Mitochondria are membrane-bound organelle hubs of cellular metabolism and signaling. The dysregulation of mitochondria is related to the genesis of several highly prevalent diseases, including cancer and cardiovascular disorders, urging the development of novel technologies to systematically study this organelle and its dynamics. Thermal proteome profiling (TPP) allows the unbiased study of the interactions of proteins with drugs, metabolites, and other proteins, providing a unique understanding of the state of the proteome. Here, we develop and introduce an optimized TPP workflow, mito-TPP, for the direct and extensive study of this organelle. We demonstrate that our approach detects both direct mitochondrial small molecule-protein and metabolite-protein interactions, as well as indirect downstream effects. We also show that mito-TPP preserves features from whole-cell systems, such as the coaggregation of interacting proteins. Finally, we explore the mitochondrial proteoform map, detecting more than 180 proteins with multiple proteoform groups. Overall, we demonstrate that mito-TPP is a powerful new tool for the functional study of the mitochondrial proteome.### Competing Interest StatementThe authors have declared no competing interest.

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.

The complexity of the functional proteome extends significantly beyond the protein 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 apply thermal proteome profiling with deep peptide coverage to detect functional proteoforms in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detect 15,846 proteoforms, capturing differently spliced, post-translationally modified, and cleaved proteins expressed from 9,290 genes. We identify differential coaggregation of proteoform pairs and establish links to disease biology. Moreover, we systematically make 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 proteoforms.

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

Reversible protein phosphorylation is an important mechanism for regulating (dis)assembly of biomolecular condensates. However, condensate-specific phosphosites remain largely unknown, thereby limiting our understanding of the underlying mechanisms. Here, we combine solubility proteome profiling with phosphoproteomics to quantitatively map several hundred phosphosites enriched in either soluble or condensate-bound protein subpopulations, including a subset of phosphosites modulating protein-RNA interactions. We show that multi-phosphorylation of the C-terminal disordered segment of heteronuclear ribonucleoprotein A1 (HNRNPA1), a key RNA-splicing factor, reduces its ability to locate to nuclear clusters. For nucleophosmin 1 (NPM1), an essential nucleolar protein, we show that phosphorylation of S254 and S260 is crucial for lowering its partitioning to the nucleolus and additional phosphorylation of distal sites enhances its retention in the nucleoplasm. These phosphorylation events decrease RNA and protein interactions of NPM1 to regulate its condensation. Our dataset is a rich resource for systematically uncovering the phosphoregulation of biomolecular condensates.

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

Numerous drugs and endogenous ligands bind to cell surface receptors leading to modulation of downstream signaling cascades and frequently to adaptation of the plasma membrane proteome. In-depth analysis of dynamic processes at the cell surface is challenging due to biochemical properties and low abundances of plasma membrane proteins. Here we introduce cell surface thermal proteome profiling for the comprehensive characterization of ligand-induced changes in protein abundances and thermal stabilities at the plasma membrane. We demonstrate drug binding to extracellular receptors and transporters, discover stimulation-dependent remodeling of T cell receptor complexes and describe a competition-based approach to measure target engagement of G-protein-coupled receptor antagonists. Remodeling of the plasma membrane proteome in response to treatment with the TGFB receptor inhibitor SB431542 leads to partial internalization of the monocarboxylate transporters MCT1/3 explaining the antimetastatic effects of the drug.

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

In the last decades, molecular biology has transformed into a data-rich discipline. This trend is driven by developments in imaging and the continuous increase in available omics technologies which allow for high-throughput profiling of various types of molecules in a given biological system. Classical omics approaches profile the abundance of thousands of cellular biomolecules, e.g., RNAs or proteins. Recently developed assays, such as Thermal Proteome Profiling (TPP), however, can additionally inform on biophysical states of proteins. By choosing the right experimental design or through contextualization of TPP experiments they can reveal small molecule protein engagement, protein-protein interaction (PPI) dynamics or effects of post-translational modifications (PTM). However, while experimental de- signs, reproducibility, amenable organisms and throughput of the TPP assay are being advanced at a fast pace, computational methods for statistical analysis of obtained data are lagging behind. This thesis proposes a suite of computational methods to provide tools for several of the aforementioned application areas of TPP. First, it describes a software package for analysis of TPP experiments in the context of PPIs and suggests a method for detection of differential PPIs across conditions. The application of this method to different TPP datasets revealed significantly changing PPIs during different phases of the human cell cycle and behavior of protein complexes in Escherichia coli within and across cellular compartments. Second, this work addresses a specific experimental TPP setup called 2D-TPP in which thermal stability of proteins is measured as a function of temperature and concentration of a compound of interest to find proteome-wide interactions of the compound. This was done by implementation of a curve-based hypothesis test to analyze data obtained from such experiments with false discovery rate control. The method was benchmarked on simulated data and on several real datasets. Application of the software to 2D-TPP datasets profiling epigenetic drugs revealed hitherto unknown off-targets and downstream effects of these drugs. Third, the same computational method was applied to a 2D-TPP dataset profiling ATP and GTP in a crude cell extract. The analysis of these datasets revealed functional roles of ATP in proteome regulation ranging from allosteric binding, over protein complex assembly and condensate formation. Last, a method for analysis of TPP experiments to profile the effect of PTMs is presented. While the application of this method led to the detection of phosphosites known to be involved in protein regulation, it also pointed out sites which appear to be involved in controlling the localization of proteins to membrane-less organelles. Taken together, this thesis introduces and showcases computational methods for different application areas of TPP. The presented methods are implemented as open source software packages to enable long-term availability and access to the broader community.

Abstract Summary Rtpca is an R package implementing methods for inferring protein–protein interactions (PPIs) based on thermal proteome profiling experiments of a single condition or in a differential setting via an approach called thermal proximity coaggregation. It offers user-friendly tools to explore datasets for their PPI predictive performance and easily integrates with available R packages. Availability and implementation Rtpca is available from Bioconductor (https://bioconductor.org/packages/Rtpca). Supplementary information Supplementary data are available at Bioinformatics online.

Summary Reversible protein phosphorylation regulates virtually every cellular process and is arguably the most well-studied post-translational modification. Still, less than 3% of the phosphorylation sites identified in humans have annotated functions. Functionally-relevant phosphorylation sites are known to trigger conformational changes to proteins and/or to regulate their interactions with other proteins, nucleic acids and small molecules - all of which can be reflected in the thermal stability of a protein. Thus, combining thermal proteome profiling (TPP) with phosphoproteomics (phospho-TPP) provides a way to assess the functional relevance of identified phosphorylation sites on a proteome-wide scale by comparing the melting behavior of a protein and its phosphorylated form(s). We performed phospho-TPP experiments in HeLa cells with an optimized protocol, and conclude that phosphorylation does affect protein thermal stability, but to a much lesser extent than previously reported.

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.

Thermal proteome profiling (TPP) is a mass spectrometry-based technology which was originally developed to identify drug (off-)targets on a proteome-wide scale. The assay is built on the principle of the cellular thermal shift assay which is that proteins inside living cells may be modulated in their thermal stability by ligand binding. This can be read out by shifts of melting profiles of respective proteins. Soon after TPP was established, a first Bioconductor package TPP was released, which implemented a method to infer ligand-protein interactions using shifts of melting points. The realization that this procedure lacks sensitivity to detect proteins with e.g. very high thermal stability lead to the development of the NPARC package. Further, it was realized that TPP data can also inform on protein-protein interaction (PPI) dynamics. Another Bioconductor package Rtpca was thus implemented to accomodate respective analyses. Lastly, the experimental setup of TPP experiments was expanded to profile ligand dose and temperature ranges together to improve sensitivity. To accomodate the different data structure and different required statistical analysis the package TPP2D was created. Here, the different functionalities of these packages are presented.

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. We present a statistical analysis method with reliable control of the false discovery rate and apply it to several datasets probing epigenetic drugs. We detect off-target drug engagement in unrelated protein families.

Non-negative matrix factorization (NMF) has been widely used for the analysis of genomic data to perform feature extraction and signature identification due to the interpretability of the decomposed signatures. However, running a basic NMF analysis requires the installation of multiple tools and dependencies, along with a steep learning curve and computing time. To mitigate such obstacles, we developed ShinyButchR, a novel R/Shiny application that provides a complete NMFbased analysis workflow, allowing the user to perform matrix decomposition using NMF, feature extraction, interactive visualization, relevant signature identification, and association to biological and clinical variables. ShinyButchR builds upon the also novel R package ButchR, which provides new TensorFlow solvers for algorithms of the NMF family, functions for downstream analysis, a rational method to determine the optimal factorization rank and a novel feature selection strategy.

Monitoring drug–target interactions with methods such as the cellular thermal-shift assay (CETSA) is well established for simple cell systems but remains challenging in vivo. Here we introduce tissue thermal proteome profiling (tissue-TPP), which measures binding of small-molecule drugs to proteins in tissue samples from drug-treated animals by detecting changes in protein thermal stability using quantitative mass spectrometry. We report organ-specific, proteome-wide thermal stability maps and derive target profiles of the non-covalent histone deacetylase inhibitor panobinostat in rat liver, lung, kidney and spleen and of the B-Raf inhibitor vemurafenib in mouse testis. In addition, we devised blood-CETSA and blood-TPP and applied it to measure target and off-target engagement of panobinostat and the BET family inhibitor JQ1 directly in whole blood. Blood-TPP analysis of panobinostat confirmed its binding to known targets and also revealed thermal stabilization of the zinc-finger transcription factor ZNF512. These methods will help to elucidate the mechanisms of drug action in vivo.

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 .

Protein aggregates have negative implications in disease. While reductionist experiments have increased our understanding of aggregation processes, the systemic view in biological context is still limited. To extend this understanding, we used mass spectrometry-based proteomics to characterize aggregation and disaggregation in human cells after non-lethal heat shock. Aggregation-prone proteins were enriched in nuclear proteins, high proportion of intrinsically disordered regions, high molecular mass, high isoelectric point, and hydrophilic amino acids. During recovery, most aggregating proteins disaggregated with a rate proportional to the aggregation propensity: larger loss in solubility was counteracted by faster disaggregation. High amount of intrinsically disordered regions were associated with faster disaggregation. However, other characteristics enriched in aggregating proteins did not correlate with the disaggregation rates. In addition, we analyzed changes in protein thermal stability after heat shock. Soluble remnants of aggregated proteins were more thermally stable compared with control condition. Therefore, our results provide a rich resource of heat stress-related protein solubility data and can foster further studies related to protein aggregation diseases.

The phosphatases PP1 and PP2A are responsible for the majority of dephosphorylation reactions on phosphoserine (pSer) and phosphothreonine (pThr), and are involved in virtually all cellular processes and numerous diseases. The catalytic subunits exist in cells in form of holoenzymes, which impart substrate specificity. The contribution of the catalytic subunits to the recognition of substrates is unclear. By developing a phosphopeptide library approach and a phosphoproteomic assay, we demonstrate that the specificity of PP1 and PP2A holoenzymes towards pThr and of PP1 for basic motifs adjacent to the phosphorylation site are due to intrinsic properties of the catalytic subunits. Thus, we dissect this amino acid specificity of the catalytic subunits from the contribution of regulatory proteins. Furthermore, our approach enables discovering a role for PP1 as regulator of the GRB-associated-binding protein 2 (GAB2)/14-3-3 complex. Beyond this, we expect that this approach is broadly applicable to detect enzyme-substrate recognition preferences.

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. 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.