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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. Graphical Abstract This tutorial explains the principles of thermal proteome profiling (TPP) and analyzes the different steps of a TPP experiment. It reviews the recent developments and current applications of this methodology, and provides an outlook of possible new applications.

A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000–6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types. The proteome-wide characterization of proteostasis depends on robust approaches to determine protein half-lives. Here, the authors improve the accuracy and precision of mass spectrometry-based quantification, enabling reliable protein half-life determination in several non-dividing cell types.

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.

Bantscheff et al . use chemoproteomics to measure the affinity of small molecules for megadalton protein complexes in cell extracts. Differences in the selectivity of HDAC inhibitors observed when native HDAC complexes are compared with their purified catalytic subunits suggest the limitations of using isolated recombinant proteins in certain drug screens. The development of selective histone deacetylase (HDAC) inhibitors with anti-cancer and anti-inflammatory properties remains challenging in large part owing to the difficulty of probing the interaction of small molecules with megadalton protein complexes. A combination of affinity capture and quantitative mass spectrometry revealed the selectivity with which 16 HDAC inhibitors target multiple HDAC complexes scaffolded by ELM-SANT domain subunits, including a novel mitotic deacetylase complex (MiDAC). Inhibitors clustered according to their target profiles with stronger binding of aminobenzamides to the HDAC NCoR complex than to the HDAC Sin3 complex. We identified several non-HDAC targets for hydroxamate inhibitors. HDAC inhibitors with distinct profiles have correspondingly different effects on downstream targets. We also identified the anti-inflammatory drug bufexamac as a class IIb (HDAC6, HDAC10) HDAC inhibitor. Our approach enables the discovery of novel targets and inhibitors and suggests that the selectivity of HDAC inhibitors should be evaluated in the context of HDAC complexes and not purified catalytic subunits.

Signaling pathways are useful models for interpreting molecular data, but their coverage has long been constrained by classic biochemistry methods. The growing corpus of kinase-substrate interactions, coupled to phosphoproteomics improvements, pave the way to revisit classic signaling pathways. In this study, we explore context-specific signaling pathway inference from phosphoproteomics and kinase-substrate networks. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis and generate three datasets representing the most comprehensive characterization of the EGF response to date. We infer kinase-kinase pathways and compare them to different ground truth sets. Literature-curated networks consistently yield the highest recovery of ground-truth interactions, with modest gains from network propagation methods. Up to 90% of interactions are absent from current ground truth sets, indicating many unexplored interactions supported by data and knowledge. Our results demonstrate the limitations of traditional views on signaling pathways and point to opportunities for generating better mechanistic hypotheses. To what extent can large-scale approaches accurately reconstruct classic signaling pathways? Here, authors revisit the EGF pathway using phosphoproteomics and kinase-substrate interactions

In biological systems, liquid and solid-like biomolecular condensates may contain the same molecules but their behaviour, including movement, elasticity, and viscosity, is different on account of distinct physicochemical properties. As such, it is known that phase transitions affect the function of biological condensates and that material properties can be tuned by several factors including temperature, concentration, and valency. It is, however, unclear if some factors are more efficient than others at regulating their behaviour. Viral infections are good systems to address this question as they form condensates de novo as part of their replication programmes. Here, we used influenza A virus (IAV) liquid cytosolic condensates, AKA viral inclusions, to provide a proof of concept that liquid condensate hardening via changes in the valency of its components is more efficient than altering their concentration or the temperature of the cell. Liquid IAV inclusions may be hardened by targeting vRNP (viral ribonucleoprotein) interactions via the known NP (nucleoprotein) oligomerising molecule, nucleozin, both in vitro and in vivo without affecting host proteome abundance nor solubility. This study is a starting point for understanding how to pharmacologically modulate the material properties of IAV inclusions and may offer opportunities for alternative antiviral strategies. Cells are organized into compartments that carry out specific functions. Envelope-like membranes enclose some of those compartments, while others remain unenclosed. The latter are called biomolecular condensates, and they can shift their physical states from a more liquid to a more solid form, which may affect how well they function. Temperature, molecular concentration and molecular interactions affect the physical state of condensates. Understanding what causes physical shifts in biomolecular condensates could have important implications for human health. For example, many viruses, including influenza, HIV, rabies, measles and the virus that causes COVID-19, SARS-CoV-2, use biomolecular condensates to multiply in cells. Changing the physical state of biomolecular condensates to one that hampers viruses’ ability to multiply could be an innovative approach to treating viruses. Etibor et al. show that it is possible to harden condensates produced by influenza A virus. In the experiments, the researchers manipulated the temperature, molecular concentration and strength of connections between molecules in condensates created by influenza A-infected cells. Then, they measured their effects on the condensate’s physical state. The experiments showed that using drugs that strengthen the bonds between molecules in condensates was the most effective strategy for hardening. Studies in both human cells and mice showed that using drugs to harden condensate in infected cells did not harm the cells or the animal and disabled the virus. The experiments provide preliminary evidence that using drugs to harden biomolecular condensates may be a potential treatment strategy for influenza A. More studies are necessary to test this approach to treating influenza A or other viruses that use condensates. If they are successful, the drug could add a new tool to the antiviral treatment toolbox.

We have used a mass spectrometry-based proteomic approach to compile an atlas of the thermal stability of 48,000 proteins across 13 species ranging from archaea to humans and covering melting temperatures of 30–90 °C. Protein sequence, composition and size affect thermal stability in prokaryotes and eukaryotic proteins show a nonlinear relationship between the degree of disordered protein structure and thermal stability. The data indicate that evolutionary conservation of protein complexes is reflected by similar thermal stability of their proteins, and we show examples in which genomic alterations can affect thermal stability. Proteins of the respiratory chain were found to be very stable in many organisms, and human mitochondria showed close to normal respiration at 46 °C. We also noted cell-type-specific effects that can affect protein stability or the efficacy of drugs. This meltome atlas broadly defines the proteome amenable to thermal profiling in biology and drug discovery and can be explored online at http://meltomeatlas.proteomics.wzw.tum.de:5003/ and http://www.proteomicsdb.org . The meltome atlas compiles the thermal stability of 48,000 proteins across 13 species ranging from archaea to humans, providing a resource for analyzing protein stability in the context of function and interactions.

The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups. Applying thermal proteome profiling to acute B cell childhood leukemia cell lines combined with deep peptide fractionation and a graph-based clustering algorithm allows inference of functional proteoform groups and their association with drug response.