Thermal proteome profiling for interrogating protein interactions
2020; Springer Nature; Volume: 16; Issue: 3 Linguagem: Inglês
10.15252/msb.20199232
ISSN1744-4292
AutoresAndré Mateus, Nils Kurzawa, Isabelle Becher, Sindhuja Sridharan, Dominic Helm, Frank Stein, Athanasios Typas, Mikhail M. Savitski,
Tópico(s)Advanced Proteomics Techniques and Applications
ResumoReview5 March 2020Open Access Thermal proteome profiling for interrogating protein interactions André Mateus orcid.org/0000-0001-6870-0677 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Nils Kurzawa orcid.org/0000-0002-7846-2817 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Faculty of Biosciences, EMBL and Heidelberg University, Heidelberg, Germany Search for more papers by this author Isabelle Becher Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Sindhuja Sridharan Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Dominic Helm Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Frank Stein orcid.org/0000-0001-9695-1692 Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Athanasios Typas orcid.org/0000-0002-0797-9018 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Mikhail M Savitski Corresponding Author [email protected] orcid.org/0000-0003-2011-9247 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author André Mateus orcid.org/0000-0001-6870-0677 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Nils Kurzawa orcid.org/0000-0002-7846-2817 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Faculty of Biosciences, EMBL and Heidelberg University, Heidelberg, Germany Search for more papers by this author Isabelle Becher Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Sindhuja Sridharan Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Dominic Helm Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Frank Stein orcid.org/0000-0001-9695-1692 Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Athanasios Typas orcid.org/0000-0002-0797-9018 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Mikhail M Savitski Corresponding Author [email protected] orcid.org/0000-0003-2011-9247 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Author Information André Mateus1,‡, Nils Kurzawa1,2,‡, Isabelle Becher1, Sindhuja Sridharan1, Dominic Helm3, Frank Stein3, Athanasios Typas1 and Mikhail M Savitski *,1 1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany 2Faculty of Biosciences, EMBL and Heidelberg University, Heidelberg, Germany 3Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany ‡These authors contributed equally to this work *Corresponding author. Tel: +49 6221 387 8560; E-mail: [email protected] Mol Syst Biol (2020)16:e9232https://doi.org/10.15252/msb.20199232 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract 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. Introduction The advent of mass spectrometry-based proteomics has transformed the study of protein biology, by allowing for a global view of the proteome in its native context (Aebersold & Mann, 2016). This encompasses, for example, the study of protein abundances (Kim et al, 2014; Wilhelm et al, 2014), turnover (Schwanhausser et al, 2011), localization (Geladaki et al, 2019), or post-translational modifications (Potel et al, 2018). Recently, biophysical properties of proteins have been explored and studied system-wide with proteomics approaches. Thermal proteome profiling (TPP; Savitski et al, 2014) combines the principles of the cellular thermal shift assay (CETSA; Martinez Molina et al, 2013) with multiplexed quantitative mass spectrometry-based proteomics (Werner et al, 2012, 2014). CETSA is based on the long-standing knowledge that, when heated, proteins denature and generally become insoluble. With CETSA, the heating and aggregation can be performed directly in whole cells, and the soluble protein fraction at each temperature is determined, which allows for generating an in vivo melting curve. The melting curve profile is dependent on the context of the protein and can be altered by interactions with small molecules, such as drugs (Martinez Molina et al, 2013; Gad et al, 2014; Huber et al, 2014; Chan-Penebre et al, 2015; Fig 1). Figure 1. Thermal proteome profiling (TPP) provides proteome-wide information on protein states and interactionsTPP combines the principles of the cellular thermal shift assay (CETSA) with multiplexed quantitative mass spectrometry-based proteomics. CETSA is based on the principle that proteins denature and become insoluble when subjected to heat. By monitoring the remaining soluble fraction at multiple temperatures, melting profiles for each detected protein can be obtained. The melting profile depends on the context of the protein and can be altered by interactions with small molecules (such as drugs or metabolites), nucleic acids, or other proteins, or post-translational modifications. CETSA and TPP can be applied in vitro, in situ, and in vivo. Download figure Download PowerPoint By determining the melting profile of all detected proteins, TPP was initially developed to find targets and off-targets of drug-like molecules (Savitski et al, 2014, 2018; Huber et al, 2015; Reinhard et al, 2015; Becher et al, 2016; Mateus et al, 2016, 2018; Kitagawa et al, 2017; Azimi et al, 2018; Hu et al, 2019; Sridharan et al, 2019a)—generally, binding of a drug to a protein leads to a thermal stabilization of the protein (Fig 1). More recently, TPP has been used to identify metabolite-binding proteins, mapping the proteins which interact with different nucleotides (Huber et al, 2015; preprint: Saei et al, 2018; Dziekan et al, 2019; Sridharan et al, 2019b), and unraveling that such interactions can be both promiscuous [e.g., interactions with adenosine triphosphate (ATP)] and very specific [e.g., interactions with thymidine monophosphate (dTMP)]. Binding to nucleic acids also leads to changes in protein thermal stability (Becher et al, 2018). Proteins have also been shown to change thermal stability upon phosphorylation, illuminating the ability of TPP to capture intracellular signaling. For example, inhibition of the BCR-ABL tyrosine kinase by dasatinib leads to changes in thermal stability of proteins of this signaling pathway, including CRKL (Savitski et al, 2014). More recently, direct measurement of phosphorylated proteins has shown that these can display a different melting profile compared to their non-phosphorylated counterparts (Azimi et al, 2018; Huang et al, 2019; preprint: Potel et al, 2020). Similarly, the redox state of a protein can also alter its melting behavior (Sun et al, 2019), and we anticipate that similar stabilization events are yet to be identified for other types of post-translational modifications. It was noted early on that kinase inhibitors stabilized not only their kinase targets, but also their tightly interacting regulatory subunits, showing that interacting proteins affect each other's thermal stability (Savitski et al, 2014). Indeed, subsequent work has shown that protein complex members tend to have similar melting curves in vivo, which has been coined as thermal proximity coaggregation (TPCA) and has been used to monitor protein complex dynamics in their native state in the cell (Becher et al, 2018; Mateus et al, 2018; Tan et al, 2018). Consistently, when a complex is broken apart by genetically removing one of its members (gene knock-out), the other complex members are thermally destabilized (Mateus et al, 2018). Hence, systematically monitoring changes in protein thermal stability can facilitate the understanding of various cell processes, from that of the downstream effects of drug treatment (Savitski et al, 2014, 2018; Huber et al, 2015; Reinhard et al, 2015; Becher et al, 2016; Mateus et al, 2016, 2018; Kitagawa et al, 2017; Azimi et al, 2018; Hu et al, 2019) to the detailed study of the eukaryotic cell cycle (Becher et al, 2018; Dai et al, 2018). This approach can be applied to multiple cellular systems—including lysates, living cells, tissues, or biological fluids—and extends beyond mammalian species. The recent application of TPP to bacteria can expedite the discovery of new antibiotics, by enabling the mapping of the targets of new compounds and understanding of their resistance mechanisms (Mateus et al, 2018). New antibiotics are urgently needed in an era in which increasing resistance to existent molecules poses an imminent threat to public health (Brown & Wright, 2016; Tacconelli et al, 2018). We should emphasize that protein thermal stability is not correlated with protein stability, which is generally described by the protein half-life (Becher et al, 2018; Savitski et al, 2018). Nevertheless, there are some links between the two, such as that proteins in complexes have both similar melting curves (TPCA) and similar turnover (Mathieson et al, 2018) and that protein clients of HSP90 that require the chaperone throughout their lifetime have lower thermal stability than clients that only require it during synthesis (Savitski et al, 2018). Thermal proteome profiling is part of a larger group of recently developed tools based on proteome stability changes, which include other methods to study heat-induced protein aggregation (Peng et al, 2016; Xu et al, 2018), but also methods based on other principles such as the differential proteolytic access upon ligand binding, or changes to protein interactions or conformation, termed limited proteolysis (LiP; Feng et al, 2014; Leuenberger et al, 2017; Schopper et al, 2017; Piazza et al, 2018), or the inferring of stability of proteins from rates of oxidation (SPROX; Strickland et al, 2013). TPP is so far the only method that allows these types of experiments in living cells. This tutorial is focused on the TPP experimental setup and its recent developments, the multiple data analysis strategies, the current limitations of the methodology, and possible future developments. Thermal proteome profiling experimental setup In broad terms, a TPP experiment consists of (i) preparation of the cellular material and induction of perturbation; (ii) heat treatment; (iii) collection of soluble protein fraction; (iv) mass spectrometry-based proteomic analysis; and (v) data analysis (Fig 2). Step-by-step protocols that describe the experiment in detail have been published (Jafari et al, 2014; Franken et al, 2015). Here, we will highlight the different choices that can be made at each step and detail recent modifications that were not included in the published protocols (Box 1). Figure 2. Thermal proteome profiling (TPP) experimental setup(A) TPP starts by the choice of cellular material to study: cell extracts, intact cells, tissues, or biological fluids, from any domain of life (Archaea, Bacteria, or Eukarya, the latter including Protista, Fungi, Plantae, or Animalia). (B) A perturbation can then be induced: commonly a chemical (e.g., drug or metabolite), genetic (e.g., gene knock-out or overexpression, or point mutation in a gene), environmental, or enzymatic perturbation. Some of these can be applied in a dose- or time-dependent manner. (C) Samples are then subjected to a short heat treatment to induce protein aggregation. (D) The remaining soluble fraction at each temperature is collected after ultracentrifugation or using multi-well filter plates. (E) Samples are processed using a bottom-up proteomics workflow, generally using isobaric tandem mass tags (TMT). Labeled peptides are combined and fractionated. (f) Peptides are analyzed by mass spectrometry. Download figure Download PowerPoint Preparation of the cellular material and induction of perturbation Cellular material Thermal proteome profiling experiments start by the choice of the biological system to study, i.e., cell extracts, intact cells, tissues, or biological fluids (Fig 2; Box 2). Cell extracts are prepared by lysis, which dilutes cellular contents (such as proteins, metabolites, and co-factors) and greatly reduces the normal cell metabolism. Therefore, cell extracts are generally used to identify direct targets of perturbations (e.g., the protein(s) to which a drug binds). The extracts can be prepared by mechanical disruption of cells, for example, by douncing (Sridharan et al, 2019b) or freeze–thaw cycles (Savitski et al, 2014), which can be further aided by enzymatic digestion of certain cell structures [e.g., addition of DNAse to reduce the viscosity of the lysate (Becher et al, 2018), or lysozyme or zymolyase to digest the bacterial or yeast cell walls (Mateus et al, 2018; Ochoa et al, 2019)]. Care should be taken when preparing cell extracts to ensure that proteins remain in their native form—for example, the temperature should not be increased dramatically, and degradation by proteases should be prevented. For the latter, protease inhibitors can be added to the lysis buffer. However, this will prevent observing thermal shifts in these proteins, and therefore, keeping the lysate at low temperature and minimizing the experiment time are generally sufficient to guarantee that proteins are not degraded. The lysates can be clarified by centrifugation to remove insoluble proteins, such as membrane proteins and protein condensates (Savitski et al, 2014), although crude lysates have been successfully used (Savitski et al, 2018; Sridharan et al, 2019b). The latter allow the study of the whole proteome in near native conditions (e.g., preserving most protein complexes and membrane proteins), which has allowed the study of interactions with molecules that cannot enter intact cells, e.g., ATP (Sridharan et al, 2019b). The use of detergents to facilitate cell lysis or to solubilize membrane proteins is not recommended at this point, since it has been shown to alter the melting point of proteins (Reinhard et al, 2015)—these can be added after the heat treatment, as described below. Intact cells preserve the physiology of the cell allowing the study of downstream effects of the perturbation (e.g., the (de)activation of a metabolic pathway, or changes in protein levels, or post-translational modifications). In theory, any cell type can be used, provided that the lysis method does not resolubilize the heat-induced insoluble protein fraction. To date, the method has been used to profile bacteria (Peng et al, 2016; Mateus et al, 2018), yeast (Ochoa et al, 2019; preprint: Viéitez et al, 2019), intracellular parasites (Dziekan et al, 2019), plant cells (Volkening et al, 2019), or mammalian cells (Savitski et al, 2014). Intact tissues can also be used to preserve the in vivo context of cells (Martinez Molina et al, 2013; Ishii et al, 2017; Perrin et al, 2020). These can either be collected and treated with a perturbation, or be collected after the perturbation is performed in the whole organism and systematically analyzed (Perrin et al, 2020). This allows the collection of multiple tissues from a single animal, which provides a holistic view of the perturbation in the organism. Biological fluids, such as blood, can also be collected (Perrin et al, 2020). In the future, these might offer new therapeutic monitoring strategies or disease biomarkers. Perturbation Thermal proteome profiling can be applied without any perturbation (other than temperature) to study the melting behavior of proteins in situ, unraveling diverse properties of cellular systems, such as that physically interacting proteins have similar melting profiles (Becher et al, 2018; Mateus et al, 2018; Tan et al, 2018). More commonly, TPP experiments involve chemical [e.g., drug (Azimi et al, 2018; Becher et al, 2016; Hu et al, 2019; Huber et al, 2015; Kitagawa et al, 2017; Mateus et al, 2018, 2016; Reinhard et al, 2015; Savitski et al, 2014, 2018); or metabolite (preprint: Saei et al, 2018; Dziekan et al, 2019; Sridharan et al, 2019b)], genetic [e.g., gene knock-out (Mateus et al, 2018; Banzhaf et al, 2020)], or enzymatic (preprint: Saei et al, 2018) perturbations; or different cell states [different phase of the cell cycle (Becher et al, 2018; Dai et al, 2018), or growth phase (Mateus et al, 2018); Fig 2]. Some of the perturbations can be applied in a dose-dependent manner (Becher et al, 2016) or time-dependent manner (Becher et al, 2018; Dai et al, 2018) to improve data analysis or facilitate mechanistic understanding of the perturbation (Fig 2). Using this approach, it has been possible to deconvolute drug targets (Savitski et al, 2014, 2018; Huber et al, 2015; Reinhard et al, 2015; Becher et al, 2016; Mateus et al, 2016, 2018; Kitagawa et al, 2017; Azimi et al, 2018; Hu et al, 2019) and enzyme substrates (preprint: Saei et al, 2018), study metabolic shifts (Becher et al, 2018; Dai et al, 2018; Mateus et al, 2018), or identify protein–protein interactions (Tan et al, 2018). Heat treatment The next step in a TPP experiment is subjecting the samples to a heat cycle [at a single (Dai et al, 2018; Franken et al, 2015) or, more commonly, multiple temperatures], which is generally performed in small volumes in a thermocycler, for rapid and homogenous heat transfer (Fig 2). Usually, samples are heated for 3 min, which was initially shown to be sufficient to induce intracellular protein aggregation (Martinez Molina et al, 2013). The temperatures should range from a point in which the proteome is not affected, to a point in which the majority of the proteome has become insoluble. Therefore, these need to be adjusted depending on the optimal growth temperature of each organism. The number of temperatures probed is generally limited by practical terms (e.g., analytical capacity or possible range in the thermocycler), although 10 or 12 temperatures with an average of 3–5°C between them have generally been used (a range of 30–50°C). Wider ranges allow the study of larger fractions of the proteome and better interspecies comparisons (Mateus et al, 2018), while smaller gaps can detect subtler shifts in melting behavior (Becher et al, 2016). Collection of soluble protein fraction After the heat treatment, the remaining soluble fraction at each temperature needs to be extracted (Fig 2). If experiments are performed with intact cells, the cells need to first be lysed. Similar approaches to the ones described above in "Preparation of the cellular material and induction of perturbation" can be used. However, at this point, mild detergents that do not resolubilize the insoluble protein fraction can be used [e.g., NP40 (up to 0.8%), or DDM (up to 1%; Huber et al, 2015; Reinhard et al, 2015; Hashimoto et al, 2018)], which allows monitoring thermal stability shifts in membrane proteins (Reinhard et al, 2015). Ultracentrifugation is then used to precipitate the insoluble protein fraction, and generally, the supernatant (soluble fraction) is collected (Savitski et al, 2014)—the analysis of the insoluble fraction is also possible, an approach termed target identification by ligand stabilization (TILS), which is claimed by the authors to increase the sensitivity of the method but that has not been further explored (Peng et al, 2016). More recently, the soluble protein fraction has been extracted using multi-well filter plates at low centrifugation speeds, since the insoluble proteins do not traverse the pores of the filter (Mateus et al, 2018; Savitski et al, 2018). This allows the preparation of large numbers of samples in a benchtop centrifuge and brings TPP to an automatable format that could allow for high-throughput screens. Mass spectrometry-based proteomic analysis Protein samples are then processed using a general bottom-up proteomics workflow, such as in-gel digestion (Shevchenko et al, 2006), in-solution digestion, filter-aided sample preparation (FASP; Wisniewski et al, 2009), or single-pot solid-phase sample preparation (SP3; Hughes et al, 2014, 2019; Fig 2). All of these use a protease to digest proteins into peptides (commonly trypsin and/or Lys-C). The abundance of these peptides in each sample is then quantified by mass spectrometry (Fig 2). Generally, isobaric tandem mass tags (TMT; Werner et al, 2012, 2014) have been used to multiplex samples and increase quantification precision (Savitski et al, 2014). However, isobaric tags for relative and absolute quantitation (iTRAQ; Ross et al, 2004) have also been used (Huber et al, 2015), but limit the multiplexing capacity (i.e., fewer temperatures or compound concentrations can be multiplexed), which will generally result in longer analysis time. It is possible that other isobaric labels (Virreira Winter et al, 2018; Thompson et al, 2019) or even label-free approaches could also be used. When samples are multiplexed, they can be combined in multiple ways. In the original approach, now termed TPP temperature range (TPP-TR), samples from the same perturbation are multiplexed across the multiple temperatures—i.e., each temperature is labeled with a unique isobaric tag and each perturbation results in one sample to be analyzed in the mass spectrometer (Savitski et al, 2014; Fig 2). TPP-TR allows plotting melting profiles, which are essential for the TPCA approach (co-melting of protein complexes), or can provide additional information about protein interactions. For example, the eukaryotic RNA polymerase II (POLR2A/B) shows a biphasic melting behavior that is only visible in the melting profile, and that reflects the presence of two sub-populations: one with a higher melting point that is bound to DNA and actively transcribes it, and one that is less thermostable because it is not bound to DNA. The latter is more prevalent during mitosis, when there is a general transcriptional arrest (Becher et al, 2018). When using dose- or time-dependent perturbations, samples from a single temperature can be combined in the same mass spectrometry run—an approach termed TPP compound concentration range (TPP-CCR; Savitski et al, 2014; Franken et al, 2015), or if multiple temperatures are analyzed sequentially, two-dimensional TPP (2D-TPP; Becher et al, 2016; Fig 2). Recently, the 2D-TPP approach has been extended to discrete perturbations to study the human cell cycle (Becher et al, 2018; Dai et al, 2018), the effect of gene knock-outs (Mateus et al, 2018; Banzhaf et al, 2020), or point mutations (Ochoa et al, 2019; preprint: Peck Justice et al, 2019; preprint: Viéitez et al, 2019). In the 2D-TPP approach, melting curves for each protein cannot be obtained, since the lowest temperature sample (the reference sample for calculating the remaining soluble fraction at each temperature) is not present in all samples. However, the sensitivity of the method is greatly increased (i.e., it is possible to observe smaller thermal stability effect sizes), since control and perturbation conditions are compared in the same mass spectrometry run. To obtain a melting curve profile while combining treatment and control conditions in the same mass spectrometry run, it is possible to split the probed temperatures across multiple runs. For this, the sample from the lowest temperature is included in all runs (Perrin et al, 2020). It has also been proposed that samples originating from different temperatures of the same perturbation can be mixed prior to multiplexing (effectively, an empirical approach to determine the integral of the melting curve), an approach termed proteome integral stability alteration (PISA) that has the potential to reduce the number of samples analyzed in the mass spectrometer, but is likely to be less sensitive (Gaetani et al, 2019). The mass spectrometry analysis is generally performed on an Orbitrap instrument, since it requires resolving 6 mDa mass differences when using TMT. Quantification of isobaric tags (TMT or iTRAQ) requires the fragmentation of the labels to release the reporter ions that provide the quantification of each condition. If two peptides are co-isolated for fragmentation, this can lead to a dampening of the expected fold changes, termed ratio compression (Savitski et al, 2013). To reduce peptide co-isolation, pre-fractionation of the samples with an off-line chromatographic separation is necessary (Savitski et al, 2013, 2018). In addition, MS3 approaches in which peptide fragments are further selected and fragmented can be used (Ting et al, 2011; McAlister et al, 2014). The MS3 approach increases quantification accuracy, but reduces proteome coverage and precision. Thermal proteome profiling data analysis Raw mass spectrometry data processing The obtained raw mass spectrometry data are processed to identify and quantify the measured proteins. These steps have been usually performed by using isobarQuant (https://github.com/protcode/isob; Franken et al, 2015) together with the Mascot search engine (Matrix Science) to identify peptides based on a supplied proteome of the organism used in the experiment (Fig 3A). However, this step can be performed using any proteomics search engine, e.g., MaxQuant (Cox & Mann, 2008) or Proteome Discoverer (Thermo Fisher Scientific). Figure 3. Thermal proteome profiling (TPP) data analysis(A) Raw mass spectrometry data are processed to identify and quantify the measured proteins. (B) Data are then normalized to remove any artifacts introduced during the experimental procedure (e.g., different amounts of protein in each sample). Depending on the type of experiment performed, different analysis strategies exist as follows: (C, D) For TPP-TR experiments, (C) melting points or (D) whole melting profiles can be compared between conditions. (E) For TPP-CCR, dose–response curves are fit and targets are selected if a certain degree of stabilization and a good coefficient of determination are obtained. (F) For 2D-TPP experiments with a dose-dependent setup, a null model (linear) can be compared to an alternative model (sigmoidal) by comparing the goodness of fit of both models. The false discovery rate (FDR) is inferred by using a bootstrapping approach. (G) For 2D-TPP with discrete perturbations, a reference condition is selected and fold changes for all other conditions are calculated. To separate abundance from thermal stability effects, this method integrates the relative log-transformed fold changes measured at the first two temperatures, which are assumed to solely reflect abundance changes. Then, the log-transformed fold changes are adjusted for the abundance effect and the integral of the log-transformed fold changes at all temperatures is calculated, which reflect thermal stability changes. In this way, individual perturbations are assigned an abundance and thermal stability score which both are tested for significant deviation from zero by a bootstrapping approach. Download figure Download PowerPoint Data normalization Data generally need to be normalized to remove any artifacts introduced during the experimental procedure (e.g., different amounts of protein in each sample due to pipetting errors), which could mask or exaggerate differences between the conditions tested (Fig 3B). Typically, performing a variance stabilizing normalization (VSN; Huber et al, 2002; Karp et al, 2010) of the reporter ion intensities across replicates of the same treatment conditions but for separate temperatures is recommended. If treatment conditions are expected to vary only in few cases, normalization should be performed across treatment conditions. Specifically, for TPP-TR analysis, an additional normalization has been used, since there is a different amount of protein at each temperature. This uses a fit of the medians of relative fold changes per protein profile showing high goodness of fit in each replicate. Then, the parameters obtained from the best fit of median values across replicates are used as reference to obtain normalization coefficients for each replicate. In this way, strong deviations from the expected melting curve can be moderated (Savitski et al, 2014; Franken et al, 2015). Detecting proteins with altered thermal profiles The most common goal in analysis of TPP datasets is to find proteins with altered thermal profiles between two or more conditions. These conditions can be control and perturbation (one or multiple drug doses, or genetic perturbations), samples originatin
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