Integration of Evolution to Avoid Resistance in Structure Based Drug Design

在基于结构的药物设计中整合进化以避免耐药性 困扰我们社会的许多最致命的疾病发展迅速，挑战着我们的 治疗策略。耐药是这种进化的结果，当药物压力 改变分子识别事件的平衡，选择性地削弱抑制剂的结合 同时保持治疗靶点的生物功能。扰乱了治疗 目标的行动是必要的，但不足以避免阻力。我们假设 抗性进化的多维格局可以通过整合 揭示关键途径和耦合分子的实验和计算数据 抗药性的机制。此外，我们假设这一战略可以被纳入 进入基于结构的药物设计，以评估新的抑制剂。 耐药性发生在渐进和持续的药物压力下，有趣的是， 突变并不局限于药物靶标的活性部位，而是可以在整个酶中发生。 以赋予高度的抵抗力。这种抗性发生的分子机制是 不清楚。我们的目标是开发丰富而通用的实验数据，集成缓蚀剂 具有内部一致性的系综动力学的效力和晶体结构 用机器学习的方式阐明耐药的分子机制 并生成抑制剂效力的预测模型。

Integration of Evolution to Avoid Resistance in Structure Based Drug Design Many of the most deadly diseases that plague our society evolve quickly, challenging our therapeutic strategies. Drug resistance occurs as a result of this evolution when drug pressure changes the balance of molecular recognition events, selectively weakening inhibitor binding while maintaining the biological function of the therapeutic target. Disrupting the therapeutic target’s activity is necessary but not sufficient to avoid resistance. We hypothesize that the multi-dimensional landscape of resistance evolution can be elucidated through integration of experimental and computational data to reveal the key pathways and coupled molecular mechanisms to resistance. Furthermore, we hypothesize that this strategy can be incorporated into structure-based drug design to evaluate novel inhibitors. Resistance occurs under gradual and persistent drug pressure, and interestingly the mutations are not limited to the active site of a drug target, but can occur throughout the enzyme to confer high levels of resistance. The molecular mechanism by which this resistance occurs is not clear. Our aim is to exploit the rich and versatile experimental data, integrating inhibitor potency and crystallographic structures with ensemble dynamics in an internally consistent manner using machine learning to elucidate both the molecular mechanisms of drug resistance and generate predictive models of inhibitor potency.