@article {48,
	title = {A genistein derivative, ITB-301, induces microtubule depolymerization and mitotic arrest in multidrug-resistant ovarian cancer.},
	journal = {Cancer chemotherapy and pharmacology},
	volume = {68},
	year = {2011},
	month = {10/2011},
	pages = {1033-44},
	abstract = {PURPOSE: To investigate the mechanistic basis of the anti-tumor effect of the compound ITB-301.

METHODS: Chemical modifications of genistein have been introduced to improve its solubility and efficacy. The anti-tumor effects were tested in ovarian cancer cells using proliferation assays, cell cycle analysis, immunofluorescence, and microscopy.

RESULTS: In this work, we show that a unique glycoside of genistein, ITB-301, inhibits the proliferation of SKOv3 ovarian cancer cells. We found that the 50\% growth inhibitory concentration of ITB-301 in SKOv3 cells was 0.5~μM. Similar results were obtained in breast cancer, ovarian cancer, and acute myelogenous leukemia cell lines. ITB-301 induced significant time- and dose-dependent microtubule depolymerization. This depolymerization resulted in mitotic arrest and inhibited proliferation in all ovarian cancer cell lines examined including SKOv3, ES2, HeyA8, and HeyA8-MDR cells. The cytotoxic effect of ITB-301 was dependent on its induction of mitotic arrest as siRNA-mediated depletion of BUBR1 significantly reduced the cytotoxic effects of ITB-301, even at a concentration of 10~μM. Importantly, efflux-mediated drug resistance did not alter the cytotoxic effect of ITB-301 in two independent cancer cell models of drug resistance.

CONCLUSION: These results identify ITB-301 as a novel anti-tubulin agent that could be used in cancers that are multidrug resistant. We propose a structural model for the binding of ITB-301 to α- and β-tubulin dimers on the basis of molecular docking simulations. This model provides a rationale for future work aimed at designing of more potent analogs.},
	keywords = {Antineoplastic Agents, Cell Line, Tumor, Cell Proliferation, Dose-Response Relationship, Drug, Drug Resistance, Multiple, Drug Resistance, Neoplasm, Female, Genistein, Glycosides, Humans, Inhibitory Concentration 50, Microtubules, Mitosis, Models, Molecular, Molecular Dynamics Simulation, Ovarian Neoplasms, Protein Binding, Tubulin},
	issn = {1432-0843},
	doi = {10.1007/s00280-011-1575-2},
	author = {Ahmed, Ahmed Ashour and Goldsmith, Juliet and Fokt, Izabela and Le, Xiao-Feng and Krzy{\'s}ko, Krystiana A and Lesyng, Bogdan and Bast, Robert C and Priebe, Waldemar}
}
@article {3,
	title = {A novel method to compare protein structures using local descriptors.},
	journal = {BMC bioinformatics},
	volume = {12},
	year = {2011},
	month = {08/2011},
	pages = {344},
	abstract = {BACKGROUND: Protein structure comparison is one of the most widely performed tasks in bioinformatics. However, currently used methods have problems with the so-called "difficult similarities", including considerable shifts and distortions of structure, sequential swaps and circular permutations. There is a demand for efficient and automated systems capable of overcoming these difficulties, which may lead to the discovery of previously unknown structural relationships.

RESULTS: We present a novel method for protein structure comparison based on the formalism of local descriptors of protein structure - DEscriptor Defined Alignment (DEDAL). Local similarities identified by pairs of similar descriptors are extended into global structural alignments. We demonstrate the method{\textquoteright}s capability by aligning structures in difficult benchmark sets: curated alignments in the SISYPHUS database, as well as SISY and RIPC sets, including non-sequential and non-rigid-body alignments. On the most difficult RIPC set of sequence alignment pairs the method achieves an accuracy of 77\% (the second best method tested achieves 60\% accuracy).

CONCLUSIONS: DEDAL is fast enough to be used in whole proteome applications, and by lowering the threshold of detectable structure similarity it may shed additional light on molecular evolution processes. It is well suited to improving automatic classification of structure domains, helping analyze protein fold space, or to improving protein classification schemes. DEDAL is available online at http://bioexploratorium.pl/EP/DEDAL.},
	keywords = {Algorithms, Animals, Bacterial Proteins, Carrier Proteins, Computational Biology, GTP Phosphohydrolases, Humans, Models, Molecular, Proteins, Saposins, Structural Homology, Protein},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-12-344},
	author = {Daniluk, Pawe{\l} and Lesyng, Bogdan}
}
