Complex Protein-protein Interactions Research Articles

Interface-aware molecular generative framework for protein–protein interaction modulators

Protein–protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs, particularly by considering PPI complexes or interface hotspot residues, remains a significant challenge. In this work, we constructed a comprehensive dataset of PPI interfaces with active and inactive compound pairs. Based on this, we propose a novel molecular generative framework tailored to PPI interfaces, named GENiPPI. Our evaluation demonstrates that GENiPPI captures the implicit relationships between the PPI interfaces and the active molecules, and can generate novel compounds that target these interfaces. Moreover, GENiPPI can generate structurally diverse novel compounds with limited PPI interface modulators. To the best of our knowledge, this is the first exploration of a structure-based molecular generative model focused on PPI interfaces, which could facilitate the design of PPI modulators. The PPI interface-based molecular generative model enriches the existing landscape of structure-based (pocket/interface) molecular generative model.Scientific contributionThis study introduces GENiPPI, a protein-protein interaction (PPI) interface-aware molecular generative framework. The framework first employs Graph Attention Networks to capture atomic-level interaction features at the protein complex interface. Subsequently, Convolutional Neural Networks extract compound representations in voxel and electron density spaces. These features are integrated into a Conditional Wasserstein Generative AdversarialNetwork, which trains the model to generate compound representations targeting PPI interfaces. GENiPPI effectively captures the relationship between PPI interfaces and active/inactive compounds. Furthermore, in fewshot molecular generation, GENiPPI successfully generates compounds comparable to known disruptors. GENiPPI provides an efficient tool for structure-based design of PPI modulators.

Detection of Genetic Variations in Children with Tetralogy of Fallot Using Whole Exome Sequencing Technology Integrated Bioinformatics Analysis.

Background: Tetralogy of Fallot (TOF) is the most common cyanotic heart defect in newborns, with a complex etiology and genetic variation considered to be one of the main pathogenic factors. Identifying genetic variations associated with TOF has important clinical value for understanding its pathogenesis, patient susceptibility, and prognosis of patients with TOF. Therefore, this study aimed to identify potential pathogenic genes of TOF through comprehensive genetic analysis. Materials and Methods: In this study, we employed whole exome sequencing (WES) of the DNA of 47 Chinese children who received surgical TOF treatment at the Children's Hospital of Zhejiang University of Medicine and processed for DNA extraction and quantification of the DNA following WES using the Illumina NovaSeq platform. WES data undergo strict quality control and analysis processes including alignment, postprocessing, variant calling, annotation, and prioritization. Key tools, such as GATK's haplotype calling module and Annotate Variation, were used for variant annotation. In addition, by combining bioinformatics tools such as SIFT, Polyphen2, and Clin Pred, we evaluated the potential impact of nonsynonymous mutations on protein function and referred to relevant literature to support our prediction. Results: Comprehensive data analysis and quality assessment analysis corroborated the data generated from the WES dataset of 47 patients with TOF. Interpreting variants from the perspective of clinical pathogenicity results revealed a novel polymorphism and variant associated with TOF. The identified genetic results revealed evidence for a major contribution of MUTYH, RARB, GFM1, PDZD2, CEP57, DCPS, POMT2, BUB1B, CYP19A1, MAZ, USP10, and TCF3 and provided novel findings for functionally interacting proteins associated with the pathomechanism of TOF. Seven pathogenic variants related to TOF were detected, most of which were previously unreported in this cohort. Conclusions: The genetic variations discovered in this study emphasize the importance of genetic factors in the pathogenesis of TOF, revealing its complex molecular pathways and protein-protein interactions. The study of genetic diversity provides a new perspective for understanding the etiology of TOF and promotes an in-depth exploration of its pathological mechanisms. These findings lay the foundation for subsequent clinical research and the development of treatment strategies.

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions.

Tetraspanin-enriched microdomains play an important role in pathogenesis in the protozoan parasite Entamoeba histolytica.

Tetraspanins (TSPANs) are a family of highly conserved proteins present in a wide variety of eukaryotes. Although protein-protein interactions of TSPANs have been well established in eukaryotes including parasitic protists, the role they play in parasitism and pathogenesis remains largely unknown. In this study, we characterized three representative members of TSPANs, TSPAN4, TSPAN12, and TSPAN13 from the human intestinal protozoan Entamoeba histolytica. Co-immunoprecipitation assays demonstrated that TSPAN4, TSPAN12 and TSPAN13 are reciprocally pulled down together with several other TSPAN-interacting proteins including TSPAN binding protein of 55kDa (TBP55) and interaptin. Blue native-PAGE analysis showed that these TSPANs form several complexes of 120-250 kDa. Repression of tspan12 and tspan13 gene expression led to decreased secretion of cysteine proteases, while repression of tspan4 led to a four-fold increase in the activity of cysteine proteases in crude extracellular vesicles (EVs) fraction. Meanwhile, strains overexpressing HA-tagged TSPAN12 and TSPAN13 demonstrated reduced adhesion to collagen. Altogether, this study reveals that the TSPANs, especially TSPAN12 and TSPAN13, are engaged with complex protein-protein interactions and are involved in the pathogenicity-related biological functions such as protease secretion and adhesion, offering insights into the potential regulatory mechanisms of tetraspanins in protozoan parasites.

Identifying Key Genes and Their Associated Molecular Pathways in Lupus Nephritis-Osteoporosis: An In-Silico Analysis

Nephritis and osteoporosis are debilitating medical conditions that significantly impact human health and reduce quality of life. To develop potential therapeutic strategies for these disorders necessitates understanding the genetic and molecular mechanisms. Here, we employed bioinformatics techniques purposed to find key genes and associated pathways responsible for nephritis-osteoporosis comorbidity. Six microarray datasets of systemic lupus erythematosus (SLE) and osteoporosis were retrieved from the Gene Expression Omnibus (GEO) database. Post normalization of data sets LIMMA package was utilized for differential expression analysis, among the datasets 44 differentially expressed genes (DEGs) were identified. The identified 44 genes were further analyzed for gene ontology (GO) where it was found that these genes are involved in defense response, organism interactions, and response to external stimuli. In predicting the molecular function, they were involved in several biological processes including binding to lipopolysaccharides and having peptidase and hydrolase activities. Firstly, the identified genes were primarily associated with certain granules such as specific granules and secretory granules in the aspect of cellular components. Enrichment analysis pointed out the potential pathways linked to the immune system, neutrophil degranulation, innate immunity, and immune response to tuberculosis. To examine interactions among DEGs, a complex protein-protein interaction (PPI) network was built, resulting in the identification of seven hub genes, CXCL8, ELANE, LCN2, MMP8, IFIT1, MX1, and ISG15. The study suggests that these elucidated hub genes might have high potential to be exploited as promising biomarkers and therapeutic targets in nephritis-osteoporosis. Taken together, this study provided deeper insights into the genetic and molecular basis for the comorbidity of nephritis and osteoporosis.

Profiling protein–protein interactions to predict the efficacy of B-cell-lymphoma-2-homology-3 mimetics for acute myeloid leukaemia

B-cell-lymphoma-2 (BCL2) homology-3 (BH3) mimetics are inhibitors of protein–protein interactions (PPIs) that saturate anti-apoptotic proteins in the BCL2 family to induce apoptosis in cancer cells. Despite the success of the BH3-mimetic ABT-199 for the treatment of haematological malignancies, only a fraction of patients respond to the drug and most patients eventually develop resistance to it. Here we show that the efficacy of ABT-199 can be predicted by profiling the rewired status of the PPI network of the BCL2 family via single-molecule pull-down and co-immunoprecipitation to quantify more than 20 types of PPI from a total of only 1.2 × 106 cells per sample. By comparing the obtained multidimensional data with BH3-mimetic efficacies determined ex vivo, we constructed a model for predicting the efficacy of ABT-199 that designates two complexes of the BCL2 protein family as the primary mediators of drug effectiveness and resistance, and applied it to prospectively assist therapeutic decision-making for patients with acute myeloid leukaemia. The characterization of PPI complexes in clinical specimens opens up opportunities for individualized protein-complex-targeting therapies.

Competition between physical search and a weak-to-strong transition rate-limits kinesin binding times.

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that a typical binding event is limited by ADP state rather than physical search. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and physical properties.

Exploring AKAPs in visual signaling.

The complex nature of the retina demands well-organized signaling to uphold signal accuracy and avoid interference, a critical aspect in handling a variety of visual stimuli. A-kinase anchoring proteins (AKAPs), known for binding protein kinase A (PKA), contribute to the specificity and efficiency of retinal signaling. They play multifaceted roles in various retinal cell types, influencing photoreceptor sensitivity, neurotransmitter release in bipolar cells, and the integration of visual information in ganglion cells. AKAPs like AKAP79/150 and AKAP95 exhibit distinct subcellular localizations, impacting synaptic transmission and receptor sensitivity in photoreceptors and bipolar cells. Furthermore, AKAPs are involved in neuroprotective mechanisms and axonal degeneration, particularly in retinal ganglion cells. In particular, AKAP6 coordinates stress-specific signaling and promotes neuroprotection following optic nerve injury. As our review underscores the therapeutic potential of targeting AKAP signaling complexes for retinal neuroprotection and enhancement, it acknowledges challenges in developing selective drugs that target complex protein-protein interactions. Overall, this exploration of AKAPs provides valuable insights into the intricacies of retinal signaling, offering a foundation for understanding and potentially addressing retinal disorders.

Molecular mechanisms of Tetrastigma hemsleyanum Diels&Gilg against lung squamous cell carcinoma: From computational biology and experimental validation

Ethnopharmacological relevanceTetrastigma hemsleyanum （T. hemsleyanum）, valued in traditional medicine for its potential to boost immunity and combat tumors, contains uncharacterized active compounds and mechanisms. This represents a significant gap in our understanding of its ethnopharmacological relevance. Aim of the studyTo involve the mechanism of anti-lung cancer effect of T. hemsleyanum by means of experiment and bioinformatics analysis. Materials and methodsThe anticancer mechanism of T. hemsleyanum against lung squamous carcinoma (LUSC) in zebrafish was investigated. The LUSC model was established by injecting NCI–H2170 cells in the zebrafish and evaluating its anti-tumor efficacy. Next, component targets and key genes were obtained by molecular complex detection (MCODE) analysis and protein-protein interaction (PPI) network analysis. Component analysis of T. hemsleyanum was performed by UPLC-Q-TOF-MS. Molecular docking was used to simulate the binding activities of key potential active components to core targets were simulated using. Prognostic and pan-cancer analyses were then performed to validate the signaling pathways involved in the prognostic genes using gene set enrichment analysis (GSEA). Subsequently, Molecular dynamics simulations were then performed for key active components and core targets. Finally, cellular experiments were used to verify the expression of glutamate metabotropic receptor 3 (GRM3) and glutamate metabotropic receptor 7 (GRM7) in the anticancer effect exerted of T. hemsleyanum. ResultsWe experimentally confirmed the inhibitory effect of T. hemsleyanum on LUSC by transplantation of NCI–H2170 cells into zebrafish. There are 20 main compounds in T. hemsleyanum, such as procyanidin B1, catechin, quercetin, and kaempferol, etc. A total of 186 component targets of T. hemsleyanum and sixteen hub genes were screened by PPI network and MCODE analyses. Molecular docking and molecular dynamics simulation results showed that Gingerglycolipid B and Rutin had higher affinity with GRM3 and GRM7, respectively. Prognostic analysis, Pan-cancer analysis and verification experiment also confirmed that GRM3 and GRM7 were targets for T. hemsleyanum to exert anti-tumor effects and to participate in immune and mutation processes. In vitro experiments suggested that the inhibitory effect of T. hemsleyanum on cancer cells was correlated with GRM3 and GRM7. ConclusionIn vivo, in vitro and in silico results confirmed the potential anticancer effects against LUSC of T. hemsleyanum, which further consolidated the claim of its traditional uses.

A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery

This dataset represents a collection of pocket-centric structural data related to protein-protein interactions (PPIs) and PPI-related ligand binding sites. The dataset includes high-quality structural information on more than 23,000 pockets, 3,700 proteins on more than 500 organisms, and nearly 3500 ligands that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes with more than 1,700 unique protein families including some with associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for hypothesizing protein partners repurposing.

Preclinical Targeting of the PGRMC1-CK2 Axis with Silmitasertib: A Potential Strategy for Lung Adenocarcinoma Therapy.

Progesterone receptor membrane component 1 (PGRMC1) is a pleiotropic protein over-expressed in lung adenocarcinoma (LUAD). The precise molecular mechanisms underlying the signature motif of Casein kinase (CK2) presence in PGRMC1 and their role in LUAD remain unclear. X-ray crystallographic structure for CK2 and PGRMC1 from the PubChem database was obtained and subjected to protein-protein interaction (PPI) analysis to identify their interactions. In addition, the CK2 inhibitor - Silmitasertib was also utilised to understand the interaction between PGRMC1-CK2. The PPI complex (PGRMC1-CK2) and the PPI-ligand interaction analysis and their Molecular Dynamics (MD) studies revealed the stability of their interactions and critical amino acid contacts within the 5Ǻ vicinity of the CK2 signature motif "T/S-x-x-E/D". Moreover, in-vitro colony formation assay, migration assay, and gene expression analysis using quantitative Real-time PCR revealed that Silmitasertib (IC50-2.5 μM) was highly influential in suppressing the PGRMC1-CK2 expression axis. In conclusion, our study infers that PGRMC1-CK-2 axis inhibition could be a potential therapeutic option to limit the promotion and progression of lung cancer.

Leave-one-out-analysis (LOOA): web-based tool to predict influential proteins and interactions in aggregate-crosslinking proteomic data.

Many age-progressive diseases are accompanied by (and likely caused by) the presence of protein aggregation in affected tissues. Protein aggregates are conjoined by complex protein-protein interactions, which remain poorly understood. Knowledge of the proteins that comprise aggregates, and their adherent interfaces, can be useful to identify therapeutic targets to treat or prevent pathology, and to discover small molecules for disease interventions. We present web-based software to evaluate and rank influential proteins and protein-protein interactions based on graph modelling of the cross linked aggregate interactome. We have used two network-graph-based techniques: Leave-One-Vertex-Out (LOVO) and Leave-One-Edge-Out (LOEO), each followed by dimension reduction and calculation of influential vertices and edges using Principal Components Analysis (PCA) implemented as an R program. This method enables researchers to quickly and accurately determine influential proteins and protein-protein interactions present in their aggregate interactome data.

Gene expression profiling of host lipid metabolism in SARS-CoV-2 infected patients: a systematic review and integrated bioinformatics analysis

BackgroundThe Coronavirus disease 2019 (COVID-19) pandemic occurred due to the dispersion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe symptoms can be observed in COVID-19 patients with lipid-related comorbidities such as obesity and diabetes. Yet, the extensive molecular mechanisms of how SARS-CoV-2 causes dysregulation of lipid metabolism remain unknown.MethodsHere, an advanced search of articles was conducted using PubMed, Scopus, EBSCOhost, and Web of Science databases using terms from Medical Subject Heading (MeSH) like SARS-CoV-2, lipid metabolism and transcriptomic as the keywords. From 428 retrieved studies, only clinical studies using next-generation sequencing as a gene expression method in COVID-19 patients were accepted. Study design, study population, sample type, the method for gene expression and differentially expressed genes (DEGs) were extracted from the five included studies. The DEGs obtained from the studies were pooled and analyzed using the bioinformatics software package, DAVID, to determine the enriched pathways. The DEGs involved in lipid metabolic pathways were selected and further analyzed using STRING and Cytoscape through visualization by protein-protein interaction (PPI) network complex.ResultsThe analysis identified nine remarkable clusters from the PPI complex, where cluster 1 showed the highest molecular interaction score. Three potential candidate genes (PPARG, IFITM3 and APOBEC3G) were pointed out from the integrated bioinformatics analysis in this systematic review and were chosen due to their significant role in regulating lipid metabolism. These candidate genes were significantly involved in enriched lipid metabolic pathways, mainly in regulating lipid homeostasis affecting the pathogenicity of SARS-CoV-2, specifically in mechanisms of viral entry and viral replication in COVID-19 patients.ConclusionsTaken together, our findings in this systematic review highlight the affected lipid-metabolic pathways along with the affected genes upon SARS-CoV-2 invasion, which could be a potential target for new therapeutic strategies study in the future.

Applications of Graphene Field Effect Biosensors for Biological Sensing.

This chapter provides a comprehensive overview of the principles, applications, and advancements in graphene field-effect transistor (gFET) biosensors for biological sensing. The unique properties of graphene that make it ideal for biosensing, including its high conductivity, chemical stability, and ability to facilitate label-free detection, will be discussed. The chapter also explores various applications of gFET biosensors, from detecting pH and salinity changes to complex protein-protein interactions and DNA/RNA sensing. It also addresses the challenges and future directions in gFET biosensor technology, emphasizing the need for scalable manufacturing, sophisticated surface chemistry, and the integration of multiomics approaches to enhance biosensing capabilities.

Structure-guided screening of protein-protein interaction for the identification of Myc-Max heterodimer complex modulators

De-regulation of oncogenic myelocytomatosis (c-Myc or Myc) transcription factor is one of the most common molecular anomalies encountered in human cancers, and it is typically linked to many aggressive malignancies including breast, lung, cervix, colon glioblastomas, and other haematological organs. The Myc belongs to the basic helix-loop-helix zipper protein family (bHLH-ZIP), and its dimerization with another principal interactor protein partner Myc-associated factor X (Max) is essentially required for cellular transformation, cell growth and proliferation, and transcriptional activation. Intermolecular interactions have been evaluated between hetero-dimer Myc-Max protein, which identified protein-protein interaction (PPI) specific modulators using highly précised molecular docking study followed by long-range interaction stability analyzed through molecular dynamic (MD) simulation. Moreover, ADME profile analyses have been estimated for screened hit compounds. MM-GBSA-based binding free energy (ΔG) estimations have been performed for all screened hit compounds obtained from multi-step molecular docking-based virtual screening technique. According to the employed various rigorous multi-chemometric techniques, four identified inhibitors/modulators appear to have a considerable number of intermolecular contacts with hotspot residues in the hetero-dimer interface region of the Myc-Max PPI complex. However, identified hit compounds might need further structural optimization or extensive biophysical analyses for better understanding of the molecular mechanism for exhibiting the Myc-Max PPI interface binding stability. Communicated by Ramaswamy H. Sarma

The role of the neural NO synthase adapter protein in the pathogenesis of metabolic syndrome and type 2 diabetes mellitus

The pathogenesis of metabolic syndrome (MS) is characterized by obesity, hypertension, dyslipidemia and insulin resistance. MS increases the risk of developing type 2 diabetes mellitus (DM2). The neuronal isoform of nitric oxide synthase (nNOS) is defined by complex protein-protein interactions, since nNOS, unlike other isoforms of NOS,contains a C-terminal PDZ domain, which allows it to conjugate with other proteins and, first of all, to interact with an adapter of neuronal, or type 1, nitric oxide synthase (NOS1AP), also denoted CAPON in our work. Changes in the interaction between nNOS and NOS1AP lead to metabolic disorders in brain, heart, liver and skeletal muscles, which plays a key role in the development of MS and T2DM. NOS1AP, interacting with the PDZ domain of nNOS, competes with the postsynaptic density protein (PSD95) and regulates the stability of subcellular localization of nNOS and enzyme expression during synapse formation. NOS1AP promotes nNOS binding to targets such as small GTPase (Dexras1), synapsines, regulating the formation of dendritic roots, mediates activation of the nNOS-p38MAP kinase pathway during excitotoxicity. It has been shown that single-nucleotide polymorphism of the NOS1AP gene and its overexpression in the myocardium leads to the manifestation of long QT syndrome, which is most clearly manifested in elderly patients with DM2. It was found that the genetic polymorphism of NOS1AP affects insulin secretion when using calcium blockers, and can promote the development of DM2. The functional role of NOS1AP in stabilizing the functions of skeletal muscle nNOS in the cytoskeletal complex associated with dystrophin/utrophin was discovered. The purpose of the review is to provide updated information on the role of NOS1AP and the nNOS/NOS1AP complex in the pathogenesis of MS and DM2. The potential molecular mechanisms of the interaction of NOS1AP with nNOS and with other proteins, which leads to change in nNOS activity, localization and content, are discussed.

Characterization of allosteric modulators that disrupt androgen receptor co-activator protein-protein interactions to alter transactivation-Drug leads for metastatic castration resistant prostate cancer.

Three series of compounds were prioritized from a high content screening campaign that identified molecules that blocked dihydrotestosterone (DHT) induced formation of Androgen Receptor (AR) protein-protein interactions (PPIs) with the Transcriptional Intermediary Factor 2 (TIF2) coactivator and also disrupted preformed AR-TIF2 PPI complexes; the hydrobenzo-oxazepins (S1), thiadiazol-5-piperidine-carboxamides (S2), and phenyl-methyl-indoles (S3). Compounds from these series inhibited AR PPIs with TIF2 and SRC-1, another p160 coactivator, in mammalian 2-hybrid assays and blocked transcriptional activation in reporter assays driven by full length AR or AR-V7 splice variants. Compounds inhibited the growth of five prostate cancer cell lines, with many exhibiting differential cytotoxicity towards AR positive cell lines. Representative compounds from the 3 series substantially reduced both endogenous and DHT-enhanced expression and secretion of the prostate specific antigen (PSA) cancer biomarker in the C4-2 castration resistant prostate cancer (CRPC) cell line. The comparatively weak activities of series compounds in the H3-DHT and/or TIF2 box 3 LXXLL-peptide binding assays to the recombinant ligand binding domain of AR suggest that direct antagonism at the orthosteric ligand binding site or AF-2 surface respectively are unlikely mechanisms of action. Cellular enhanced thermal stability assays (CETSA) indicated that compounds engaged AR and reduced the maximum efficacy and right shifted the EC50 of DHT-enhanced AR thermal stabilization consistent with the effects of negative allosteric modulators. Molecular docking of potent representative hits from each series to AR structures suggest that S1-1 and S2-6 engage a novel binding pocket (BP-1) adjacent to the orthosteric ligand binding site, while S3-11 occupies the AR binding function 3 (BF-3) allosteric pocket. Hit binding poses indicate spaces and residues adjacent to the BP-1 and BF-3 pockets that will be exploited in future medicinal chemistry optimization studies. Small molecule allosteric modulators that prevent/disrupt AR PPIs with coactivators like TIF2 to alter transcriptional activation in the presence of orthosteric agonists might evade the resistance mechanisms to existing prostate cancer drugs and provide novel starting points for medicinal chemistry lead optimization and future development into therapies for metastatic CRPC.

Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells

Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.

Structural insights into the transcription activation mechanism of the global regulator GlnR from actinobacteria

In actinobacteria, an OmpR/PhoB subfamily protein called GlnR acts as an orphan response regulator and globally coordinates the expression of genes responsible for nitrogen, carbon, and phosphate metabolism in actinobacteria. Although many researchers have attempted to elucidate the mechanisms of GlnR-dependent transcription activation, progress is impeded by lacking of an overall structure of GlnR-dependent transcription activation complex (GlnR-TAC). Here, we report a co-crystal structure of the C-terminal DNA-binding domain of GlnR (GlnR_DBD) in complex with its regulatory cis-element DNA and a cryo-EM structure of GlnR-TAC which comprises Mycobacterium tuberculosis RNA polymerase, GlnR, and a promoter containing four well-characterized conserved GlnR binding sites. These structures illustrate how four GlnR protomers coordinate to engage promoter DNA in a head-to-tail manner, with four N-terminal receiver domains of GlnR (GlnR-RECs) bridging GlnR_DBDs and the RNAP core enzyme. Structural analysis also unravels that GlnR-TAC is stabilized by complex protein-protein interactions between GlnR and the conserved β flap, σAR4, αCTD, and αNTD domains of RNAP, which are further confirmed by our biochemical assays. Taken together, these results reveal a global transcription activation mechanism for the master regulator GlnR and other OmpR/PhoB subfamily proteins and present a unique mode of bacterial transcription regulation.

514 Understanding Structural and Dynamic Effects of the EWS-FLI1 interactome on the EWS Low Complexity Domain Function

OBJECTIVES/GOALS: The EWSR1-FLI1 gene fusion is implicated as a source of oncogenic activity in the majority of Ewing sarcoma (EwS) cases (>70%). Our studies will provide unparalleled insight into the transformative mechanics of EWS-FLI1 METHODS/STUDY POPULATION: EWS-FLI1 is an intrinsically disordered protein (IDP). IDPs do not form stable secondary or tertiary folds in biological conditions and ofter contain proline-rich regions and other multivalent binding regions. These regions interact with numerous partners resulting in highly dynamic and complex protein-protein interactions. Recent advances in proximity labelling techniques, such as the use of a biotin ligase fused to a protein of interest are exceptionally well suited to identifying IDP interactomes since they do not rely on the binding affinities but rather the distance between interacting proteins. Combining novel discovery proteomics with Nuclear Magnetic Resonance (NMR) approaches will provide unparalleled insight into the transformative mechanics of EWS-FLI1. RESULTS/ANTICIPATED RESULTS: Using the proximity labeling technique TurboID, which limits the number of false-positive interactions, we have shown that EWSR1 and EWS-FLI1 act within the spliceosome (responsible for mRNA splicing and processing). Using bioinformatics techniques, we show EWS-FLI1 interacts with peptidyl-prolyl isomerase (PPI) proteins, specifically PPIL1, through the EWS-FLI1 N-terminal region (EWS-LCD). We used NMR to provide insight into the interaction between PPIL1 and EWS-LCD, showing that EWS-LCD interacts with the catalytic region of PPIL1. We anticipate that PPIL1 isomerizes EWS-FLI1 to modulate its activity, and that EWS-FLI1 interacts with the spliceosome through formation of a biological condensate. However, future proximity labeling and NMR studies are needed to verify this activity. DISCUSSION/SIGNIFICANCE: Our studies will yield actionable insights regarding the protein-protein interfaces in EWS-FLI1 that can be targeted to attenuate the oncogenic activity of EWS-FLI1. More broadly, our results will be applicable toward understanding the etiology of other pediatric cancers and for guiding the development of novel targeted treatments.