Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes

Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.Comment: Since the publication of this article and among the possible improvements mentioned in the Conclusion, two improvements have been done: extending the algorithm for multivalued logic and considering the basins of attraction of the pathological attractors for selecting the therapeutic bullet

In silico identification of potential inhibitors for human aurora kinase b

Cell cycle progression through mitosis and meiosis involves regulation by serine/threonine kinases from the aurora family. Aurora kinase b (Aurkb) is mainly involved in the proper segregation of chromosomes during mitosis as well as meiosis. However, over expression of Aurkb leads to the unequal distribution of genetic information creating aneuploid cells, a hallmark of cancer. Thus, Aurkb can be used as an effective molecular target for computer-aided drug discovery against cancer. Existing Aurkb inhibitors are less efficient, hence an in silico work was carried out to identify novel potent inhibitors. Three published inhibitors azd1152, zm447439 and N-(4-{[6-methoxy-7-(3-morpholin-4-ylpropoxy) quinazolin- 4-yl] amino} phenyl) benzamide were subjected to high throughput virtual screening of over 1 million entries from a ligand info meta database, to generate a 1161 compound library. The crystal structure was optimized and energy was minimized applying an OPLS force field in Maestro v9.0. Molecular docking using Glide was performed to predict the binding orientation of the prepared ligand molecule into a grid of 20*20*20 Å created around the centroid of the optimized human Aurkb protein. Nine lead molecules with good binding affinity with human Aurkb were identified. In silico pharmacokinetics study for these nine lead molecules has shown no ADME violation. Analysis of lead ‘1’- human Aurkb docking complex has revealed a XP Gscore of -10.20 kcal/mol with a highly stabilized hydrogen bond network with Asp218 and Ala157 and good Van der wall interactions. The docking complex coincides well with the native co- crystallized human Aurkb and inhibitor zm447439 complex. Thus, lead 1 would be highly useful for developing potential drug molecules for the treatment of cancer

In silico identification of potential inhibitors for human aurora kinase b

Cell cycle progression through mitosis and meiosis involves regulation by serine/threonine kinases from the aurora family. Aurora kinase b (Aurkb) is mainly involved in the proper segregation of chromosomes during mitosis as well as meiosis. However, over expression of Aurkb leads to the unequal distribution of genetic information creating aneuploid cells, a hallmark of cancer. Thus, Aurkb can be used as an effective molecular target for computer-aided drug discovery against cancer. Existing Aurkb inhibitors are less efficient, hence an in silico work was carried out to identify novel potent inhibitors. Three published inhibitors azd1152, zm447439 and N-(4-{[6-methoxy-7-(3-morpholin-4-ylpropoxy) quinazolin- 4-yl] amino} phenyl) benzamide were subjected to high throughput virtual screening of over 1 million entries from a ligand info meta database, to generate a 1161 compound library. The crystal structure was optimized and energy was minimized applying an OPLS force field in Maestro v9.0. Molecular docking using Glide was performed to predict the binding orientation of the prepared ligand molecule into a grid of 20*20*20 Å created around the centroid of the optimized human Aurkb protein. Nine lead molecules with good binding affinity with human Aurkb were identified. In silico pharmacokinetics study for these nine lead molecules has shown no ADME violation. Analysis of lead ‘1’- human Aurkb docking complex has revealed a XP Gscore of -10.20 kcal/mol with a highly stabilized hydrogen bond network with Asp218 and Ala157 and good Van der wall interactions. The docking complex coincides well with the native co- crystallized human Aurkb and inhibitor zm447439 complex. Thus, lead 1 would be highly useful for developing potential drug molecules for the treatment of cancer

Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification.

Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines

In silico identification, synthesis and biological evaluation of novel tetrazole inhibitors of MurB

In the context of antibacterial drug discovery resurgence, novel therapeutic targets and new compounds with alternative mechanisms of action are of paramount importance. We focused on UDP-N- acetylenolpyruvylglucosamine reductase (i.e. MurB), an underexploited target enzyme that is involved in early steps of bacterial peptidoglycan biosynthesis. On the basis of the recently reported crystal structure of MurB in complex with NADP+ , a pharmacopohore model was generated and used in a virtual screening campaign with combined structure-based and ligand-based approaches. In order to explore chemical space around hit compounds, further similarity search and organic synthesis was employed to obtain several compounds with micromolar IC50 values on MurB. The best inhibitors in the reported series of 5-substituted tetrazol-2-yl acetamides were compounds 13, 26 and 30 with IC50 values of 34, 28 and 25 µM, respectively. None of the reported compounds possessed in vitro antimicrobial activity against S. aureus and E. coli

In vitro antibiofilm activity-directed in silico identification of natural products targeting bacterial biofilm regulators SarA and LasR

Background: Antibiofilm agents serve as an essential tool in the fight against antibiotic resistance, and natural products provide a promising source for potential drug leads. Objective: This study investigates the activity of twenty Bangladeshi medicinal plants against Staphylococcus aureus and Pseudomonas aeruginosa biofilms and predicts the interactions of selected phytochemicals from five of the best performing plants with the active sites of transcriptional regulatory proteins SarA of S. aureus and LasR of P. aeruginosa. Methods: The plant extracts were tested by microtiter plate-based assay against S. aureus and P. aeruginosa biofilms. Molecular docking and molecular dynamics simulation (MD) were conducted using PyRx and GROMACS, respectively. Results: The best activity was identified for Cassia fistula and Ananas comosus, showing ≥ 75% inhibition of biofilm formation. ent-Epicatechin-(4α→8)-epiafzelechin (EEE) of C. fistula, cyanidin-3,3',5-tri-O-β-D-glucopyranoside (CTG) of A. comosus, and 7-O-(4-hydroxy-Ecinnamoyl)- spinoside of A. spinosus showed the best predictive binding affinity (-7.6, -7.6 and - 7.7 kcal/mol, respectively) for SarA. EEE was the only ligand to exhibit a stable ligand-protein complex with SarA in the MD simulation of 200 ns (binding energy of MMPBSA analysis - 39.899 kJ/mol). Chrysophanol, epicatechin and physcion, of C. fistula (-10.5, -10.5, and -11.0 kcal/mol, respectively) and auraptene of F. limonia (-10.8 kcal/mol) showed the best predictive binding affinity for LasR. Epicatechin showed the most stable ligand-protein complex with LasR (binding energy of MMPBSA analysis -63.717 kJ/mol) Conclusion: Epicatechin and its derivative EEE could be used as scaffolds for the development of new antibiofilm agents against P. aeruginosa and S. aureus, respectively

A nonparametric approach for model individualization in an artificial pancreas

The identification of patient-tailored linear time invariant glucose-insulin models is investigated for type 1 diabetic patients, that are characterized by a substantial inter-subject variability. The individualized linear models are identified by considering a novel kernel-based nonparametric approach and are compared with a linear time invariant average model in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean squared error. Model identification and validation are based on in-silico data collected from the adult virtual population of the UVA/Padova simulator. The data generation involves a protocol designed to produce a sufficient input excitation without compromising patient safety, compatible also with real life scenarios. The identified models are exploited to synthesize an individualized Model Predictive Controller (MPC) for each patient, which is used in an Artificial Pancreas to maintain the blood glucose concentration within an euglycemic range. The MPC used in several clinical studies, synthesized on the basis of a non-individualized average linear time invariant model, is also considered as reference. The closed-loop control performance is evaluated in an in-silico study on the adult virtual population of the UVA/Padova simulator in a perturbed scenario, in which the MPC is blind to random variations of insulin sensitivity in each virtual patient. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy.

In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included