dependent on increased expression of a protein selectively found to be upregulated in cancers without impacting protein abundance illustrate that multi-omics methodologies are important to gain a deeper understanding of the mechanisms behind treatment induced cancer cell death.

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

DeKinomics pulse-chases kinase functions in living cells

Cellular context is crucial for understanding the complex and dynamic kinase functions in health and disease. Systematic dissection of kinase-mediated cellular processes requires rapid and precise stimulation ('pulse') of a kinase of interest, as well as global and in-depth characterization ('chase') of the perturbed proteome under living conditions. Here we developed an optogenetic 'pulse-chase' strategy, termed decaging kinase coupled proteomics (DeKinomics), for proteome-wide profiling of kinase-driven phosphorylation at second-timescale in living cells. We took advantage of the 'gain-of-function' feature of DeKinomics to identify direct kinase substrates and further portrayed the global phosphorylation of understudied receptor tyrosine kinases under native cellular settings. DeKinomics offered a general activation-based strategy to study kinase functions with high specificity and temporal resolution under living conditions.

Selective targeting of Plasmodium falciparum Hsp90 disrupts the 26S proteasome

The molecular chaperone heat shock protein 90 (Hsp90) has an essential but largely undefined role in maintaining proteostasis in Plasmodium falciparum, the most lethal malaria parasite. Herein, we identify BX-2819 and XL888 as potent P. falciparum (Pf)Hsp90 inhibitors. Derivatization of XL888's scaffold led to the development of Tropane 1, as a PfHsp90-selective binder with nanomolar affinity. Hsp90 inhibitors exhibit anti-Plasmodium activity against the liver, asexual blood, and early gametocyte life stages. Thermal proteome profiling was implemented to assess PfHsp90-dependent proteome stability, and the proteasome-the main site of cellular protein recycling-was enriched among proteins with perturbed stability upon PfHsp90 inhibition. Subsequent biochemical and cellular studies suggest that PfHsp90 directly promotes proteasome hydrolysis by chaperoning the active 26S complex. These findings expand our knowledge of the PfHsp90-dependent proteome and protein quality control mechanisms in these pathogenic parasites, as well as further characterize this chaperone as a potential antimalarial drug target.

Stability-based approaches in chemoproteomics

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition

Complex disease phenotypes often span multiple molecular processes. Functional characterization of these processes can shed light on disease mechanisms and drug effects. Thermal Proteome Profiling (TPP) is a mass-spectrometry (MS) based technique assessing changes in thermal protein stability that can serve as proxies of functional protein changes. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a multi-omics integration framework, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover consequences of Poly (ADP-ribose) polymerase (PARP) inhibition in ovarian cancer cells on cell cycle and DNA damage response as well as interferon and hippo signaling. We found that TPP offers a complementary perspective to other omics data modalities, and that its integration allowed us to obtain a more complete molecular overview of PARP inhibition. We anticipate that this strategy can be used to integrate functional proteomics with other omics to study molecular processes.

IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data

IMPRINTS-CETSA (Integrated Modulation of Protein Interaction States-Cellular Thermal Shift Assay) provides a highly resolved means to systematically study the interactions of proteins with other cellular components, including metabolites, nucleic acids and other proteins, at the proteome level, but no freely available and user-friendly data analysis software has been reported. Here, we report IMPRINTS.CETSA, an R package that provides the basic data processing framework for robust analysis of the IMPRINTS-CETSA data format, from preprocessing and normalization to visualization. We also report an accompanying R package, IMPRINTS.CETSA.app, which offers a user-friendly Shiny interface for analysis and interpretation of IMPRINTS-CETSA results, with seamless features such as functional enrichment and mapping to other databases at a single site. For the hit generation part, the diverse behaviors of protein modulations have been typically segregated with a two-measure scoring method, i.e. the abundance and thermal stability changes. We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app, respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134.

Community cohesion looseness in gene networks reveals individualized drug targets and resistance

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

Advances in Computational Methods for Protein–Protein Interaction Prediction

Protein–protein interactions (PPIs) are pivotal in various physiological processes inside biological entities. Accurate identification of PPIs holds paramount significance for comprehending biological processes, deciphering disease mechanisms, and advancing medical research. Given the costly and labor-intensive nature of experimental approaches, a multitude of computational methods have been devised to enable swift and large-scale PPI prediction. This review offers a thorough examination of recent strides in computational methodologies for PPI prediction, with a particular focus on the utilization of deep learning techniques within this domain. Alongside a systematic classification and discussion of relevant databases, feature extraction strategies, and prominent computational approaches, we conclude with a thorough analysis of current challenges and prospects for the future of this field.

Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts

Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.

High-throughput drug target discovery using a fully automated proteomics sample preparation platform

Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and data independent acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 intra- and inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.

Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space [Formula: see text]. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein-protein interaction prediction.

Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation

Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/ .

Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions

Target deconvolution is a crucial but costly and time-consuming task that hinders large-scale profiling for drug discovery. We present a matrix-augmented pooling strategy (MAPS) which mixes multiple drugs into samples with optimized permutation and delineates targets of each drug simultaneously with mathematical processing. We validated this strategy with thermal proteome profiling (TPP) testing of 15 drugs concurrently, increasing experimental throughput by 60x while maintaining high sensitivity and specificity. Benefiting from the lower cost and higher throughput of MAPS, we performed target deconvolution of the 15 drugs across 5 cell lines. Our profiling revealed that drug-target interactions can differ vastly in targets and binding affinity across cell lines. We further validated BRAF and CSNK2A2 as potential off-targets of bafetinib and abemaciclib, respectively. This work represents the largest thermal profiling of structurally diverse drugs across multiple cell lines to date.

Genetic validation of PfFKBP35 as an antimalarial drug target

Plasmodium falciparum accounts for the majority of over 600,000 malaria-associated deaths annually. Parasites resistant to nearly all antimalarials have emerged and the need for drugs with alternative modes of action is thus undoubted. The FK506-binding protein PfFKBP35 has gained attention as a promising drug target due to its high affinity to the macrolide compound FK506 (tacrolimus). Whilst there is considerable interest in targeting PfFKBP35 with small molecules, a genetic validation of this factor as a drug target is missing and its function in parasite biology remains elusive. Here, we show that limiting PfFKBP35 levels are lethal to P. falciparum and result in a delayed death-like phenotype that is characterized by defective ribosome homeostasis and stalled protein synthesis. Our data furthermore suggest that FK506, unlike the action of this drug in model organisms, exerts its antiproliferative activity in a PfFKBP35-independent manner and, using cellular thermal shift assays, we identify putative FK506-targets beyond PfFKBP35. In addition to revealing first insights into the function of PfFKBP35, our results show that FKBP-binding drugs can adopt non-canonical modes of action - with major implications for the development of FK506-derived molecules active against Plasmodium parasites and other eukaryotic pathogens.

Chemo-proteomics in antimalarial target identification and engagement

Humans have lived in tenuous battle with malaria over millennia. Today, while much of the world is free of the disease, areas of South America, Asia, and Africa still wage this war with substantial impacts on their social and economic development. The threat of widespread resistance to all currently available antimalarial therapies continues to raise concern. Therefore, it is imperative that novel antimalarial chemotypes be developed to populate the pipeline going forward. Phenotypic screening has been responsible for the majority of the new chemotypes emerging in the past few decades. However, this can result in limited information on the molecular target of these compounds which may serve as an unknown variable complicating their progression into clinical development. Target identification and validation is a process that incorporates techniques from a range of different disciplines. Chemical biology and more specifically chemo-proteomics have been heavily utilized for this purpose. This review provides an in-depth summary of the application of chemo-proteomics in antimalarial development. Here we focus particularly on the methodology, practicalities, merits, and limitations of designing these experiments. Together this provides learnings on the future use of chemo-proteomics in antimalarial development.

Observations and implication of thermal tolerance in the Arabidopsis proteome

Knowledge of the thermal stability of plant proteomes within their native environments would aid in the design of climate-resilient crop plants. Identification of thermo-sensitive and -resilient proteins not only provides foundational understanding of systematic heat-induced damage but also offers insight into protein interactions and protein evolution.

Systematic thermal analysis of the Arabidopsis proteome: Thermal tolerance, organization, and evolution

Understanding the thermal stability of the plant proteome in the context of the native cellular environment would aid the design of crops with high thermal tolerance, but only limited such data are available. Here, we applied quantitative mass spectrometry to profile the thermal stability of the Arabidopsis proteome and identify thermo-sensitive and thermo-resilient protein networks in Arabidopsis, providing a basis for understanding heat-induced damage. We also show that the similarities of the protein-melting curves can be used as a proxy to evaluate system-wide protein-protein interactions in non-engineered plants and enable the identification of transient interactions exhibited by metabolons in the context of the cellular environment. Finally, we report a systematic comparison of the thermal stability of paralogs in Arabidopsis to aid the investigation and understanding of gene duplication and protein evolution. Taken together, our results could have broad implications for the fields of plant thermal tolerance, plant protein assemblies, and evolution.

Global analysis of aging-related protein structural changes uncovers enzyme-polymerization-based control of longevity

Aging is associated with progressive phenotypic changes. Virtually all cellular phenotypes are produced by proteins, and their structural alterations can lead to age-related diseases. However, we still lack comprehensive knowledge of proteins undergoing structural-functional changes during cellular aging and their contributions to age-related phenotypes. Here, we conducted proteome-wide analysis of early age-related protein structural changes in budding yeast using limited proteolysis-mass spectrometry (LiP-MS). The results, compiled in online ProtAge catalog, unraveled age-related functional changes in regulators of translation, protein folding, and amino acid metabolism. Mechanistically, we found that folded glutamate synthase Glt1 polymerizes into supramolecular self-assemblies during aging, causing breakdown of cellular amino acid homeostasis. Inhibiting Glt1 polymerization by mutating the polymerization interface restored amino acid levels in aged cells, attenuated mitochondrial dysfunction, and led to lifespan extension. Altogether, this comprehensive map of protein structural changes enables identifying mechanisms of age-related phenotypes and offers opportunities for their reversal.

Scaled-Down Thermal Profiling and Coaggregation Analysis of the Proteome for Drug Target and Protein Interaction Analysis

Thermal proteome profiling (TPP), an experimental technique combining the cellular thermal shift assay (CETSA) with quantitative protein mass spectrometry (MS), identifies interactions of drugs and chemicals with endogenous proteins. Thermal proximity coaggregation (TPCA) profiling extended TPP to study the intracellular dynamics of protein complexes. In TPP and TPCA, samples are subjected to multiple denaturing temperatures, each requiring over 100 μg of proteins, which restricts their applications for rare cells and precious clinical samples. We developed a workflow termed STASIS (scaled-down thermal profiling and coaggregation analysis with SISPROT) that scales down the required protein to as low as 1 μg per temperature. This is achieved by heating and centrifugation using the same PCR tube, processing samples with the SISPROT technology (simple and integrated spintip-based proteomics technology), and tip-based manual fractionation of TMT-labeled peptides. We evaluate the STASIS workflow with starting protein quantities of 10, 5, and 1 μg per temperature prior to heating, identifying between 4000 and 5000 proteins with 6 h of acquisition time. Importantly, we observed a high correlation in the Tm of proteins with minimal difference in TPCA performance for predicting protein complexes. Moreover, STASIS could identify the targets of methotrexate and panobinostat with high precision with 1 μg of proteins per temperature. In conclusion, STASIS is a robust cost-effective technique for target deconvolution and extended TPCA to rare primary cells and precious clinical samples for the analysis of protein complexes.

Integrated Protein Solubility Shift Assays for Comprehensive Drug Target Identification on a Proteome-Wide Scale

Target proteins are often stabilized after binding with a ligand and thereby typically become more resistant to denaturation. Based on this phenomenon, several methods without the need to covalently modify the ligand have been developed to identify target proteins for a specific ligand. These methods usually employ complicated workflows with high cost and limited throughput. Here, we develop an iso-pH shift assay (ipHSA) method, a proteome-wide target identification method that detects ligand-induced protein solubility shifts by precipitating proteins with a single concentration of acidic agent followed by protein quantification via data-independent acquisition (DIA). Using a pan-kinase inhibitor, staurosporine, we demonstrated that ipHSA increased throughput compared to the previously developed pH-dependent protein precipitation (pHDPP) method. ipHSA was found to have high complementarity in staurosporine target identification compared with the improved isothermal shift assay (iTSA) and isosolvent shift assay (iSSA) using DIA instead of tandem mass tags (TMTs) for quantification. To further improve target identification sensitivity, we developed an integrated protein solubility shift assay (IPSSA) by pooling the supernatants yielded from ipHSA, iTSA, and iSSA methods. IPSSA exhibited increased sensitivity in screening staurosporine targets by 38, 29, and 38% compared to individual methods. Increasing the number of replicate experiments further enhanced the sensitivity of target identification. Meanwhile, IPSSA also improved the throughput and reduced the cost compared with previous methods. As a fast and efficient tool for drug target identification, IPSSA is expected to have broad applications in the study of the mechanism of action.

Could a Non-Cellular Molecular Interactome in the Blood Circulation Influence Pathogens' Infectivity?

We advance the notion that much like artificial nanoparticles, relatively more complex biological entities with nanometric dimensions such as pathogens (viruses, bacteria, and other microorganisms) may also acquire a biomolecular corona upon entering the blood circulation of an organism. We view this biomolecular corona as a component of a much broader non-cellular blood interactome that can be highly specific to the organism, akin to components of the innate immune response to an invading pathogen. We review published supporting data and generalize these notions from artificial nanoparticles to viruses and bacteria. Characterization of the non-cellular blood interactome of an organism may help explain apparent differences in the susceptibility to pathogens among individuals. The non-cellular blood interactome is a candidate therapeutic target to treat infectious and non-infectious conditions.

Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation

Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based 'omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.

CETSA and thermal proteome profiling strategies for target identification and drug discovery of natural products

BACKGROUND

Monitoring target engagement at various stages of drug development is essential for natural product (NP)-based drug discovery and development. The cellular thermal shift assay (CETSA) developed in 2013 is a novel, broadly applicable, label-free biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins, which enables direct assessment of drug-target engagement in physiologically relevant contexts, including intact cells, cell lysates and tissues. This review aims to provide an overview of the work principles of CETSA and its derivative strategies and their recent progress in protein target validation, target identification and drug lead discovery of NPs.

METHODS

A literature-based survey was conducted using the Web of Science and PubMed databases. The required information was reviewed and discussed to highlight the important role of CETSA-derived strategies in NP studies.

RESULTS

After nearly ten years of upgrading and evolution, CETSA has been mainly developed into three formats: classic Western blotting (WB)-CETSA for target validation, thermal proteome profiling (TPP, also known as MS-CETSA) for unbiased proteome-wide target identification, and high-throughput (HT)-CETSA for drug hit discovery and lead optimization. Importantly, the application possibilities of a variety of TPP approaches for the target discovery of bioactive NPs are highlighted and discussed, including TPP-temperature range (TPP-TR), TPP-compound concentration range (TPP-CCR), two-dimensional TPP (2D-TPP), cell surface-TPP (CS-TPP), simplified TPP (STPP), thermal stability shift-based fluorescence difference in 2D gel electrophoresis (TS-FITGE) and precipitate supported TPP (PSTPP). In addition, the key advantages, limitations and future outlook of CETSA strategies for NP studies are discussed.

CONCLUSION

The accumulation of CETSA-based data can significantly accelerate the elucidation of the mechanism of action and drug lead discovery of NPs, and provide strong evidence for NP treatment against certain diseases. The CETSA strategy will certainly bring a great return far beyond the initial investment and open up more possibilities for future NP-based drug research and development.

Orchestration of Mitochondrial Function and Remodeling by Post-Translational Modifications Provide Insight into Mechanisms of Viral Infection

The regulation of mitochondria structure and function is at the core of numerous viral infections. Acting in support of the host or of virus replication, mitochondria regulation facilitates control of energy metabolism, apoptosis, and immune signaling. Accumulating studies have pointed to post-translational modification (PTM) of mitochondrial proteins as a critical component of such regulatory mechanisms. Mitochondrial PTMs have been implicated in the pathology of several diseases and emerging evidence is starting to highlight essential roles in the context of viral infections. Here, we provide an overview of the growing arsenal of PTMs decorating mitochondrial proteins and their possible contribution to the infection-induced modulation of bioenergetics, apoptosis, and immune responses. We further consider links between PTM changes and mitochondrial structure remodeling, as well as the enzymatic and non-enzymatic mechanisms underlying mitochondrial PTM regulation. Finally, we highlight some of the methods, including mass spectrometry-based analyses, available for the identification, prioritization, and mechanistic interrogation of PTMs.

Proteomic discovery of chemical probes that perturb protein complexes in human cells

Most human proteins lack chemical probes, and several large-scale and generalizable small-molecule binding assays have been introduced to address this problem. How compounds discovered in such "binding-first" assays affect protein function, nonetheless, often remains unclear. Here, we describe a "function-first" proteomic strategy that uses size exclusion chromatography (SEC) to assess the global impact of electrophilic compounds on protein complexes in human cells. Integrating the SEC data with cysteine-directed activity-based protein profiling identifies changes in protein-protein interactions that are caused by site-specific liganding events, including the stereoselective engagement of cysteines in PSME1 and SF3B1 that disrupt the PA28 proteasome regulatory complex and stabilize a dynamic state of the spliceosome, respectively. Our findings thus show how multidimensional proteomic analysis of focused libraries of electrophilic compounds can expedite the discovery of chemical probes with site-specific functional effects on protein complexes in human cells.

Investigation of cellular response to the HSP90 inhibition in human cells through thermal proteome profiling

Heat shock proteins are chaperones and they are responsible for protein folding in cells. HSP90 is one of the most important chaperones in human cells, and its inhibition is promising for cancer therapy. However, despite the development of multiple HSP90 inhibitors, none of them has been approved for disease treatment due to unexpected cellular toxicity and side-effects. Hence, a more comprehensive investigation of cellular response to HSP90 inhibitors can aid in a better understanding of the molecular mechanisms of the cytotoxicity and side effects of these inhibitors. The thermal stability shifts of proteins, which represent protein structure and interaction alterations, can provide valuable information complementary to the results obtained from commonly used abundance-based proteomics analysis. Here, we systematically investigated cell response to different HSP90 inhibitors through global quantification of protein thermal stability changes using thermal proteome profiling, together with measurement of protein abundance changes. Besides the targets and potential off-targets of the drugs, proteins with significant thermal stability changes under the HSP90 inhibition are found to be involved in cell stress responses and the translation process. Moreover, proteins with thermal stability shifts under the inhibition are upstream of those with altered expression. These findings indicate that the HSP90 inhibition perturbs cell transcription and translation processes. The current study provides a different perspective for achieving a better understanding of cellular response to the chaperone inhibition.