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Flexible side chain models improve enrichment rates in in silico screening

机译:灵活的侧链模型可提高计算机筛选中的富集率

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摘要

While modem docking methods often predict accurate binding modes, affinity calculations remain challenging and enrichment rates of in silico screening methods unsatisfactory. Inadequate treatment of induced fit effects is one major shortcoming of existing in silico screening methods. Here we investigate enrichment rates of rigid-, soft- and flexible-receptor models for 12 diverse receptors using libraries containing up to 13000 molecules. For the rigid-receptor model, we observed high enrichment (EF1 > 20) only for four target proteins. A soft-receptor model showed improved docking rates at the expense of reduced enrichment rates. A flexible side-chain model with flexible dihedral angles for up to 12 amino acids (3-8 flexible side chains) increased both binding propensity and enrichment rates: EF1 values increased by similar to 35% on average with respect to rigid docking. We find on average 4 known ligands in the top 10 molecules in the rank-ordered databases for the receptors investigated.
机译:尽管现代的对接方法通常可以预测准确的结合模式,但亲和力计算仍具有挑战性,计算机筛选方法的富集率也不令人满意。诱导拟合效应的治疗不足是现有计算机筛选方法的主要缺点之一。在这里,我们使用包含多达13000个分子的库来研究12种不同受体的刚性,软性和柔性受体模型的富集率。对于刚性受体模型,我们仅对四个靶蛋白观察到了高富集(EF1> 20)。软受体模型显示出提高的对接速率,但降低了富集速率。具有多达12个氨基酸的柔性二面角的柔性侧链模型(3-8个柔性侧链)增加了结合倾向和富集率:相对于刚性对接,EF1值平均增加了约35%。我们在所研究的受体的排序数据库中的前10个分子中平均发现4个已知配体。

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