@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 {4,
	title = {Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts.},
	journal = {Bioinformatics (Oxford, England)},
	volume = {25},
	year = {2009},
	month = {2009 May 15},
	pages = {1264-70},
	abstract = {MOTIVATION: Correct prediction of residue-residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail.

RESULTS: We propose a novel hidden Markov model (HMM)-based method for predicting residue-residue contacts from protein sequences using as training data homologous sequences, predicted secondary structure and a library of local neighborhoods (local descriptors of protein structure). The library consists of recurring structural entities incorporating short-, medium- and long-range interactions and is general enough to reassemble the cores of nearly all proteins in the PDB. The method is tested on an external test set of 606 domains with no significant sequence similarity to the training set as well as 151 domains with SCOP folds not present in the training set. Considering the top 0.2 x L predictions (L = sequence length), our HMMs obtained an accuracy of 22.8\% for long-range interactions in new fold targets, and an average accuracy of 28.6\% for long-, medium- and short-range contacts. This is a significant performance increase over currently available methods when comparing against results published in the literature.

AVAILABILITY: http://predictioncenter.org/Services/FragHMMent/.},
	keywords = {Computational Biology, Databases, Protein, Markov Chains, Models, Molecular, Protein Folding, Protein Structure, Secondary, Proteins},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/btp149},
	author = {Bj{\"o}rkholm, Patrik and Daniluk, Pawe{\l} and Kryshtafovych, Andriy and Fidelis, Krzysztof and Andersson, Robin and Hvidsten, Torgeir R}
}
