Mitoketoscins : novel mitochondrial inhibitors for targeting ketone metabolism in cancer stem cells (CSCs).

Previous studies have now well-established that epithelial cancer cells can utilize ketone bodies (3-hydroxybutyrate and aceto-acetate) as mitochondrial fuels, to actively promote tumor growth and metastatic dissemination. The two critical metabolic enzymes implicated in this process are OXCT1 and ACAT1, which are both mitochondrial proteins. Importantly, over-expression of OXCT1 or ACAT1 in human breast cancer cells is sufficient to genetically drive tumorigenesis and/or lung metastasis, validating that they indeed behave as metabolic "tumor promoters". Here, we decided to target these two enzymes, which give cancer cells the ability to recycle ketone bodies into Acetyl-CoA and, therefore, to produce increased ATP. Briefly, we used computational chemistry (in silico drug design) to select a sub-set of potentially promising compounds that spatially fit within the active site of these enzymes, based on their known 3D crystal structures. These libraries of compounds were then phenotypically screened for their effects on total cellular ATP levels. Positive hits were further validated by metabolic flux analysis. Our results indicated that four of these compounds effectively inhibited mitochondrial oxygen consumption. Two of these compounds also induced a reactive glycolytic phenotype in cancer cells. Most importantly, using the mammosphere assay, we showed that these compounds can be used to functionally inhibit cancer stem cell (CSC) activity and propagation. Finally, our molecular modeling studies directly show how these novel compounds are predicted to bind to the active catalytic sites of OXCT1 and ACAT1, within their Coenzyme A binding site. As such, we speculate that these mitochondrial inhibitors are partially mimicking the structure of Coenzyme A. Thus, we conclude that OXCT1 and ACAT1 are important new therapeutic targets for further drug development and optimization. We propose that this new class of drugs should be termed "mitoketoscins", to reflect that they were designed to target ketone re-utilization and mitochondrial function

Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome

How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to “reproducibility maps” that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one’s own laboratory

Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest

Mitoriboscins : mitochondrial-based therapeutics targeting cancer stem cells (CSCs), bacteria and pathogenic yeast

The “endo-symbiotic theory of mitochondrial evolution” states that mitochondrial organelles evolved from engulfed aerobic bacteria, after millions of years of symbiosis and adaptation. Here, we have exploited this premise to design new antibiotics and novel anti-cancer therapies, using a convergent approach. First, virtual high-throughput screening (vHTS) and computational chemistry were used to identify novel compounds binding to the 3D structure of the mammalian mitochondrial ribosome. The resulting library of ~880 compounds was then subjected to phenotypic drug screening on human cancer cells, to identify which compounds functionally induce ATP-depletion, which is characteristic of mitochondrial inhibition. Notably, the top ten “hit” compounds define four new classes of mitochondrial inhibitors. Next, we further validated that these novel mitochondrial inhibitors metabolically target mitochondrial respiration in cancer cells and effectively inhibit the propagation of cancer stem-like cells in vitro. Finally, we show that these mitochondrial inhibitors possess broad-spectrum antibiotic activity, preventing the growth of both gram-positive and gram-negative bacteria, as well as C. albicans – a pathogenic yeast. Remarkably, these novel antibiotics also were effective against methicillin-resistant Staphylococcus aureus (MRSA). Thus, this simple, yet systematic, approach to the discovery of mitochondrial ribosome inhibitors could provide a plethora of anti-microbials and anti-cancer therapies, to target drug-resistance that is characteristic of both i) tumor recurrence and ii) infectious disease. In summary, we have successfully used vHTS combined with phenotypic drug screening of human cancer cells to identify several new classes of broad-spectrum antibiotics that target both bacteria and pathogenic yeast. We propose the new term “mitoriboscins” to describe these novel mitochondrial-related antibiotics. Thus far, we have identified four different classes of mitoriboscins, such as: 1) mitoribocyclines, 2) mitoribomycins, 3) mitoribosporins and 4) mitoribofloxins. However, we broadly define mitoriboscins as any small molecule(s) or peptide(s) that bind to the mitoribosome (large or small subunits) and, as a consequence, inhibit mitochondrial function, i.e., mitoribosome inhibitors

Knowledge Discovery and Prediction Modeling of Protein-Drug Binding Kinetic by Integrating Machine Learning, Normal Mode Analysis and Molecular Dynamics Simulation

One of the unaddressed challenges in drug discovery is that drug potency measured by protein-ligand binding affinity, such as IC50 and Kd in vitro, is not correlated with drug activity in vivo. Computational modeling is playing an increasing role in designing efficient therapeutics. However, existing computational methods for the high-throughput study of protein-ligand interactions (PLI) mainly focus on the prediction of the binding affinity. This is the combined effect of association (kon) and dissociation (koff) rate constants. Few works have been produced to predict koff or its reciprocal, residence time, which is a key measuring function of drug efficacy in vivo. This study addresses the unmet need of the accurate and scalable prediction of kon and koff simultaneously. The fundamental strategy of our method is to develop a machine learning model using PLI kinetic features computed by normal mode analysis (NMA). To test our method, HIV-1 protease complex was used as a model system. There are three major findings of this study. First, kinetic properties are more important than thermal dynamic characteristics in determining protein-ligand binding kinetics. We propose that coherent conformational dynamics coupling between protein and ligand were proven to be more significant than pairwise residue binding energy in the prediction of kinetic rate constants. Second, NMA is an efficient method to capture conformational dynamics features for the large scale modeling of protein-ligand binding. Third, multi-target classification as well as multi-target regression, is a potentially valuable tool for modeling PLI kinetics. With the rapid increase of PLI kinetics data, the further improvement of proposed computational methodology may provide a powerful tool for large-scale modeling of PLI kinetics, thereby accelerating drug discovery process

Development of Methods for the Investigation of RNA-Ligand Interactions.

Three critical features of RNA make it a unique challenge for drug discovery: a) it is highly negatively charged, increasing non-specific binding, b) it can be highly dynamic, adopting different conformations upon binding varying ligands, and c) it has solvent exposed shallow binding pockets. All these properties represent distinct problems in the advancement of RNA-drug discovery. To address this first problem, MATCH was developed to rapidly, accurately, and universally parameterize small molecules for docking. MATCH accomplishes this by deconstructing a force field into a set of fundamental rules which best replicates existing parameters and permits extension to new molecules. MATCH is not only necessary to study RNA-ligand interactions en masse but will also contribute to understanding the charge-charge consequences of ligand binding. To address RNA flexibility, a method to combine NMR chemical shifts and Molecular Dynamics (MD) was developed to generate dynamic ensembles. To benchmark this technique, a set of 26 RNA structures with experimentally determined chemical shift was selected. An ensemble of structures was optimized to match the chemical shifts of each system. These ensembles were also shown to be consistent with of NMR NOE and RDCs constraints. To further demonstrate the utility of this method a large pool of structures (~350,000) was used to generate an ensemble for a prominent RNA target – the ribosomal decoding site. The conformations within this ensemble were found on favorable areas of the free energy landscape, independently indicating the validity of these structures. Finally to address the solvent exposed binding pocket of RNA and its flexible ligands, a new docking approach for RNA was developed, which performs an enhanced sampling technique by fragmenting the ligand and independently optimizing the conformation of each fragment. To properly benchmark this novel algorithm, a large set of 230 nucleic acid-ligand complexes was compiled. Utilizing this large set of this enhanced sampling technique was compared to ICM – a leading docking program. ICM produced native-like conformations 45% of the time, while our approach yields native-like conformations 55% of the time. Demonstrating the effectiveness of this novel sampling procedure.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102297/1/jyesselm_1.pd

The Role of Transglutaminases in the Development of Abdominal Aortic Aneurysms

Abdominal aortic aneurysms (AAA) are dilatations of the abdominal aorta that are prone to rupture with fatal consequences. AAAs (diameter >3cm) are present in ~4% of men aged >65years. AAA formation is initiated with the loss of medial elastin. Responses to this include synthesis of tropoelastin and deposition of collagen. Dilatation occurs following degradation of this collagen, secondary to release and activation of matrix metalloproteinases (MMPs) by invading macrophages. Elastin is strengthened and protected from proteolysis by cross-linking. Transglutaminases (TGs) introduce cross-links between protein chains and have been implicated in arterial repair; TG2 has been shown to be induced early in experimental aneurysm development. The literature suggests that TG2 and the homologous enzyme FXIII-A may act cooperatively or may compensate for each other in the face of deficiency. We have bred TG2-/-, FXIII-A-/- and TG2-/-.FXIII-A-/- double knockout (DKO) mice to characterise their basal vessel structure and function and investigate their susceptibility to aneurysm formation. This work has shown that both FXIII-A and TG2 are involved in the maintenance of basal vessel integrity and that aortic permeability is increased in mice lacking FXIII-A. In the absence of the repair function of TG2, the DKO mice develop extensive cardiovascular fibrosis and exhibit decreased vessel tension. We have not seen evidence of a clear protective effect of TG2, however our DKO animals showed an (unexpected) decreased propensity to aneurysm formation. In an extended model there is evidence that aneurysm initiation and progression occur by different mechanisms and that TG2 plays a role in prevention of the latter. This thesis has also shown that TG2 and/or FXIII-A are not essential for vascular calcification. The results presented here help to define the common and distinct functions of FXIII-A and TG2 in arterial structure and function, and provide evidence in their evaluation as potential therapeutic targets