Physical Protein Interactions Research Articles

STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature.

Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature. To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities targeting the downstream semantics of the physical interaction subnetwork of the STRING database. This corpus comprises 1287 documents with ∼3500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%). We additionally enhance the model's capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database. We provide the corpus, code, and all results produced by the large-scale runs of our systems biomedical on literature via Zenodo https://doi.org/10.5281/zenodo.8139716, Github https://github.com/farmeh/ComplexTome_extraction, and the latest version of STRING database https://string-db.org/.

Artemisinin resistance in P. falciparum: probing the interacting partners of Kelch13 protein in parasite

Artemisinin (ART) resistance in Plasmodium is threatening the artemisinin combination therapies-the first line of defence against malaria. ART resistance has been established to be mediated by the Plasmodium Kelch13 (PfK13) protein. For the crucial role of PfK13 in multiple pathways of the Plasmodium life cycle and ART resistance, it is imperative that we investigate its interacting partners. We recombinantly expressed PfK13-p (Bric a brac/Poxvirus and zinc finger and propeller domains), generating anti-PfK13-p antibodies to perform co-immunoprecipitation assays and probed PfK13 interacting partners. Surface plasmon resonance and pull-down assays were performed to establish physical interactions of representative proteins with PfK13-p. The co-immunoprecipitation assays identified 17 proteins with distinct functions in the parasite life cycle- protein folding, cellular metabolism, and protein binding and invasion. In addition to the overlap with previously identified proteins, our study identified 10 unique proteins. Fructose-biphosphate aldolase and heat shock protein 70 demonstrated strong biophysical interaction with PfK13-p, with KD values of 6.6 µM and 7.6 µM, respectively. Additionally, Plasmodium merozoite surface protein 1 formed a complex with PfK13-p, which is evident from the pull-down assay. This study adds to our knowledge of the PfK13 protein in mediating ART resistance by identifying new PfK13 interacting partners. Three representative proteins-fructose-biphosphate aldolase, heat shock protein 70, and merozoite surface protein 1-demonstrated clear evidence of biophysical interactions with PfK13-p. However, elucidation of the functional relevance of these physical interactions are crucial in context of PfK13 role in ART resistance.

Computational Construction of Toxicant Signaling Networks.

Humans and animals are regularly exposed to compounds that may have adverse effects on health. The Toxicity Forecaster (ToxCast) program was developed to use high throughput screening assays to quickly screen chemicals by measuring their effects on many biological end points. Many of these assays test for effects on cellular receptors and transcription factors (TFs), under the assumption that a toxicant may perturb normal signaling pathways in the cell. We hypothesized that we could reconstruct the intermediate proteins in these pathways that may be directly or indirectly affected by the toxicant, potentially revealing important physiological processes not yet tested for many chemicals. We integrate data from ToxCast with a human protein interactome to build toxicant signaling networks that contain physical and signaling protein interactions that may be affected as a result of toxicant exposure. To build these networks, we developed the EdgeLinker algorithm, which efficiently finds short paths in the interactome that connect the receptors to TFs for each toxicant. We performed multiple evaluations and found evidence suggesting that these signaling networks capture biologically relevant effects of toxicants. To aid in dissemination and interpretation, interactive visualizations of these networks are available at http://graphspace.org.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

CellPalmSeq: A curated RNAseq database of palmitoylating and de-palmitoylating enzyme expression in human cell types and laboratory cell lines.

The reversible lipid modification protein S-palmitoylation can dynamically modify the localization, diffusion, function, conformation and physical interactions of substrate proteins. Dysregulated S-palmitoylation is associated with a multitude of human diseases including brain and metabolic disorders, viral infection and cancer. However, the diverse expression patterns of the genes that regulate palmitoylation in the broad range of human cell types are currently unexplored, and their expression in commonly used cell lines that are the workhorse of basic and preclinical research are often overlooked when studying palmitoylation dependent processes. We therefore created CellPalmSeq (https://cellpalmseq.med.ubc.ca), a curated RNAseq database and interactive webtool for visualization of the expression patterns of the genes that regulate palmitoylation across human single cell types, bulk tissue, cancer cell lines and commonly used laboratory non-human cell lines. This resource will allow exploration of these expression patterns, revealing important insights into cellular physiology and disease, and will aid with cell line selection and the interpretation of results when studying important cellular processes that depend on protein S-palmitoylation.

Centrality Analysis of Protein-Protein Interaction Networks Using R.

Proteins are structural and functional components of cells. They interact with each other to drive specific cellularfunctions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologicallyimportant proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.

Replication protein RPA2A regulates floral transition by cooperating with PRC2 in Arabidopsis.

RPA2A is a subunit of the conserved heterotrimeric replication protein A (RPA) in Arabidopsis, which is an essential replisome component that binds to single-stranded DNA during DNA replication. RPA2A controls a set of developmental processes, but the underlying mechanism is largely unknown. Here we show that RPA2A represses key flowering genes including FLOWERING LOCUS T (FT), AGAMOUS (AG) and AGAMOUS LIKE 71 (AGL71) to suppress floral transition by cooperating with the PRC2 complex. RPA2A is vigorously expressed in dividing cells and required for correct DNA replication. Mutation of RPA2A leads to early flowering, which is dependent on ectopic expression of key flowering genes including FT molecularly and genetically. RPA2A and PRC2 have common target genes including FT, AG and AGL71 supported using genetic analysis, transcriptome profiling and H3K27me3 ChIP-seq analysis. Furthermore, RPA2A physically interacts with PRC2 components CLF, EMF2 and MSI1, which recruits CLF to the chromatin loci of FT, AG and AGL71. Together, our results show that the replication protein RPA2A recruits PRC2 to key flowering genes through physical protein interaction, thereby repressing the expression of these genes to suppress floral transition in Arabidopsis.

UBE2B promotes ovarian cancer growth via promoting RAD18 mediated ZMYM2 monoubiquitination and stabilization

ABSTRACT Ubiquitin-conjugating enzyme E2 B (UBE2B) can form a heterodimer with ubiquitin E3 ligase RAD18. In this study, we aimed to explore new substrates of the UBE2B/RAD18 complex and their regulatory effects in ovarian cancer. Protein physical interactions were predicted using GeneMANIA. Serial sections of commercial ovarian cancer tissue arrays were used to check the protein expression of UBE2B, RAD18, and ZMYM2. Immunofluorescence staining and co-immunoprecipitation assays were performed to check their location and interactions. Cycloheximide chase assay was applied to explore the influence of UBE2B and RAD18 on ZMYM2 degradation. Xenograft tumor models were constructed to assess the influence of the UBE2B-ZMYM2 axis on in vivo tumor growth. A strong positive correlation between UBE2B and ZMYM2 and a moderate positive correlation between RAD18 and ZMYM2 were observed in 23 ovarian cancer cases. In CAOV4 and OVCAR3 cells, myc-ZMYM2 interacted with UBE2B and RAD18. UBE2B and ZMYM2 could be detected in the samples immunoprecipitated by anti-RAD18. UBE2B overexpression or knockdown did not alter ZMYM2 mRNA expression. UBE2B overexpression increased ZMYM2 monoubiquitination but reduced its polyubiquitination. RAD18 knockdown impaired UBE2B-induced ZMYM2 monoubiquitination. UBE2B overexpression significantly enhanced the stability of ZMYM2 protein, the effect of which was weakened by RAD18 knockdown. UBE2B overexpression significantly enhanced the growth of xenograft tumors derived from CAOV4 cells. ZMYM2 knockdown remarkedly suppressed tumor growth and impaired the growth-promoting effect of UBE2B overexpression. In conclusion, this study revealed a novel regulatory effect of the UBE2B/RAD18 complex on ZMYM2 monoubiquitination and stability in ovarian cancer.

A dominant mutation in β-AMYLASE1 disrupts nighttime control of starch degradation in Arabidopsis leaves.

Arabidopsis (Arabidopsis thaliana) leaves possess a mechanism that couples the rate of nighttime starch degradation to the anticipated time of dawn, thus preventing premature exhaustion of starch and nighttime starvation. To shed light on the mechanism, we screened a mutagenized population of a starvation reporter line and isolated a mutant that starved prior to dawn. The mutant had accelerated starch degradation, and the rate was not adjusted to time of dawn. The mutation responsible led to a single amino acid change (S132N) in the starch degradation enzyme BETA-AMYLASE1 (BAM1; mutant allele named bam1-2D), resulting in a dominant, gain-of-function phenotype. Complete loss of BAM1 (in bam1-1) did not affect rates of starch degradation, while expression of BAM1(S132N) in bam1-1 recapitulated the accelerated starch degradation phenotype of bam1-2D. In vitro analysis of recombinant BAM1 and BAM1(S132N) proteins revealed no differences in kinetic or stability properties, but in leaf extracts, BAM1(S132N) apparently had a higher affinity than BAM1 for an established binding partner required for normal rates of starch degradation, LIKE SEX FOUR1 (LSF1). Genetic approaches showed that BAM1(S132N) itself is likely responsible for accelerated starch degradation in bam1-2D and that this activity requires LSF1. Analysis of plants expressing BAM1 with alanine or aspartate rather than serine at position 132 indicated that the gain-of-function phenotype is not related to phosphorylation status at this position. Our results strengthen the view that control of starch degradation in wild-type plants involves dynamic physical interactions of degradative enzymes and related proteins with a central role for complexes containing LSF1.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

Estrogen Receptor 1Inhibition ofWnt/β-Catenin SignalingContributes toSex Differences in Hepatocarcinogenesis.

BackgroundHepatocellular Carcinoma (HCC) is a sexually dimorphic cancer, with female sex being independently protective against HCC incidence and progression. The aim of our study was to understand the mechanism of estrogen receptor signaling in driving sex differences in hepatocarcinogenesis.MethodsWe integrated 1,268 HCC patient sample profiles from publicly available gene expression data to identify the most differentially expressed genes (DEGs). We mapped DEGs into a physical protein interaction network and performed network topology analysis to identify the most important proteins. Experimental validation was performed in vitro on HCC cell lines, in and in vivo, using HCC mouse model.ResultsWe showed that the most central protein, ESR1, is HCC prognostic, as increased ESR1 expression was protective for overall survival, with HR=0.45 (95%CI 0.32-0.64, p=4.4E-06), and was more pronounced in women. Transfection of HCC cell lines with ESR1 and exposure to estradiol affected expression of genes involved in the Wnt/β-catenin signaling pathway. ER-α (protein product of ESR1) agonist treatment in a mouse model of HCC resulted in significantly longer survival and decreased tumor burden (p<0.0001), with inhibition of Wnt/β-Catenin signaling. In vitro experiments confirmed colocalization of β-catenin with ER-α, leading to inhibition of β-catenin-mediated transcription of target genes c-Myc and Cyclin D1.ConclusionCombined, the centrality of ESR1 and its inhibition of the Wnt/β-catenin signaling axis provide a biological rationale for protection against HCC incidence and progression in women.

HumanNet v3: an improved database of human gene networks for disease research.

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein–protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene–phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein–protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

Rabies Infection: An Overview of Lyssavirus-Host Protein Interactions.

Viruses are obligatory intracellular parasites that use cell proteins to take the control of the cell functions in order to accomplish their life cycle. Studying the viral-host interactions would increase our knowledge of the viral biology and mechanisms of pathogenesis. Studies on pathogenesis mechanisms of lyssaviruses, which are the causative agents of rabies, have revealed some important host protein partners for viral proteins, especially for most studied species, i.e. RABV. In this review article, the key physical lyssavirus-host protein interactions, their contributions to rabies infection, and their exploitation are discussed to improve the knowledge about rabies pathogenesis.

Protein Docking Model Evaluation by Graph Neural Networks.

Physical interactions of proteins play key functional roles in many important cellular processes. To understand molecular mechanisms of such functions, it is crucial to determine the structure of protein complexes. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed for predicting the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning–based approach named Graph Neural Network–based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph, respectively. GNN-DOVE was trained, validated, and tested on docking models in the Dockground database and further tested on a combined dataset of Dockground and ZDOCK benchmark as well as a CAPRI scoring dataset. GNN-DOVE performed better than existing methods, including DOVE, which is our previous development that uses a convolutional neural network on voxelized structure models.

Network Analysis of Integrin Adhesion Complexes.

Cell-surface adhesion receptors mediate interactions with the extracellular matrix (ECM) to control many fundamental aspects of cell behavior, including cell migration, survival, and proliferation. Integrin adhesion receptors recruit structural and signaling proteins to form multimolecular adhesion complexes that link the plasma membrane to the actomyosin cytoskeleton. The assembly and turnover of adhesion complexes are tightly regulated, governed in part by the networks of physical protein interactions and functional signaling associations between components of the adhesome. Proteomic profiling of adhesion complexes has begun to reveal their molecular complexity and diversity. To interrogate the composition of cell-ECM adhesions, we detail herein an approach for the network analysis of adhesion complex proteomes. Integration of these proteomic data with adhesome databases in the context of predicted protein interactions enables the mapping of experimentally defined adhesion complex networks. Computational analysis of resultant network models can identify subnetworks of putative functionally linked adhesion protein communities. This approach provides a framework to predict functional adhesion protein relationships and generate new mechanistic hypotheses for further experimental testing.

Uncovering physical interactions among human and Mycobacterium tuberculosis proteins

Background: Pathogens usually evade and manipulate host immune pathways through host-pathogen protein interactions. Uncovering these interactions is crucial for determining the mechanisms underlying pathogen infection and the defense system. The growing prevalence of tuberculosis (TB) infection in the world necessitated advances in TB research. With the rising information from several divisions of biosciences, computational approaches are promising to analyze and interpret the data at the system level. Methods: In the present study, in silico two-hybrid systems is employed on model organisms to predict physical interactions among proteins of Human and Mycobacterium tuberculosis (Mtb). Consistent protein interactions are identified by the Interlog method. Co-expression analysis and functional annotations are performed to infer significant Human and Mtb protein physical interactions (HMIs). Results: The interactions identified in this study support the current TB research through an improved understanding of the pathogen infection and survival mechanism. A network of HMIs highlighted dnaK as the most highly interacting protein. Further, dnaK, eno, tuf, and gap proteins are found to trigger toll-like receptor signaling pathways and initiate pathogenesis. Conclusion: The interactions proteins identified in this study may incline the researchers to explore for novel therapeutic intervention strategies.

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.

Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.

FUNMarker: Fusion Network-Based Method to Identify Prognostic and Heterogeneous Breast Cancer Biomarkers.

Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.

Protein docking model evaluation by 3D deep convolutional neural networks.

Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models. We developed a convolutional deep neural network-based approach named DOcking decoy selection with Voxel-based deep neural nEtwork (DOVE) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein-protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions. Codes available at http://github.com/kiharalab/DOVE, http://kiharalab.org/dove/. Supplementary data are available at Bioinformatics online.

Abstract 2954: Mithramycin analogues disrupt ETS transcription factor DNA binding

Abstract Introduction: Ewing sarcoma, prostate cancer, and leukemia are a few examples where ETS transcription factors drive tumorigenesis. The transcription factors EWS-FLI1 and EWS-ERG are common translocations in Ewing sarcoma and bind DNA at GGAA repeats leading to expression of genes that drive tumor growth. Pharmacologic inhibition of EWS-FLI1 with mithramycin (MTM) was shown to inhibit expression of downstream genes and tumor growth in mice. But despite this specific inhibitory activity, MTM has a narrow therapeutic window with hematologic and hepatic toxicity attributed to displacement of the ubiquitously acting Sp1 transcription factor. Thus, a synthetic effort was initiated to develop MTM analogues with reduced toxicity and increased specificity for ETS binding sites. Structural studies informed the design of MTM analogues that may stabilize transcriptional complexes leading to the disruption of transcriptional activity and DNA damage. In vitro cytotoxicity assays demonstrated that MTM analogues have significantly higher cytotoxicity in EWS-ETS expressing cell lines. Here we present mechanistic evidence for the differences in biochemical activity among MTM and its novel analogues. Methods: Qualitative interactions between drug-DNA-protein were assessed and optimized by electrophoretic mobility shift assays (EMSA). Time-resolved fluorescence energy transfer (TR-FRET) assays were used to quantitatively determine ERG displacement from DNA in the presence of MTM and analogues. Expression of proteins indicating DNA damage (c-PARP, γ-H2AX) and phosphorylation at the C-terminal domain (CTD) of RNAPII was determined by western blot following drug treatments in ETS and non ETS expressing cell lines. Results: Using TR-FRET, we observed that MTM displaced DNA bound ERG more potently and in a concentration dependent manner as compared to MTM analogues. As compared to MTM, treatment with MTM analogues resulted in higher expression of DNA damage markers, γ-H2AX and c-PARP, specifically in cell lines containing EWS-ETS translocations in a concentration dependent manner. Conclusion: These studies provide insights regarding differences among MTM and analogues in DNA binding and interactions with DNA associated proteins in the presence and absence of EWS-ETS expression. Our results suggest that MTM analogues may bind and stabilize transcriptional complexes. These differences will provide the basis for structure activity relationships and for the development of analogues with decreased in vivo toxicity. Future work will incorporate co-immunoprecipitation studies to determine if physical protein interactions are being disrupted by MTM and analogues and cellular thermal shift assays to directly probe drug interactions with EWS-ETS proteins and with RNAPII. Note: This abstract was not presented at the meeting. Citation Format: Reiya C. Hayden, Caixia Hou, Prithiba Mitra, Abhisek Mandal, Jurgen Rohr, Jon Thorson, Oleg Tsodikov, Markos Leggas. Mithramycin analogues disrupt ETS transcription factor DNA binding [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2954.