Structural Determination of Three Different Series of Compounds as Hsp90 Inhibitors Using 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Methods

Hsp90 is involved in correcting, folding, maturation and activation of a diverse array of client proteins; it has also been implicated in the treatment of cancer in recent years. In this work, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular docking and molecular dynamics were performed on three different series of Hsp90 inhibitors to build 3D-QSAR models, which were based on the ligand-based or receptor-based methods. The optimum 3D-QSAR models exhibited reasonable statistical characteristics with averaging internal q2 > 0.60 and external r2pred > 0.66 for Benzamide tetrahydro-4H-carbazol-4-one analogs (BT), AT13387 derivatives (AT) and Dihydroxylphenyl amides (DA). The results revealed that steric effects contributed the most to the BT model, whereas H-bonding was more important to AT, and electrostatic, hydrophobic, H-bond donor almost contributed equally to the DA model. The docking analysis showed that Asp93, Tyr139 and Thr184 in Hsp90 are important for the three series of inhibitors. Molecular dynamics simulation (MD) further indicated that the conformation derived from docking is basically consistent with the average structure extracted from MD simulation. These results not only lead to a better understanding of interactions between these inhibitors and Hsp90 receptor but also provide useful information for the design of new inhibitors with a specific activity

Convex optimization of programmable quantum computers

A fundamental model of quantum computation is the programmable quantum gate array. This is a quantum processor that is fed by a program state that induces a corresponding quantum operation on input states. While being programmable, any finite-dimensional design of this model is known to be non-universal, meaning that the processor cannot perfectly simulate an arbitrary quantum channel over the input. Characterizing how close the simulation is and finding the optimal program state have been open questions for the past 20 years. Here, we answer these questions by showing that the search for the optimal program state is a convex optimization problem that can be solved via semi-definite programming and gradient-based methods commonly employed for machine learning. We apply this general result to different types of processors, from a shallow design based on quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits

Thermodynamics of Aryl-Dihydroxyphenyl-Thiadiazole Binding to Human Hsp90

The design of specific inhibitors against the Hsp90 chaperone and other enzyme relies on the detailed and correct understanding of both the thermodynamics of inhibitor binding and the structural features of the protein-inhibitor complex. Here we present a detailed thermodynamic study of binding of aryl-dihydroxyphenyl-thiadiazole inhibitor series to recombinant human Hsp90 alpha isozyme. The inhibitors are highly potent, with the intrinsic Kd approximately equal to 1 nM as determined by isothermal titration calorimetry (ITC) and thermal shift assay (TSA). Dissection of protonation contributions yielded the intrinsic thermodynamic parameters of binding, such as enthalpy, entropy, Gibbs free energy, and the heat capacity. The differences in binding thermodynamic parameters between the series of inhibitors revealed contributions of the functional groups, thus providing insight into molecular reasons for improved or diminished binding efficiency. The inhibitor binding to Hsp90 alpha primarily depended on a large favorable enthalpic contribution combined with the smaller favorable entropic contribution, thus suggesting that their binding was both enthalpically and entropically optimized. The enthalpy-entropy compensation phenomenon was highly evident when comparing the inhibitor binding enthalpies and entropies. This study illustrates how detailed thermodynamic analysis helps to understand energetic reasons for the binding efficiency and develop more potent inhibitors that could be applied for therapeutic use as Hsp90 inhibitors

Verification of Quantum Computation: An Overview of Existing Approaches

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High-Content, High-Throughput Analysis of Cell Cycle Perturbations Induced by the HSP90 Inhibitor XL888

BACKGROUND: Many proteins that are dysregulated or mutated in cancer cells rely on the molecular chaperone HSP90 for their proper folding and activity, which has led to considerable interest in HSP90 as a cancer drug target. The diverse array of HSP90 client proteins encompasses oncogenic drivers, cell cycle components, and a variety of regulatory factors, so inhibition of HSP90 perturbs multiple cellular processes, including mitogenic signaling and cell cycle control. Although many reports have investigated HSP90 inhibition in the context of the cell cycle, no large-scale studies have examined potential correlations between cell genotype and the cell cycle phenotypes of HSP90 inhibition. METHODOLOGY/PRINCIPAL FINDINGS: To address this question, we developed a novel high-content, high-throughput cell cycle assay and profiled the effects of two distinct small molecule HSP90 inhibitors (XL888 and 17-AAG [17-allylamino-17-demethoxygeldanamycin]) in a large, genetically diverse panel of cancer cell lines. The cell cycle phenotypes of both inhibitors were strikingly similar and fell into three classes: accumulation in M-phase, G2-phase, or G1-phase. Accumulation in M-phase was the most prominent phenotype and notably, was also correlated with TP53 mutant status. We additionally observed unexpected complexity in the response of the cell cycle-associated client PLK1 to HSP90 inhibition, and we suggest that inhibitor-induced PLK1 depletion may contribute to the striking metaphase arrest phenotype seen in many of the M-arrested cell lines. CONCLUSIONS/SIGNIFICANCE: Our analysis of the cell cycle phenotypes induced by HSP90 inhibition in 25 cancer cell lines revealed that the phenotypic response was highly dependent on cellular genotype as well as on the concentration of HSP90 inhibitor and the time of treatment. M-phase arrest correlated with the presence of TP53 mutations, while G2 or G1 arrest was more commonly seen in cells bearing wt TP53. We draw upon previous literature to suggest an integrated model that accounts for these varying observations

Exploring the Trypanosoma brucei Hsp83 Potential as a Target for Structure Guided Drug Design

Human African trypanosomiasis is a neglected parasitic disease that is fatal if untreated. The current drugs available to eliminate the causative agent Trypanosoma brucei have multiple liabilities, including toxicity, increasing problems due to treatment failure and limited efficacy. There are two approaches to discover novel antimicrobial drugs--whole-cell screening and target-based discovery. In the latter case, there is a need to identify and validate novel drug targets in Trypanosoma parasites. The heat shock proteins (Hsp), while best known as cancer targets with a number of drug candidates in clinical development, are a family of emerging targets for infectious diseases. In this paper, we report the exploration of T. brucei Hsp83--a homolog of human Hsp90--as a drug target using multiple biophysical and biochemical techniques. Our approach included the characterization of the chemical sensitivity of the parasitic chaperone against a library of known Hsp90 inhibitors by means of differential scanning fluorimetry (DSF). Several compounds identified by this screening procedure were further studied using isothermal titration calorimetry (ITC) and X-ray crystallography, as well as tested in parasite growth inhibitions assays. These experiments led us to the identification of a benzamide derivative compound capable of interacting with TbHsp83 more strongly than with its human homologs and structural rationalization of this selectivity. The results highlight the opportunities created by subtle structural differences to develop new series of compounds to selectively target the Trypanosoma brucei chaperone and effectively kill the sleeping sickness parasite

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile

High Frequency of Endothelial Colony Forming Cells Marks a Non-Active Myeloproliferative Neoplasm with High Risk of Splanchnic Vein Thrombosis

Increased mobilization of circulating endothelial progenitor cells may represent a new biological hallmark of myeloproliferative neoplasms. We measured circulating endothelial colony forming cells (ECFCs) in 106 patients with primary myelofibrosis, fibrotic stage, 49 with prefibrotic myelofibrosis, 59 with essential thrombocythemia or polycythemia vera, and 43 normal controls. Levels of ECFC frequency for patient's characteristics were estimated by using logistic regression in univariate and multivariate setting. The sensitivity, specificity, likelihood ratios, and positive predictive value of increased ECFC frequency were calculated for the significantly associated characteristics. Increased frequency of ECFCs resulted independently associated with history of splanchnic vein thrombosis (adjusted odds ratio = 6.61, 95% CI = 2.54–17.16), and a summary measure of non-active disease, i.e. hemoglobin of 13.8 g/dL or lower, white blood cells count of 7.8×109/L or lower, and platelet count of 400×109/L or lower (adjusted odds ratio = 4.43, 95% CI = 1.45–13.49) Thirteen patients with splanchnic vein thrombosis non associated with myeloproliferative neoplasms were recruited as controls. We excluded a causal role of splanchnic vein thrombosis in ECFCs increase, since no control had elevated ECFCs. We concluded that increased frequency of ECFCs represents the biological hallmark of a non-active myeloproliferative neoplasm with high risk of splanchnic vein thrombosis. The recognition of this disease category copes with the phenotypic mimicry of myeloproliferative neoplasms. Due to inherent performance limitations of ECFCs assay, there is an urgent need to arrive to an acceptable standardization of ECFC assessment

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

S9.6-based hybrid capture immunoassay for pathogen detection

Abstract The detection of pathogens is critical for clinical diagnosis and public health surveillance. Detection is usually done with nucleic acid-based tests (NATs) and rapid antigen tests (e.g., lateral flow assays [LFAs]). Although NATs are more sensitive and specific, their use is often limited in resource-poor settings due to specialized requirements. To address this limitation, we developed a rapid DNA-RNA Hybrid Capture immunoassay (HC) that specifically detects RNA from pathogens. This assay utilizes a unique monoclonal antibody, S9.6, which binds DNA-RNA hybrids. Biotinylated single-stranded DNA probes are hybridized to target RNAs, followed by hybrid capture on streptavidin and detection with S9.6. The HC-ELISA assay can detect as few as 104 RNA molecules that are 2.2 kb in length. We also adapted this assay into a LFA format, where captured Bacillus anthracis rpoB RNA of 3.5 kb length was detectable from a bacterial load equivalent to 107 CFU per 100 mg of mouse tissue using either HC-ELISA or HC-LFA. Importantly, we also demonstrated the versatility of HC by detecting other pathogens, including SARS-CoV-2 and Toxoplasma gondii, showing its potential for broad pathogen detection. Notably, HC does not require amplification of the target nucleic acid and utilizes economical formats like ELISA and LFA, making it suitable for use in sentinel labs for pathogen detection or as a molecular tool in basic research laboratories. Our study highlights the potential of HC as a sensitive and versatile method for RNA-based pathogen detection