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Application of MM-GB/SAand WaterMap to SRC KinaseInhibitor Potency Prediction

机译:MM-GB / SA的应用和WaterMap到SRC激酶抑制剂效能预测

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摘要

WaterMap and MM-GB/SA scoring methods were applied to an extensive congeneric series of small-molecule SRC inhibitors with high-quality enzyme data and well characterized binding modes to compare the performance of these scoring methods in this data set and to provide insight into the relative strengths of each method. Only minor conformational changes in SRC bound with representative DFG-in class of inhibitors were demonstrated in previous studies; thus, the protein flexibility that normally presents a challenge to pose and potency predictions was minimized in this model system. While WaterMap correctly recognized major trends in the SAR of this series, MM-GB/SA performed better in ranking the relative ligand affinities. The different scoring methods were further analyzed to determine which aspects of series SAR were more amenable to MM-GB/SA than WaterMap scoring.
机译:将WaterMap和MM-GB / SA评分方法应用于具有高质量酶数据和特征明确的结合模式的一系列广泛的小分子SRC抑制剂系列,以比较这些评分方法在该数据集中的性能并提供深入的了解每种方法的相对优势。在先前的研究中,仅证明了与代表性的DFG-in抑制剂结合的SRC的构象变化很小;因此,在此模型系统中,通常会给姿势和效价预测带来挑战的蛋白质柔韧性已降至最低。尽管WaterMap正确识别了该系列SAR的主要趋势,但MM-GB / SA在对相对配体亲和力进行排名时表现更好。进一步分析了不同的评分方法,以确定与Water-Map评分相比,系列SAR的哪些方面更适合MM-GB / SA。

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