94 research outputs found

    Folding of small proteins: A matter of geometry?

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    We review some of our recent results obtained within the scope of simple lattice models and Monte Carlo simulations that illustrate the role of native geometry in the folding kinetics of two state folders.Comment: To appear in Molecular Physic

    Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

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    <p>Abstract</p> <p>Background</p> <p>By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.</p> <p>Results</p> <p>First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.</p> <p>Conclusion</p> <p>By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.</p

    Candidate biomarkers of PARP inhibitor sensitivity in ovarian cancer beyond the BRCA genes

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    BACKGROUND: Olaparib (Lynparza™) is a PARP inhibitor approved for advanced BRCA-mutated (BRCAm) ovarian cancer. PARP inhibitors may benefit patients whose tumours are dysfunctional in DNA repair mechanisms unrelated to BRCA1/2. We report exploratory analyses, including the long-term outcome of candidate biomarkers of sensitivity to olaparib in BRCA wild-type (BRCAwt) tumours. METHODS: Tumour samples from an olaparib maintenance monotherapy trial (Study 19, D0810C00019; NCT00753545) were analysed. Analyses included classification of mutations in genes involved in homologous recombination repair (HRR), BRCA1 promoter methylation status, measurement of BRCA1 protein and Myriad HRD score. RESULTS: Patients with BRCAm tumours gained most benefit from olaparib; a similar treatment benefit was also observed in 21/95 patients whose tumours were BRCAwt but had loss-of-function HRR mutations compared to patients with no detectable HRR mutations (58/95). A higher median Myriad MyChoice® HRD score was observed in BRCAm and BRCAwt tumours with BRCA1 methylation. Patients without BRCAm tumours derived benefit from olaparib treatment vs placebo although to a lesser extent than BRCAm patients.CONCLUSIONS: Ovarian cancer patients with tumours harbouring loss-of-function mutations in HRR genes other than BRCA1/2 may constitute a small, molecularly identifiable and clinically relevant population who derive treatment benefit from olaparib similar to patients with BRCAm

    Iodine-125 brachytherapy for brain tumours - a review

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    Iodine-125 brachytherapy has been applied to brain tumours since 1979. Even though the physical and biological characteristics make these implants particularly attractive for minimal invasive treatment, the place for stereotactic brachytherapy is still poorly defined

    Antiangiogenic agents in the treatment of recurrent or newly diagnosed glioblastoma: Analysis of single-agent and combined modality approaches

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    Surgical resection followed by radiotherapy and temozolomide in newly diagnosed glioblastoma can prolong survival, but it is not curative. For patients with disease progression after frontline therapy, there is no standard of care, although further surgery, chemotherapy, and radiotherapy may be used. Antiangiogenic therapies may be appropriate for treating glioblastomas because angiogenesis is critical to tumor growth. In a large, noncomparative phase II trial, bevacizumab was evaluated alone and with irinotecan in patients with recurrent glioblastoma; combination treatment was associated with an estimated 6-month progression-free survival (PFS) rate of 50.3%, a median overall survival of 8.9 months, and a response rate of 37.8%. Single-agent bevacizumab also exceeded the predetermined threshold of activity for salvage chemotherapy (6-month PFS rate, 15%), achieving a 6-month PFS rate of 42.6% (p < 0.0001). On the basis of these results and those from another phase II trial, the US Food and Drug Administration granted accelerated approval of single-agent bevacizumab for the treatment of glioblastoma that has progressed following prior therapy. Potential antiangiogenic agents-such as cilengitide and XL184-also show evidence of single-agent activity in recurrent glioblastoma. Moreover, the use of antiangiogenic agents with radiation at disease progression may improve the therapeutic ratio of single-modality approaches. Overall, these agents appear to be well tolerated, with adverse event profiles similar to those reported in studies of other solid tumors. Further research is needed to determine the role of antiangiogenic therapy in frontline treatment and to identify the optimal schedule and partnering agents for use in combination therapy
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