DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors are shown to be not informative enough to predict accurate DTIs. Thus, in this study, we employ a convolutional neural network (CNN) on raw protein sequences to capture local residue patterns participating in DTIs. With CNN on protein sequences, our model performs better than previous protein descriptor-based models. In addition, our model performs better than the previous deep learning model for massive prediction of DTIs. By examining the pooled convolution results, we found that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches.Comment: 26 pages, 7 figure

A first generation inhibitor of human Greatwall kinase, enabled by structural and functional characterisation of a minimal kinase domain construct

MASTL (microtubule-associated serine/threonine kinase-like), more commonly known as Greatwall (GWL), has been proposed as a novel cancer therapy target. GWL plays a crucial role in mitotic progression, via its known substrates ENSA/ARPP19, which when phosphorylated inactivate PP2A/B55 phosphatase. When over-expressed in breast cancer, GWL induces oncogenic properties such as transformation and invasiveness. Conversely, down-regulation of GWL selectively sensitises tumour cells to chemotherapy. Here we describe the rst structure of the GWL minimal kinase domain and development of a small-molecule inhibitor GKI-1 (Greatwall Kinase Inhibitor-1). In vitro, GKI-1 inhibits full-length human GWL, and shows cellular e cacy. Treatment of HeLa cells with GKI-1 reduces ENSA/ARPP19 phosphorylation levels, such that they are comparable to those obtained by siRNA depletion of GWL; resulting in a decrease in mitotic events, mitotic arrest/cell death and cytokinesis failure. Furthermore, GKI-1 will be a useful starting point for the development of more potent and selective GWL inhibitors

Phosphoproteomic analysis of platelet signalling cascades by flow cytometry

The activation of blood platelets is a critical haemostatic response that serves to prevent haemorrhage, but unregulated platelet activation is associated with arterial thrombosis. Endothelial-derived inhibitors prostacyclin (PGI₂) and nitric oxide (NO) activate protein kinase-mediated signalling cascades to regulate platelet function and prevent vascular thrombosis. These signalling cascades involve a number of complex protein phosphorylation reactions, which regulate different aspects of platelet function. Dissecting the signalling events that regulate platelet function could facilitate the development of novel antiplatelet agents. Intraplatelet protein phosphorylation is commonly measured by immunoblotting, which is not conducive to whole blood analysis and therefore may not provide an accurate representation of signalling events in a (patho)physiological context. Therefore, the major aim of this thesis was to develop methodologies that could examine platelet signalling events in a more physiological context. In particular, we wanted to develop methodologies that could evaluate the ability of PGI₂ to modulate blood platelet activity. Using whole blood flow cytometry, PGI₂ was found to inhibit platelet fibrinogen binding and P-selectin expression, two independent markers of platelet activation. The inhibition of platelet function by PGI₂ corresponded with increased phosphorylation of proteins known to be targeted by PGI₂-mediated signalling cascades including vasodilator-stimulated phosphoprotein (VASP). In the next series of experiments, we developed an assay to evaluate these signalling events in whole blood. This phosphoflow assay was sensitive enough to accurately and reproducibly detect subtle dose- and time-dependent changes in protein phosphorylation in whole blood that were consistent with immunoblotting protocols with washed platelets. The application of fluorescent barcoding protocols to this assay enabled the simultaneous staining and acquisition of 24-96 samples in a single analysis tube. To exploit the high-throughput nature of the method and demonstrate its value as a drug discovery platform, we screened a library of 70 prostaglandins for their ability to stimulate intraplatelet VASP phosphorylation. The screen revealed three previously uncharacterised molecules that stimulated cAMP formation, induced VASP phosphorylation, and inhibited platelet aggregation. Because whole blood samples could be processed after cold storage, the method could be performed on samples obtained at remote locations such as clinical sites. To this end, we showed that the method could be used to measure signalling events in patients with polycystic ovary syndrome (PCOS), an endocrine disorder associated with platelet dysfunction. We envisage that the method will be useful for basic scientists, clinicians, and pharmacologists seeking novel therapies

The importance of ERBB receptor tyrosine kinase signaling in colorectal cancer: implications for EGFR-targeted therapies

Colorectal cancer (CRC) is one of the leading causes of cancer-related death in the United States. Our current understanding of the molecular pathways associated with this malignancy has led to the development of novel molecule targeted therapies, exemplified by small molecule inhibitors and monoclonal antibodies targeting the epidermal growth factor receptor (EGFR/ERBB1). EGFR is a member of the ERBB family of receptor tyrosine kinases consisting of EGFR(ERBB1), ERBB2, ERBB3 and ERBB4. They are transmembrane receptors to elicit cellular signaling pathways in response to extracellular stimuli. Upon ligand binding, ERBB family receptors dimerize to phosphorylate the cytoplasmic kinase domain, resulting in activation of complex downstream signaling cascades, among which the RAS/MEK/MAPK pathway delivers pro-proliferative signals and the PI3K/ATK/mTOR cascade act as a pro-survival pathway. The ERBB family members play a pivotal role in many aspects of cellular biology. As such, misregulation or dysfunction of ERBB receptors has been implicated in many disease states, in particular cancers of epithelial tissues, making the ERBB pathways valuable targets for pharmacological inhibition in cancer treatment. For EGFR-targeted therapies, although preclinical and early clinical studies presented encouraging results, the large-scale clinical trials clearly demonstrate that the majority of patients do not respond. This discrepancy demonstrates that little is known about the mechanisms underlying tumor response to EGFR-targeted therapies. In this study, by using ApcMin mouse models of familial CRC, we generated mice with Egfr deletion exclusively in the intestinal epithelium and demonstrated that although EGFR signaling is critical for establishment of most intestinal tumor, tumors can arise independent of EGFR activity. Moreover, we identified gene expression signatures of EGFR-independent tumors and provided evidence for ERBB3 activity in mediating compensatory pathways. Consequently, we further established the importance of ERBB3 pathway during intestinal tumorigenesis with both ApcMin mouse models of familial CRC and azoxymethane (AOM) model of sporadic CRC. Finally, by utilizing mice harboring a hypomorphic Egfr allele on four different strain backgrounds, we demonstrated the strong background modulation of tumor response to EGFR inhibition. These studies may advance understanding of ERBB biology during intestinal tumorigenesis and help design better therapies in combination with EGFR-targeted agents

Finding a targetable super-hub within the network of cancer cell persistency and adaptiveness: a clinician-scientist quantitative perspective for melanoma

In this perspective article, a clinically inspired phenotype-driven experimental approach is put forward to address the challenge of the adaptive response of solid cancers to small-molecule targeted therapies. A list of conditions is derived, including an experimental quantitative assessment of cell plasticity and an information theory-based detection of in vivo dependencies, for the discovery of post-transcriptional druggable mechanisms capable of preventing at multiple levels the emergence of plastic dedifferentiated slow-proliferating cells. The approach is illustrated by the author's own work in the example case of the adaptive response of BRAFV600-melanoma to BRAF inhibition. A bench-to-bedside and back to bench effort leads to a therapeutic strategy in which the inhibition of the baseline activity of the interferon-gamma-activated inhibitor of translation (GAIT) complex, incriminated in the expression insufficiency of the RNA-binding protein HuR in a minority of cells, results in the suppression of the plastic, intermittently slow-proliferating cells involved in the adaptive response. A similar approach is recommended for the validation of other classes of mechanisms that we seek to modulate to overcome this complex challenge of modern cancer therapy.Comment: 12 pages, 3 figure

Transcription kinetics in pluripotent cells : RNA turnover, transcription velocity, and epigenomic regulation

Transcriptional regulation is one of the primary steps in gene expression control. It is now appreciated that a large fraction of coding genome is transcribed in concert of other functional RNAs. A quantitative method for transient transcriptome sequencing (TT-seq) allows profiling of entire transcriptional activities, de novo transcription unit (TU) annotation, and estimation of transcription kinetics from initiation to termination. In Paper I, we showed the establishment of TT-seq method in mouse embryonic stem cells (mESCs) to understand transcriptome plasticity for both coding and non-coding RNAs. With external references in form of a spike-in RNA mix, we were able to estimated RNA synthesis and turnover rates, which consolidated the attenuation under inhibitor-induced pluripotent states (naïve 2i and paused mTORi). We also extended the estimation of transcription velocity to each annotated TU, by integration of RNA polymerase II (Pol II) quantitative profiles from MINUTE-ChIP (quantitative multiplexed ChIP). After explaining transcription velocity with chromatin features, we also evaluated its genome-wide contribution to termination distance. In Paper II, we mapped endogenous genomic G-quadraplex structures (G4) with CUT&Tag in HEK293T and mESCs. We verified the high signal-to-ratio G4 peaks to reflect the DNA motifs of both canonical and trans-strand putative quadraplex sequences (PQS), which enriched on both gene and active enhancer TSSs (transcription start sites). After stabilizing G4 with the small molecule PDS, we observed a genome-wide reduction of RNA synthesis (by TT-seq). The co-occupancy of G4 and R-loop was further verified at transcribed promoters and enhancers. However, promoter G4s could consistently form after transcription inhibition, which suggests an intricate cause-consequence relationship between G4 and transcription activity. In Paper III, we evaluated the regulatory role of repressive histone modifications, H2AK119 ubiquitination and H3K27 tri-methylation. We introduced a rapid H2Aub depletion by BAP1 pulse expression with the amber-suppression system, and observed a wide Polycomb target genes de-repression, especially in the bivalent chromatin state (H3K4me3 + H3K27me3). Further, we observed that H2Aub-mediated repression strength was associated with H3K27me3 occupancy. However, double depletion of H3K27me3 by Ezh2 inhibition with ectopic BAP1 failed to enlarge Polycomb genes de-repression. We also measured transcriptional responses with TT-seq and observed that H2Aub depletion immediately triggered transcription activation before the redistribution of Polycomb proteins and their associated nucleosomes decompaction. Together, our results indicate that H2Aub directly mediates Polycomb integrity and nucleosome barrier that limits early transcription checkpoints

Uncovering Intratumoral And Intertumoral Heterogeneity Among Single-Cell Cancer Specimens

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. Developing such an approach is of great translational relevance and interest, as single-cell expression data are now often collected across numerous experimental conditions (e.g., representing different drug perturbation conditions, CRISPR knockdowns, or patients undergoing clinical trials) that need to be compared. In this work, “Phenotypic Earth Mover\u27s Distance” (PhEMD) is presented as a solution to this problem. PhEMD is a general method for embedding a “manifold of manifolds,” in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). PhEMD is applied to a newly-generated, 300-biospecimen mass cytometry drug screen experiment to map small-molecule inhibitors based on their differing effects on breast cancer cells undergoing epithelial–mesenchymal transition (EMT). These experiments highlight EGFR and MEK1/2 inhibitors as strongly halting EMT at an early stage and PI3K/mTOR/Akt inhibitors as enriching for a drug-resistant mesenchymal cell subtype characterized by high expression of phospho-S6. More generally, these experiments reveal that the final mapping of perturbation conditions has low intrinsic dimension and that the network of drugs demonstrates manifold structure, providing insight into how these single-cell experiments should be computational modeled and visualized. In the presented drug-screen experiment, the full spectrum of perturbation effects could be learned by profiling just a small fraction (11%) of drugs. Moreover, PhEMD could be integrated with complementary datasets to infer the phenotypes of biospecimens not directly profiled with single-cell profiling. Together, these findings have major implications for conducting future drug-screen experiments, as they suggest that large-scale drug screens can be conducted by measuring only a small fraction of the drugs using the most expensive high-throughput single-cell technologies—the effects of other drugs may be inferred by mapping and extending the perturbation space. PhEMD is also applied to patient tumor biopsies to assess intertumoral heterogeneity. Applied to a melanoma dataset and a clear-cell renal cell carcinoma dataset (ccRCC), PhEMD maps tumors similarly to how it maps perturbation conditions as above in order to learn key axes along which tumors vary with respect to their tumor-infiltrating immune cells. In both of these datasets, PhEMD highlights a subset of tumors demonstrating a marked enrichment of exhausted CD8+ T-cells. The wide variability in tumor-infiltrating immune cell abundance and particularly prominent exhausted CD8+ T-cell subpopulation highlights the importance of careful patient stratification when assessing clinical response to T cell-directed immunotherapies. Altogether, this work highlights PhEMD’s potential to facilitate drug discovery and patient stratification efforts by uncovering the network geometry of a large collection of single-cell biospecimens. Our varied experiments demonstrate that PhEMD is highly scalable, compatible with leading batch effect correction techniques, and generalizable to multiple experimental designs, with clear applicability to modern precision oncology efforts