Application of fragment-based screening to the design of inhibitors of Escherichia coli DsbA

The thiol-disulfide oxidoreductase enzyme DsbA catalyzes the formation of disulfide bonds in the periplasm of Gram-negative bacteria. DsbA substrates include proteins involved in bacterial virulence. In the absence of DsbA, many of these proteins do not fold correctly, which renders the bacteria avirulent. Thus DsbA is a critical mediator of virulence and inhibitors may act as antivirulence agents. Biophysical screening has been employed to identify fragments that bind to DsbA from Escherichia coli. Elaboration of one of these fragments produced compounds that inhibit DsbA activity in vitro. In cell-based assays, the compounds inhibit bacterial motility, but have no effect on growth in liquid culture, which is consistent with selective inhibition of DsbA. Crystal structures of inhibitors bound to DsbA indicate that they bind adjacent to the active site. Together, the data suggest that DsbA may be amenable to the development of novel antibacterial compounds that act by inhibiting bacterial virulence.

Targeting low-druggability bromodomains: fragment based screening and inhibitor design against the BAZ2B bromodomain

Bromodomains are epigenetic reader domains that have recently become popular targets. In contrast to BET bromodomains, which have proven druggable, bromodomains from other regions of the phylogenetic tree have shallower pockets. We describe successful targeting of the challenging BAZ2B bromodomain using biophysical fragment screening and structure-based optimization of high ligand-efficiency fragments into a novel series of low-micromolar inhibitors. Our results provide attractive leads for development of BAZ2B chemical probes and indicate the whole family may be tractable.

Fragment-Based QSAR and Molecular Modeling Studies on a Series of Discodermolide Analogs as Microtubule-Stabilizing Anticancer Agents

Inhibition of microtubule function is an attractive rational approach to anticancer therapy. Although taxanes are the most prominent among the microtubule-stabilizers, their clinical toxicity, poor pharmacokinetic properties, and resistance have stimulated the search for new antitumor agents having the same mechanism of action. Discodermolide is an example of nontaxane natural product that has the same mechanism of action, demonstrating superior antitumor efficacy and therapeutic index. The extraordinary chemical and biological properties have qualified discodermolide as a lead structure for the design of novel anticancer agents with optimized therapeutic properties. In the present work, we have employed a specialized fragment-based method to develop robust quantitative structure - activity relationship models for a series of synthetic discodermolide analogs. The generated molecular recognition patterns were combined with three-dimensional molecular modeling studies as a fundamental step on the path to understanding the molecular basis of drug-receptor interactions within this important series of potent antitumoral agents.

Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q(2) = 0.68 and r(2) = 0.72; HQSAR, q(2) = 0.63 and r(2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(pred)(2) = 0.87; classical QSAR, r(pred)(2) = 0.75).

Classical and fragment-based hologram structure-activity relationships for a series of analgesic cyclic imides

Cyclic imides have been widely employed in drug design research due to their multiple pharmacological and biological properties. In the present study, two-dimensional quantitative structure-activity relationship (2D QSAR) studies were conducted on a series of potent analgesic cyclic imides using both classical and hologram QSAR (HQSAR) methods, yielding significant statistical models (classical QSAR, q(2) = 0.80; HQSAR, q(2) = 0.84). The models were then used to evaluate an external data test, and the predicted values were in good agreement with the experimental results, indicating their consistency for untested compounds.

A fragment-based approach for ligand binding affinity and selectivity for the liver X receptor beta

Selective modulation of liver X receptor beta (LXR beta) has been recognized as an important approach to prevent or reverse the atherosclerotic process. In the present work, we have developed robust conformation-independent fragment-based quantitative structure-activity and structure-selectivity relationship models for a series of quinolines and cinnolines as potent modulators of the two LXR sub-types. The generated models were then used to predict the potency of an external test set and the predicted values were in good agreement with the experimental results, indicating the potential of the models for untested compounds. The final 2D molecular recognition patterns obtained were integrated to 3D structure-based molecular modeling studies to provide useful insights into the chemical and structural determinants for increased LXR beta binding affinity and selectivity. (C) 2011 Elsevier Inc. All rights reserved.

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.

Fragment-based version management for repositories of business process models

As organizations reach higher levels of Business Process Management maturity, they tend to accumulate large collections of process models. These repositories may contain thousands of activities and be managed by different stakeholders with varying skills and responsibilities. However, while being of great value, these repositories induce high management costs. Thus, it becomes essential to keep track of the various model versions as they may mutually overlap, supersede one another and evolve over time. We propose an innovative versioning model and associated storage structure, specifically designed to maximize sharing across process model versions, and to automatically handle change propagation. The focal point of this technique is to version single process model fragments, rather than entire process models. Indeed empirical evidence shows that real-life process model repositories have numerous duplicate fragments. Experiments on two industrial datasets confirm the usefulness of our technique.

Fragment-based version management for repositories of business process models

As organizations reach higher levels of Business Process Management maturity, they tend to accumulate large collections of process models. These repositories may contain thousands of activities and be managed by different stakeholders with varying skills and responsibilities. However, while being of great value, these repositories induce high management costs. Thus, it becomes essential to keep track of the various model versions as they may mutually overlap, supersede one another and evolve over time. We propose an innovative versioning model, and associated storage structure, specifically designed to maximize sharing across process models and process model versions, reduce conflicts in concurrent edits and automatically handle controlled change propagation. The focal point of this technique is to version single process model fragments, rather than entire process models. Indeed empirical evidence shows that real-life process model repositories have numerous duplicate fragments. Experiments on two industrial datasets confirm the usefulness of our technique.

Developing an IS-Impact decision tool : a literature based design science roadmap

This paper derives from research-in-progress intending both Design Research (DR) and Design Science (DS) outputs; the former a management decision tool based in IS-Impact (Gable et al. 2008) kernel theory; the latter being methodological learnings deriving from synthesis of the literature and reflection on the DR ‘case study’ experience. The paper introduces a generic, detailed and pragmatic DS ‘Research Roadmap’ or methodology, deriving at this stage primarily from synthesis and harmonization of relevant concepts identified through systematic archival analysis of related literature. The scope of the Roadmap too has been influenced by the parallel study aim to undertake DR applying and further evolving the Roadmap. The Roadmap is presented in attention to the dearth of detailed guidance available to novice Researchers in Design Science Research (DSR), and though preliminary, is expected to evolve and gradually be substantiated through experience of its application. A key distinction of the Roadmap from other DSR methods is its breadth of coverage of published DSR concepts and activities; its detail and scope. It represents a useful synthesis and integration of otherwise highly disparate DSR-related concepts.

Relative entropy rate based design for linear hybrid system models

Hybrid system representations have been applied to many challenging modeling situations. In these hybrid system representations, a mixture of continuous and discrete states is used to capture the dominating behavioural features of a nonlinear, possible uncertain, model under approximation. Unfortunately, the problem of how to best design a suitable hybrid system model has not yet been fully addressed. This paper proposes a new joint state measurement relative entropy rate based approach for this design purpose. Design examples and simulation studies are presented which highlight the benefits of our proposed design approaches.

Sky high and back again : the evolution of simulation-based design from aerospace to construction

Emerging from the challenge to reduce energy consumption in buildings is a need for research and development into the more effective use of simulation as a decision-support tool. Despite significant research, persistent limitations in process and software inhibit the integration of energy simulation in early architectural design. This paper presents a green star case study to highlight the obstacles commonly encountered with current integration strategies. It then examines simulation-based design in the aerospace industry, which has overcome similar limitations. Finally, it proposes a design system based on this contrasting approach, coupling parametric modelling and energy simulation software for rapid and iterative performance assessment of early design options.