Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Dynamical models of the mammalian target of rapamycin network in ageing

Phd ThesisThe mammalian Target of Rapamycin (mTOR)kinase is a central regulator of cellular growth and metabolism and plays an important role in ageing and age- related diseases. The increase of invitro data collected to extend our knowledge on its regulation, and consequently improve drug intervention,has highlighted the complexity of the mTOR network. This complexity is also aggravated by the intrinsic time-dependent nature of cellular regulatory network cross-talks and feedbacks. Systems biology constitutes a powerful tool for mathematically for- malising biological networks and investigating such dynamical properties. The present work discusses the development of three dynamical models of the mTOR network. The first aimed at the analysis of the current literature-based hypotheses of mTOR Complex2(mTORC2)regulation. For each hypothesis, the model predicted specific differential dynamics which were systematically tested by invitro experiments. Surprisingly, nocurrent hypothesis could explain the data and a new hypothesis of mTORC2 activation was proposed.The second model extended the previous one with an AMPK module. In this study AMPK was reported to be activated by insulin. Using a hypothesis ranking approach based on model goodness-of-fit, AMPK activity was insilico predicted and in vitro tested to be activated by the insulin receptor substrate(IRS).Finally,the last model linked mTOR with the oxidative stress response, mitochondrial reg- ulation, DNA damage and FoxO transcription factors. This work provided the characterisation of a dynamical mechanism to explain the state transition from normal to senescent cells and their reversibility of the senescentphenotype.European Council 6FP NoE LifeSpan, School of the Faculty of Medical Sciences, Newcastle Universit

Development of small-scale fluidised bed bioreactor for 3D cell culture

Three-dimensional cell culture has gained significant importance by producing physiologically relevant in vitro models with complex cell-cell and cell-matrix interactions. However, current constructs lack vasculature, efficient mass transport and tend to reproduce static or short-term conditions. The work presented aimed to design a benchtop fluidised bed bioreactor (sFBB) for hydrogel encapsulated cells to generate perfusion for homogenous diffusion of nutrients and, host substantial biomass for long-term evolution of tissue-like structures and “per cell” performance analysis. The sFBB induced consistent fluidisation of hydrogel spheres while maintaining their shape and integrity. Moreover, this system expanded into a multiple parallel units’ setup with equivalent performances enabling simultaneous comparisons. Long term culture of alginate encapsulated hepatoblastoma cells under dynamic environment led to proliferation of highly viable cell spheroids with a 2-fold increase in cellular density over static (27.3 vs 13.4 million cells/mL beads). Upregulation of hepatic phenotype markers (transcription factor C/EBP-α and drug-metabolism CYP3A4) was observed from an early stage in dynamic culture. This environment also affected ERK1/2 signalling pathway, progressively reducing its activation while increasing it in static conditions. Furthermore, culture of primary human mesenchymal stem cells was evaluated. Cell proliferation was not observed but continuous perfusion sustained their viability and undifferentiated phenotype, enabling differentiation into chondrogenic and adipogenic lineages after de-encapsulation. These biological readouts validated the sFBB as a robust dynamic platform and the prototype design was optimised using computer-aided design and computational fluid dynamics, followed by experimental tests. This thesis proved that dynamic environment promoted by fluidisation sustains biomass viability in long-term cell culture and leads 3D cell constructs with physiologically relevant phenotype. Therefore, this bioreactor would constitute a simple and versatile tool to generate in vitro tissue models and test their response to different agents, potentially increasing the complexity of the system by modifying the scaffold or co-culturing relevant cell types

Unravelling the insulin signalling pathway using mechanistic modelling

Type two diabetes affects 5% of the world's population and is increasing in prevalence. A key precursor to this disease is insulin resistance, which is characterised by a loss of responsiveness to insulin in liver, muscle and adipose tissue. This thesis focuses on understanding insulin signalling using the 3T3-L1 adipocyte cell model. Computational modelling was used to generate quantitative predictions in the signalling pathways of the adipocyte, many of which are mediated by enzymatic reactions. This study began by comparing existing enzyme kinetic models and evaluating their applicability to insulin signalling in particular. From this understanding, we developed an improved enzyme kinetic model, the differential quasi-steady state model (dQSSA), that avoids the reactant stationary assumption used in the Michaelis Menten model. The dQSSA was found to more accurately model the behaviours of enzymes in large in silico systems, and in various coenzyme inhibited and non-inhibited reactions in vitro. To apply the dQSSA, the SigMat software package was developed in the MATLAB environment to construct mathematical models from qualitative descriptions of networks. After the robustness of the package was verified, it was used to construct a basic model of the insulin signalling pathway. This model was trained against experimental temporal data at 1 nM and 100 nM doses of insulin. It revealed that the simple description of Akt activation, which displays an overshoot behaviour, was insufficient to describe the kinetics of substrate phosphorylation, which does not display the overshoot behaviour. The model was expanded to include Akt translocation and the individual phosphorylation at the 308 and 473 residues. This model resolved the discrepancy and predicts that Akt substrates are only accessible to Akt localised in the cytosol and that PIP3 sequestration of cytosolic Akt acts as a negative feedback

Design, Synthesis, Characterization and Pharmacological Evaluation of mTOR Inhibitors for Anticancer Activity.

Cancer can be defined as a disease in which a group of abnormal cells grow uncontrollably by disregarding the normal rules of cell division. Cancers arise approximately in one among every three individuals. DNA mutations arise normally at a frequency of 1 in every 20 million per gene per cell division. The average number of cells formed in any individual during an average lifetime is 1016 (10 million cells being replaced every second!). Risk of cancers are increased by infectious agents including viruses [Hepatitis B virus (HBV1), Human Papillomavirus (HPV), Human Immunodeficiency Virus(HIV)-increase risk of Nasopharyngeal, Cervical carcinomas and Kaposi’s Sarcoma] and bacteria such as Helicobacter pylori (Stomach cancers). Candidate molecules were docked for anticancer activity against the modeled protein target mTOR using drug design software (Maestro 9.1). Twenty five scaffolds were screened with high docking score against mTOR inhibitor. These compounds also passed Lipinski’s rule. The scaffold containing quinoline nucleus was selected on the basis of synthetic feasibility. All the synthesized compounds were characterized by UV spectroscopy, IR spectroscopy, NMR spectroscopy and MASS spectrometry and reported as pure. All the synthesized compounds were subjected to acute toxicity studies to fix the LD50. The LD50 value of the title compounds (C1-C5) was expected to be in category 4 i.e. > 300-2000mg/kg body weight. All the synthesized compounds were subjected to invitro experiment to determine anticancer activity using MTT assay procedure against mTOR inhibitor and found to be effective

Determining S6K1 localisation and interactions with mTORC1 in live cells using fluorescence lifetime imaging microscopy

The S6K1 kinase functions downstream within the mTORC1 pathway to regulate cell proliferation, aging and adiposity. Using GFP technology and advanced imaging, localisation of S6K1 has been established (47% nucleus and 53% cytoplasm). S6K1 strongly (Δτ=200 ps) interacts with the complex scaffold protein, raptor, and when the presence of the latter protein is increased, S6K1 translocates to the cytoplasm. S6K1 weakly interacts with mTOR (Δτ=100 ps) and not with Rheb which is required for the inhibitor function of rapamycin (71% vs 8.9% decrease in phospho-S6K1 without Rheb). The development of a novel biosensor (SensOR) shows phosphorylation of S6K1 occurring mainly in the cytoplasm of living cells (from τm=2.5 to 2.3 ns). In Chapter 4, AZD2014 and INK128, both pan-mTOR inhibitors, show fluorescent properties that can be used to investigate their cellular action. The fluorescence quantum yields for AZD2014 and INK128 are 0.47 and 0.33, respectively. Cellular uptake of the drugs is rapid with a half-life of 60 seconds and 42 seconds, respectively. Both drugs localise to mTORC1 related sub-cellular sites. Using cell spheroids to mimic a tumour environment, it was observed the outer spheroid layers take up AZD2014 5x faster than the inner layers. AZD2014 functions by interacting strongly with S6K1 (EFRET=18%) and Rheb (EFRET=16%) and less with mTOR and raptor (EFRET=11%). Chapter 5 investigates mass production techniques for generating sufficient quantities of S6K1 and mTORC1 proteins for future structural work. Although the baculovirus-insect cell expression system produced ~1mg of S6K1-raptor protein, impurities and degradation were present. Large quantities of the SensOR (3.7mg/ml) have been generated and purified. Solution phase studies show an open-closed SensOR conformation (from 2.7ns – 2 ns) upon the addition of ATP. Overall the research shows how FRET-FLIM technology can be usefully employed to elucidate where active drug targets must localise with regard to targeting mTOR phosphorylation

Mechanistic Modeling of Cell Signaling Heterogeneity and Chemokine Gradients in Cancer

Heterogeneity in cancer can give rise to rare subpopulations of cells that are unlike the bulk average of the cancer cell population, such as metastatic cells which promote the formation of secondary tumors at distant sites. These subpopulations of cells can dramatically affect the progression of the disease. Recent work suggests that heterogeneity in cancer cell populations assessed by clonal analysis cannot fully account for differences in single cell behaviors. Nonclonal heterogeneity, such as variability due to the cellular microenvironment, can play a key role in promoting cancer behaviors. Signaling heterogeneity to downstream kinase effectors is one manifestation of nonclonal heterogeneity. Seemingly-identical cancer cells activate heterogeneous signaling to extracellular signal-regulated kinase (ERK) and Akt, two kinases implicated in cancer growth, survival, proliferation, and metastasis. The mechanistic drivers that promote signaling heterogeneity remain unclear and understanding them is crucial in effectively treating cancer. Advancements in single-cell experimental techniques and computational modeling can elucidate how the spatiotemporal tumor microenvironment shapes cancer cell behavior. In this thesis, we construct mechanistic computational models coupled to experimental data to understand the major drivers of nonclonal heterogeneity in cancer. First, we built a single-cell computational model to explain the heterogeneity in cell signaling responses to ERK and Akt that we observed in breast cancer cells in experiments. The model predicted that the pre-existing signaling state of cancer cells controls signaling responses through chemokine receptor CXCR4, a critical receptor in cancer initiation and metastasis, and that these pre-existing states are shaped by environmental stimuli. The model also predicted that targeted therapies currently under clinical investigation may inadvertently potentiate pro-metastatic signaling through CXCR4. Second, we expanded our computational model to test its robustness in predicting signaling through another key receptor in cancer proliferation, epidermal growth factor receptor (EGFR). Our model predicted single-cell signaling responses in two breast cancer cell lines of various mutational backgrounds to different doses of both CXCR4 and EGFR stimuli. The robustness of our model solidified our hypothesis that variation in cell signaling stems from extrinsic noise in three key pathway components: phosphatidylinositol-3-kinase (PI3K), Ras, and mammalian target of rapamycin complex 1 (mTORC1). Third, we built a spatial model of the tumor microenvironment to understand the impact of circadian rhythms and heterogeneity in the spatial tumor composition in promoting metastasis. We found that the magnitude of chemokine gradients, which can act as the molecular highway directing cancer cells where to invade and metastasize, varies throughout the course of the day with the circadian rhythm such that therapies may be more or less effective based on time of administration. Additionally, the spatial arrangement of a tumor with regard to cells secreting and scavenging these chemokines, which can vary tumor-to-tumor or within a single tumor, has a marked impact on the direction of chemokine gradients. We found that specific arrangements of cells in tumors promote chemokine gradients that can direct cancer cells to intravasate and metastasize. Overall, this thesis builds on our knowledge of heterogeneity in cancer and provides suggestions for clinical opportunities.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153359/1/spinospc_1.pd

Identification of a high affinity binding site for abscisic acid on human lanthionine synthetase component C-like protein 2

Lanthionine synthetase component C-like protein 2 (LANCL2) has been identified as the mammalian receptor mediating the functional effects of the universal stress hormone abscisic acid (ABA) in mammals. ABA stimulates insulin independent glucose uptake in myocytes and adipocytes via LANCL2 binding in vitro, improves glucose tolerance in vivo and induces brown fat activity in vitro and in vivo. The emerging role of the ABA/LANCL2 system in glucose and lipid metabolism makes it an attractive target for pharmacological interventions in diabetes mellitus and the metabolic syndrome. The aim of this study was to investigate the presence of ABA binding site(s) on LANCL2 and identify the amino acid residues involved in ABA binding. Equilibrium binding assays ([3H]-ABA saturation binding and surface plasmon resonance analysis) suggested multiple ABA-binding sites, prompting us to perform a computational study that indicated one putative high-affinity and two low-affinity binding sites. Site-directed mutagenesis (single mutant R118I, triple mutants R118I/R22I/K362I and R118I/S41A/E46I) and equilibrium binding experiments on the mutated LANCL2 proteins identified a high-affinity ABA-binding site involving R118, with a KD of 2.6 nM ± 1.2 nM, as determined by surface plasmon resonance. Scatchard plot analysis of binding curves from both types of equilibrium binding assays revealed a Hill coefficient >1, suggesting cooperativity of ABA binding to LANCL2. Identification of the high-affinity ABA-binding site is expected to allow the design of ABA agonists/antagonists, which will help to understand the role of the ABA/LANCL2 system in human physiology and disease

Modeling and Analysis of Signal Transduction Networks

Biological pathways, such as signaling networks, are a key component of biological systems of each living cell. In fact, malfunctions of signaling pathways are linked to a number of diseases, and components of signaling pathways are used as potential drug targets. Elucidating the dynamic behavior of the components of pathways, and their interactions, is one of the key research areas of systems biology. Biological signaling networks are characterized by a large number of components and an even larger number of parameters describing the network. Furthermore, investigations of signaling networks are characterized by large uncertainties of the network as well as limited availability of data due to expensive and time-consuming experiments. As such, techniques derived from systems analysis, e.g., sensitivity analysis, experimental design, and parameter estimation, are important tools for elucidating the mechanisms involved in signaling networks. This Special Issue contains papers that investigate a variety of different signaling networks via established, as well as newly developed modeling and analysis techniques

3D culture platform for the study of cancer biology and drug response

Thesis (Ph.D.)--Boston UniversityCancer is the third leading cause of death worldwide, accounting for almost 13% of all mortalities. In developed countries, when caught early, cancer is very treatable with high success rates for first line treatments. In fact, only about 10% of cancer-related deaths are due to the primary tumor, with the other 90% being caused by metastatic or recurrent neoplasms. These secondary tumors often present with a reduced sensitivity to chemotherapeutic agents, making treatment difficult. Recently, the role of the cell microenvironment in informing tumor drug response has begun to be appreciated. Despite this, we still lack a comprehensive understanding of this relationship, mostly due to the lack of appropriate in vitro models in which to study cancer-matrix interactions. With the goal of providing insight to such behaviors, the presented research seeks to elucidate the following questions: (1) What effect does a 3D ECM have on cancer cell drug response, both at a cell behavior and protein level? (2) Can we promote in vivo-like cell-cell and cell-ECM interactions in a biomimetic 3D environment? (3) Does a collagen-based 3D culture system recapitulate tissue-specific behaviors of tumor cells? And (4) Can we model disease progression by modulating ECM characteristics? The presented research attempts to first establish the value of 3D culture systems as a model for cancer study, and then use this knowledge to develop and validate a novel, biomimetic cancer cell culture platform. In short, cancer cells are grown into large spheroids and then implanted into type 1 collagen gels. Advanced fluorescent microscopy and protein assays are used to assess cell behavior and drug response. Results indicate that by modulating the collagen content of the gels, cell behavior can be directly controlled, and that the resultant cell behavior is consistent with previous in vivo studies that employed a similar microenvironment. Finally, we show that increasing collagen content can be used as a model of breast cancer progression, including developing insights into later stage tumors with invasive properties