Protein painting for structural and binding site analysis via intracellular lysine reactivity profiling with o-phthalaldehyde.

The three-dimensional structure and the molecular interaction of proteins determine their roles in many cellular processes. Chemical protein painting with protein mass spectrometry can identify changes in structural conformations and molecular interactions of proteins including their binding sites. Nevertheless, most current protein painting techniques identify protein targets and binding sites of drugs in vitro using a cell lysate or purified protein. Here, we tested 11 membrane-permeable lysine-reactive chemical probes for intracellular covalent labeling of endogenous proteins, which reveals ortho-phthalaldehyde (OPA) as the most reactive probe in the intracellular environment. An MS workflow and a new data analysis strategy termed RAPID (Reactive Amino acid Profiling by Inverse Detection) was developed to enhance detection sensitivity. RAPID with OPA successfully identified structural changes induced by the allosteric drug TEPP-46 on its target protein PKM2 and was applied to profile the conformation change of the proteome occurring in cells during thermal denaturation. The application of RAPID-OPA on cells treated with geldanamycin, selumetinib, and staurosporine successfully revealed their binding sites on target proteins. Thus, RAPID-OPA for cellular protein painting enables the identification of ligand-binding sites and detection of protein structural changes occurring in cells.

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

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

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

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

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

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

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

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

Targeting macrophage autophagy for inflammation resolution and tissue repair in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a chronic, non-specific, recurrent inflammatory disease, majorly affecting the gastrointestinal tract. Due to its unclear pathogenesis, the current therapeutic strategy for IBD is focused on symptoms alleviation. Autophagy is a lysosome-mediated catabolic process for maintaining cellular homeostasis. Genome-wide association studies and subsequent functional studies have highlighted the critical role of autophagy in IBD via a number of mechanisms, including modulating macrophage function. Macrophages are the gatekeepers of intestinal immune homeostasis, especially involved in regulating inflammation remission and tissue repair. Interestingly, many autophagic proteins and IBD-related genes have been revealed to regulate macrophage function, suggesting that macrophage autophagy is a potentially important process implicated in IBD regulation. Here, we have summarized current understanding of macrophage autophagy function in pathogen and apoptotic cell clearance, inflammation remission and tissue repair regulation in IBD, and discuss how this knowledge can be used as a strategy for IBD treatment.

A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks.

Detecting protein complexes is critical for studying cellular organizations and functions. The accumulation of protein-protein interaction (PPI) data enables the identification of protein complexes computationally. Although a great number of computational methods have been proposed to identify protein complexes from PPI networks, most of them ignore the signs of PPIs that reflect the ways proteins interact (activation or inhibition). As not all PPIs imply co-complex relationships, taking into account the signs of PPIs can benefit the identification of protein complexes. Moreover, PPI networks are not static, but vary with the change of cell states or environments. However, existing methods are primarily designed for single-network clustering, and rarely consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering (PS-SNC) model for identifying protein complexes from multiple state-specific signed PPI networks jointly. PS-SNC can not only consider the signs of PPIs, but also identify the common and unique protein complexes in different states. Experimental results on synthetic and real datasets show that our PS-SNC model can achieve better performance than other state-of-the-art protein complex detection methods. Extensive analysis on real datasets demonstrate the effectiveness of PS-SNC in revealing novel insights about the underlying patterns of different cell lines.

Toosendanin, a late-stage autophagy inhibitor, sensitizes triple-negative breast cancer to irinotecan chemotherapy.

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that develops resistance to chemotherapy frequently. Autophagy has been reported as a pro-survival response to chemotherapeutic drugs in TNBC, and suppression of autophagy can be a strategy to overcome drug resistance. METHODS: The efficacy of toosendanin (TSN) in blocking autophagy flux was measured by western blot analysis of autophagy markers, and the fluorescent imaging of RFP-GFP-LC3 probe. The co-localization of autophagosomes and lysosomes was analyzed by fluorescent imaging. Then, lysosome function was determined by measuring the lysosomal pH value and the activity of lysosomal hydrolytic proteases. For in vitro study, human triple-negative breast cancer MDA-MB-231 and MDA-MB-436 cell lines were used for evaluating the anti-proliferative effect. For in vivo study, the RFP-GFP-LC3 MDA-MB-231 xenograft nude mice received intraperitoneal injection of irinotecan (10 mg/kg), TSN (0.5 mg/kg) or a combination, and the autophagy activity and cell apoptosis were determined in tumor tissue. The degree of pathological injury of tissue was evaluated by liver index. RESULTS: The natural autophagy inhibitor TSN, a triterpenoid extracted from Melia toosenda Sieb. et Zucc, potently inhibited late-stage autophagy in TNBC cells. This effect was achieved via elevating lysosome pH rather than blocking the fusion of autophagosomes and lysosomes. We further investigated the effects of TSN on the in vitro and in vivo TNBC models, in combination with chemotherapeutic drug irinotecan (or its active metabolite 7-ethyl-10-hydroxycamptothecin), a topoisomerase I inhibitor showing therapeutic potential for TNBC. The data showed that TSN blocked 7-ethyl-10-hydroxycamptothecin (SN-38)/irinotecan-induced protective autophagy, and significantly induced apoptosis in TNBC cells and tumor xenograft models when compared to SN-38/irinotecan alone group.

ProSAP: a GUI software tool for statistical analysis and assessment of thermal stability data.

The Cellular Thermal Shift Assay (CETSA) plays an important role in drug-target identification, and statistical analysis is a crucial step significantly affecting conclusion. We put forward ProSAP (Protein Stability Analysis Pod), an open-source, cross-platform and user-friendly software tool, which provides multiple methods for thermal proteome profiling (TPP) analysis, nonparametric analysis (NPA), proteome integral solubility alteration and isothermal shift assay (iTSA). For testing the performance of ProSAP, we processed several datasets and compare the performance of different algorithms. Overall, TPP analysis is more accurate with fewer false positive targets, but NPA methods are flexible and free from parameters. For iTSA, edgeR and DESeq2 identify more true targets than t-test and Limma, but when it comes to ranking, the four methods show not much difference. ProSAP software is available at https://github.com/hcji/ProSAP and https://zenodo.org/record/5763315.

Recent progress in mass spectrometry-based strategies for elucidating protein-protein interactions.

Protein-protein interactions are fundamental to various aspects of cell biology with many protein complexes participating in numerous fundamental biological processes such as transcription, translation and cell cycle. MS-based proteomics techniques are routinely applied for characterising the interactome, such as affinity purification coupled to mass spectrometry that has been used to selectively enrich and identify interacting partners of a bait protein. In recent years, many orthogonal MS-based techniques and approaches have surfaced including proximity-dependent labelling of neighbouring proteins, chemical cross-linking of two interacting proteins, as well as inferring PPIs from the co-behaviour of proteins such as the co-fractionating profiles and the thermal solubility profiles of proteins. This review discusses the underlying principles, advantages, limitations and experimental considerations of these emerging techniques. In addition, a brief account on how MS-based techniques are used to investigate the structural and functional properties of protein complexes, including their topology, stoichiometry, copy number and dynamics, are discussed.

An efficient proteome-wide strategy for discovery and characterization of cellular nucleotide-protein interactions.

Metabolite-protein interactions define the output of metabolic pathways and regulate many cellular processes. Although diseases are often characterized by distortions in metabolic processes, efficient means to discover and study such interactions directly in cells have been lacking. A stringent implementation of proteome-wide Cellular Thermal Shift Assay (CETSA) was developed and applied to key cellular nucleotides, where previously experimentally confirmed protein-nucleotide interactions were well recaptured. Many predicted, but never experimentally confirmed, as well as novel protein-nucleotide interactions were discovered. Interactions included a range of different protein families where nucleotides serve as substrates, products, co-factors or regulators. In cells exposed to thymidine, a limiting precursor for DNA synthesis, both dose- and time-dependence of the intracellular binding events for sequentially generated thymidine metabolites were revealed. Interactions included known cancer targets in deoxyribonucleotide metabolism as well as novel interacting proteins. This stringent CETSA based strategy will be applicable for a wide range of metabolites and will therefore greatly facilitate the discovery and studies of interactions and specificities of the many metabolites in human cells that remain uncharacterized.

Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells.

Proteins differentially interact with each other across cellular states and conditions, but an efficient proteome-wide strategy to monitor them is lacking. We report the application of thermal proximity coaggregation (TPCA) for high-throughput intracellular monitoring of protein complex dynamics. Significant TPCA signatures observed among well-validated protein-protein interactions correlate positively with interaction stoichiometry and are statistically observable in more than 350 annotated human protein complexes. Using TPCA, we identified many complexes without detectable differential protein expression, including chromatin-associated complexes, modulated in S phase of the cell cycle. Comparison of six cell lines by TPCA revealed cell-specific interactions even in fundamental cellular processes. TPCA constitutes an approach for system-wide studies of protein complexes in nonengineered cells and tissues and might be used to identify protein complexes that are modulated in diseases.

Databases and Computational Tools for Evolutionary Analysis of Protein Phosphorylation.

Advancements in MS-based phospho-proteomics techniques have helped uncover hundred thousands of protein phosphorylation sites in human and various model organisms. The majority of these sites are uncharacterized. The sheer number of uncharacterized sites necessitates systematic approaches to prioritize sites for more in-depth annotation. Analyzing the phosphorylation and sequence conservation of uncharacterized sites across species can help reveal a subset of the functionally important phosphorylation events. Here, we outline the workflow and provide an overview of publicly available computational resources for conservation analysis of novel phosphorylation sites.

Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling.

Thermal stabilization of proteins after ligand binding provides an efficient means to assess the binding of small molecules to proteins. We show here that in combination with quantitative mass spectrometry, the approach allows for the systematic survey of protein engagement by cellular metabolites and drugs. We profiled the targets of the drugs methotrexate and (S)-crizotinib and the metabolite 2'3'-cGAMP in intact cells and identified the 2'3'-cGAMP cognate transmembrane receptor STING, involved in immune signaling.

Mutational properties of amino acid residues: implications for evolvability of phosphorylatable residues.

As François Jacob pointed out over 30 years ago, evolution is a tinkering process, and, as such, relies on the genetic diversity produced by mutation subsequently shaped by Darwinian selection. However, there is one implicit assumption that is made when studying this tinkering process; it is typically assumed that all amino acid residues are equally likely to mutate or to result from a mutation. Here, by reconstructing ancestral sequences and computing mutational probabilities for all the amino acid residues, we refute this assumption and show extensive inequalities between different residues in terms of their mutational activity. Moreover, we highlight the importance of the genetic code and physico-chemical properties of the amino acid residues as likely causes of these inequalities and uncover serine as a mutational hot spot. Finally, we explore the consequences that these different mutational properties have on phosphorylation site evolution, showing that a higher degree of evolvability exists for phosphorylated threonine and, to a lesser extent, serine in comparison with tyrosine residues. As exemplified by the suppression of serine's mutational activity in phosphorylation sites, our results suggest that the cell can fine-tune the mutational activities of amino acid residues when they reside in functional protein regions.

Sequence, structure, and network evolution of protein phosphorylation.

With the increasing amount of information about the phosphoproteomes of diverse organisms, it is now possible to begin to evaluate this information in the context of evolution. Work described at the inaugural Keystone Symposium on "The Evolution of Protein Phosphorylation" covered a wide range of eukaryotic and prokaryotic organisms, revealing insights into the evolution of protein phosphorylation at the sequence, network, and structural levels.

Roles of "junk phosphorylation" in modulating biomolecular association of phosphorylated proteins?

Protein phosphorylation dynamically regulates cellular activities in response to environmental cues. Sequence conservation analysis of recent proteome-wide phosphorylation data revealed that many previously unidentified phosphorylation sites are not well-conserved leading to the proposal that many are non-functional. However, this is based on the assumption that protein phosphorylation modulates protein function through specific position on protein sequence. Based on emerging understanding on phospho-regulation of cellular activities, we argue, with examples, that non-positionally conserved phosphorylation sites can very well be functional. We previously identified phosphorylation events that need not be conserved at same positions across orthologous proteins but are likely maintained by evolutionary conserved signaling networks through orthologous kinases. We found that proteins with such conserved phosphorylation patterns are statistically over-represented with protein and DNA-binding annotation. Here, we further correlated these proteins with protein-protein interaction data from an independent systematic study and observed they indeed interact frequently with other proteins. Hence, we speculate that non-positionally conserved phosphorylation site could be modulating biomolecular association of phosphorylated proteins possibly through fine-tuning protein's bulk electrostatic charge and through creating binding sites for phospho-binding interaction domains. We, therefore, advocate the development of complementary evolutionary approaches to interpret physiological important sites.

Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks.

The explosion of site- and context-specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector kinases. This is important for (re)constructing transient kinase-substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high-throughput protein-interaction studies due to their transient nature and strong dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts aimed at reconstructing biological signaling networks.