PurposeThe tumor suppressor p53 is most commonly mutated in human cancer. The structural mutant in the β-sandwich of the protein, p53-Y220C, is the ninth most common p53 mutant. The p53-Y220C mutant has a solvent-acc...
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PurposeThe tumor suppressor p53 is most commonly mutated in human cancer. The structural mutant in the β-sandwich of the protein, p53-Y220C, is the ninth most common p53 mutant. The p53-Y220C mutant has a solvent-accessible hydrophobic pocket, leading to thermal destabilization of the protein. Screening of our covalent fragment library (CovLib) revealed the highly reactive pyrazine derivatives SN006 and SN007, which arylate among other cysteines in p53, the mutation-generated Cys220. Herein, comprehensive structure-activity relationship (SAR) studies of these intrinsically reactive CovLib hits were performed, aiming to identify improved stabilizers for p53-Y220C, with a more balanced reactivity profile, diverse binding modes and a better potential for chemical *** compounds were screened for enhanced stabilization of p53 wild type and its mutants using differential scanning fluorimetry (DSF). To confirm covalent modification, intact mass spectrometry was performed. Thiol reactivity profiles were determined using a standardized Glutathione-modifying (GSH) assay. The binding modes of the identified hits and covalent modification of Cys220 were elucidated by X-ray crystallography. Moreover, the influence of the hits on the DNA-binding affinity of full-length p53 was investigated employing a fluorescence polarization assay (FPA).Results and ConclusionThe promising pyrazine derivatives SN006/7-3, SN006/7-8, and SN006/7-9 were identified, occupying different subsites of the Y220C binding pocket. The compound SN006/7-8 substantially stabilized the thermosensitive cancer mutant Y220C by up to 5.0 °C, representing a strong enhancement over SN006 (1.8 °C) and SN007 (2.0 °C).Graphical AbstractDisplay full size
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An improved genetic algorithm based on information entropy is presented in this paper. As a new iteration scheme in conjunction with multi-population genetic strategy, entropy-based searching technique with narrowing ...
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ISBN:
(纸本)0780384032
An improved genetic algorithm based on information entropy is presented in this paper. As a new iteration scheme in conjunction with multi-population genetic strategy, entropy-based searching technique with narrowing down space and the quasi-exact penalty function is developed to solve nonlinear programming (NLP) problems with equality and inequality constraints. A specific strategy of reserving the fittest member with evolutionary historic information is effectively used to approximate the solution of the nonlinear programming problems to the global optimization. Numerical examples show that the proposed method has good accuracy and efficiency.
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