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.

Identification of an inhibitory pocket in falcilysin provides a new avenue for malaria drug development.

Identification of new druggable protein targets remains the key challenge in the current antimalarial development efforts. Here we used mass-spectrometry-based cellular thermal shift assay (MS-CETSA) to identify potential targets of several antimalarials and drug candidates. We found that falcilysin (FLN) is a common binding partner for several drug candidates such as MK-4815, MMV000848, and MMV665806 but also interacts with quinoline drugs such as chloroquine and mefloquine. Enzymatic assays showed that these compounds can inhibit FLN proteolytic activity. Their interaction with FLN was explored systematically by isothermal titration calorimetry and X-ray crystallography, revealing a shared hydrophobic pocket in the catalytic chamber of the enzyme. Characterization of transgenic cell lines with lowered FLN expression demonstrated statistically significant increases in susceptibility toward MK-4815, MMV000848, and several quinolines. Importantly, the hydrophobic pocket of FLN appears amenable to inhibition and the structures reported here can guide the development of novel drugs against malaria.

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.

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.

CETSA Feature Based Clustering for Protein Outlier Discovery by Protein-to-Protein Interaction Prediction.

The Cellular Thermal Shift Assay (CETSA) is a biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins. This technology has revolutionized cell-based target engagement studies and has been used as guidance for drug design. Although many ap-plications of CETSA data have been explored, the correlations between CETSA data and protein-protein interactions (PPI) have barely been touched. In this study, we conduct the first exploration study applying CETSA data for PPI prediction. We use a machine learning method, Decision Tree, to predict PPI scores using proteins' CETSA features. It shows promising results that the predicted PPI scores closely match the ground-truth PPI scores. Furthermore, for a small number of protein pairs, whose PPI score predictions mismatch the ground truth, we use iterative clustering strategy to gradually reduce the number of these pairs. At the end of iterative clustering, the remaining protein pairs may have some unusual properties and are of scientific value for further biological investigation. Our study has demonstrated that PPI is a brand-new application of CETSA data. At the same time, it also manifests that CETSA data can be used as a new data source for PPI exploration study.

CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines.

Cellular Thermal Shift Assay (CETSA) has been widely used in drug discovery, cancer cell biology, immunology, etc. One of the barriers for CETSA applications is that CETSA experiments have to be conducted on various cell lines, which is extremely time-consuming and costly. In this study, we make an effort to explore the translation of CETSA features cross cell lines, i.e., known CETSA feature of a given protein in one cell line, can we automatically predict the CETSA feature of this protein in another cell line, and vice versa? Inspired by pix2pix and CycleGAN, which perform well on image-to-image translation cross various domains in computer vision, we propose a novel deep neural network model called CycleDNN for CETSA feature translation cross cell lines. Given cell lines A and B, the proposed CycleDNN consists of two auto-encoders, the first one encodes the CETSA feature from cell line A into Z in the latent space [Formula: see text], then decodes Z into the CETSA feature in cell line B., Similarly, the second one translates the CETSA feature from cell line B to cell line A through the latent space [Formula: see text]. In such a way, the two auto-encoders form a cyclic feature translation between cell lines. The reconstructed loss, cycle-consistency loss, and latent vector regularization loss are used to guide the training of the model. The experimental results on a public CETSA dataset demonstrate the effectiveness of the proposed approach.

Engineering an autonomous VH domain to modulate intracellular pathways and to interrogate the eIF4F complex.

An attractive approach to target intracellular macromolecular interfaces and to model putative drug interactions is to design small high-affinity proteins. Variable domains of the immunoglobulin heavy chain (VH domains) are ideal miniproteins, but their development has been restricted by poor intracellular stability and expression. Here we show that an autonomous and disufhide-free VH domain is suitable for intracellular studies and use it to construct a high-diversity phage display library. Using this library and affinity maturation techniques we identify VH domains with picomolar affinity against eIF4E, a protein commonly hyper-activated in cancer. We demonstrate that these molecules interact with eIF4E at the eIF4G binding site via a distinct structural pose. Intracellular overexpression of these miniproteins reduce cellular proliferation and expression of malignancy-related proteins in cancer cell lines. The linkage of high-diversity in vitro libraries with an intracellularly expressible miniprotein scaffold will facilitate the discovery of VH domains suitable for intracellular applications.

CETSA interaction proteomics define specific RNA-modification pathways as key components of fluorouracil-based cancer drug cytotoxicity.

The optimal use of many cancer drugs is hampered by a lack of detailed understanding of their mechanism of action (MoA). Here, we apply a high-resolution implementation of the proteome-wide cellular thermal shift assay (CETSA) to follow protein interaction changes induced by the antimetabolite 5-fluorouracil (5-FU) and related nucleosides. We confirm anticipated effects on the known main target, thymidylate synthase (TYMS), and enzymes in pyrimidine metabolism and DNA damage pathways. However, most interaction changes we see are for proteins previously not associated with the MoA of 5-FU, including wide-ranging effects on RNA-modification and -processing pathways. Attenuated responses of specific proteins in a resistant cell model identify key components of the 5-FU MoA, where intriguingly the abrogation of TYMS inhibition is not required for cell proliferation.

Mutant p53-reactivating compound APR-246 synergizes with asparaginase in inducing growth suppression in acute lymphoblastic leukemia cells.

Asparaginase depletes extracellular asparagine in the blood and is an important treatment for acute lymphoblastic leukemia (ALL) due to asparagine auxotrophy of ALL blasts. Unfortunately, resistance occurs and has been linked to expression of the enzyme asparagine synthetase (ASNS), which generates asparagine from intracellular sources. Although TP53 is the most frequently mutated gene in cancer overall, TP53 mutations are rare in ALL. However, TP53 mutation is associated with poor therapy response and occurs at higher frequency in relapsed ALL. The mutant p53-reactivating compound APR-246 (Eprenetapopt/PRIMA-1Met) is currently being tested in phase II and III clinical trials in several hematological malignancies with mutant TP53. Here we present CEllular Thermal Shift Assay (CETSA) data indicating that ASNS is a direct or indirect target of APR-246 via the active product methylene quinuclidinone (MQ). Furthermore, combination treatment with asparaginase and APR-246 resulted in synergistic growth suppression in ALL cell lines. Our results thus suggest a potential novel treatment strategy for ALL.

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.

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

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

New ideas for non-animal approaches to predict repeated-dose systemic toxicity: Report from an EPAA Blue Sky Workshop.

The European Partnership for Alternative Approaches to Animal Testing (EPAA) convened a 'Blue Sky Workshop' on new ideas for non-animal approaches to predict repeated-dose systemic toxicity. The aim of the Workshop was to formulate strategic ideas to improve and increase the applicability, implementation and acceptance of modern non-animal methods to determine systemic toxicity. The Workshop concluded that good progress is being made to assess repeated dose toxicity without animals taking advantage of existing knowledge in toxicology, thresholds of toxicological concern, adverse outcome pathways and read-across workflows. These approaches can be supported by New Approach Methodologies (NAMs) utilising modern molecular technologies and computational methods. Recommendations from the Workshop were based around the needs for better chemical safety assessment: how to strengthen the evidence base for decision making; to develop, standardise and harmonise NAMs for human toxicity; and the improvement in the applicability and acceptance of novel techniques. "Disruptive thinking" is required to reconsider chemical legislation, validation of NAMs and the opportunities to move away from reliance on animal tests. Case study practices and data sharing, ensuring reproducibility of NAMs, were viewed as crucial to the improvement of non-animal test approaches for systemic toxicity.

Cellular thermal shift assay for the identification of drug-target interactions in the Plasmodium falciparum proteome.

Despite decades of research, little is known about the cellular targets and the mode of action of the vast majority of antimalarial drugs. We recently demonstrated that the cellular thermal shift assay (CETSA) protocol in its two variants: the melt curve and the isothermal dose-response, represents a comprehensive strategy for the identification of antimalarial drug targets. CETSA enables proteome-wide target screening for unmodified antimalarial compounds with undetermined mechanisms of action, providing quantitative evidence about direct drug-protein interactions. The experimental workflow involves treatment of P. falciparum-infected erythrocytes with a compound of interest, heat exposure to denature proteins, soluble protein isolation, enzymatic digestion, peptide labeling with tandem mass tags, offline fractionation, and liquid chromatography-tandem mass spectrometry analysis. Methodological optimizations necessary for the analysis of this intracellular parasite are discussed, including enrichment of parasitized cells and hemoglobin depletion strategies to overcome high hemoglobin abundance in the host red blood cells. We outline an effective data processing workflow using the mineCETSA R package, which enables prioritization of drug-target candidates for follow-up studies. The entire protocol can be completed within 2 weeks.

CETSA in integrated proteomics studies of cellular processes.

The Cellular Thermal Shift Assay (CETSA) has recently emerged as a promising method to directly monitor functional modulations of protein interaction states in intact cells and tissue samples. Recent data support that the mass spectrometry-coupled proteome-wide implementation of CETSA (MS-CETSA) generates stringent information on a wide range of different interaction classes and is uniquely well suited to study the modulation of protein interaction states in cellular processes and during drug action. To expand the mechanistic insight of CETSA shifts, and to complement information from CETSA experiments, we outline how the integration of MS-CETSA with other proteomics techniques can provide a new platform for detailed, comprehensive, and interactive studies of the functional modulations of proteomes in situ.

CETSA-based target engagement of taxanes as biomarkers for efficacy and resistance.

The use of taxanes has for decades been crucial for treatment of several cancers. A major limitation of these therapies is inherent or acquired drug resistance. A key to improved outcome of taxane-based therapies is to develop tools to predict and monitor drug efficacy and resistance in the clinical setting allowing for treatment and dose stratification for individual patients. To assess treatment efficacy up to the level of drug target engagement, we have established several formats of tubulin-specific Cellular Thermal Shift Assays (CETSAs). This technique was evaluated in breast and prostate cancer models and in a cohort of breast cancer patients. Here we show that taxanes induce significant CETSA shifts in cell lines as well as in animal models including patient-derived xenograft (PDX) models. Furthermore, isothermal dose response CETSA measurements allowed for drugs to be rapidly ranked according to their reported potency. Using multidrug resistant cancer cell lines and taxane-resistant PDX models we demonstrate that CETSA can identify taxane resistance up to the level of target engagement. An imaging-based CETSA format was also established, which in principle allows for taxane target engagement to be accessed in specific cell types in complex cell mixtures. Using a highly sensitive implementation of CETSA, we measured target engagement in fine needle aspirates from breast cancer patients, revealing a range of different sensitivities. Together, our data support that CETSA is a robust tool for assessing taxane target engagement in preclinical models and clinical material and therefore should be evaluated as a prognostic tool during taxane-based therapies.

Hot CoFi Blot: A High-Throughput Colony-Based Screen for Identifying More Thermally Stable Protein Variants.

Highly soluble and stable proteins are desirable for many different applications, from basic science to reaching a cancer patient in the form of a biological drug. For X-ray crystallography-where production of a protein crystal might take weeks and even months-a stable protein sample of high purity and concentration can greatly increase the chances of producing a well-diffracting crystal. For a patient receiving a specific protein drug, its safety, efficacy, and even cost are factors affected by its solubility and stability. Increased protein expression and protein stability can be achieved by randomly altering the coding sequence. As the number of mutants generated might be overwhelming, a powerful protein expression and stability screen is required. In this chapter, we describe a colony filtration technology, which allows us to screen random mutagenesis libraries for increased thermal stability-the Hot CoFi blot. We share how to create the random mutagenesis library, how to perform the Hot CoFi blot, and how to identify more thermally stable clones. We use the Tobacco Etch Virus protease as a target to exemplify the procedure.

Horizontal Cell Biology: Monitoring Global Changes of Protein Interaction States with the Proteome-Wide Cellular Thermal Shift Assay (CETSA).

The cellular thermal shift assay (CETSA) is a biophysical technique allowing direct studies of ligand binding to proteins in cells and tissues. The proteome-wide implementation of CETSA with mass spectrometry detection (MS-CETSA) has now been successfully applied to discover targets for orphan clinical drugs and hits from phenotypic screens, to identify off-targets, and to explain poly-pharmacology and drug toxicity. Highly sensitive multidimensional MS-CETSA implementations can now also access binding of physiological ligands to proteins, such as metabolites, nucleic acids, and other proteins. MS-CETSA can thereby provide comprehensive information on modulations of protein interaction states in cellular processes, including downstream effects of drugs and transitions between different physiological cell states. Such horizontal information on ligandmodulation in cells is largely orthogonal to vertical information on the levels of different proteins and therefore opens novel opportunities to understand operational aspects of cellular proteomes.

Monitoring structural modulation of redox-sensitive proteins in cells with MS-CETSA.

Reactive oxygen species (ROS) induce different cellular stress responses but can also mediate cellular signaling. Augmented levels of ROS are associated with aging, cancer as well as various metabolic and neurological disorders. ROS can also affect the efficacy and adverse effects of drugs. Although proteins are key mediators of most ROS effects, direct studies of ROS-modulated-protein function in the cellular context are very challenging. Therefore the understanding of specific roles of different proteins in cellular ROS responses is still relatively rudimentary. In the present work we show that Mass Spectrometry-Cellular Thermal Shift Assay (MS-CETSA) can directly monitor ROS and redox modulations of protein structure at the proteome level. By altering ROS levels in cultured human hepatocellular carcinoma cell lysates and intact cells, we detected CETSA responses in many proteins known to be redox sensitive, and also revealed novel candidate ROS sensitive proteins. Studies in intact cells treated with hydrogen peroxide and sulfasalazine, a ROS modulating drug, identified not only proteins that are directly modified, but also proteins reporting on downstream cellular effects. Comprehensive changes are seen on rate-limiting proteins regulating key cellular processes, including known redox control systems, protein degradation, epigenetic control and protein translational processes. Interestingly, concerted shifts on ATP-binding proteins revealed redox-induced modulation of ATP levels, which likely control many cellular processes. Collectively, these studies establish CETSA as a novel method for cellular studies of redox modulations of proteins, which implicated in a wide range of processes and for the discovery of CETSA-based biomarkers reporting on the efficacy as well as adverse effects of drugs.

Identifying purine nucleoside phosphorylase as the target of quinine using cellular thermal shift assay.

Mechanisms of action (MoAs) have been elusive for most antimalarial drugs in clinical use. Decreasing responsiveness to antimalarial treatments stresses the need for a better resolved understanding of their MoAs and associated resistance mechanisms. In the present work, we implemented the cellular thermal shift assay coupled with mass spectrometry (MS-CETSA) for drug target identification in Plasmodium falciparum, the main causative agent of human malaria. We validated the efficacy of this approach for pyrimethamine, a folic acid antagonist, and E64d, a broad-spectrum cysteine proteinase inhibitor. Subsequently, we applied MS-CETSA to quinine and mefloquine, two important antimalarial drugs with poorly characterized MoAs. Combining studies in the P. falciparum parasite lysate and intact infected red blood cells, we found P. falciparum purine nucleoside phosphorylase (PfPNP) as a common binding target for these two quinoline drugs. Biophysical and structural studies with a recombinant protein further established that both compounds bind within the enzyme's active site. Quinine binds to PfPNP at low nanomolar affinity, suggesting a substantial contribution to its therapeutic effect. Overall, we demonstrated that implementation of MS-CETSA for P. falciparum constitutes a promising strategy to elucidate the MoAs of existing and candidate antimalarial drugs.