使用PLACER模型建模蛋白质小分子构造组合
Ivan Anishchenko1,2, Yakov Kipnis1,2,3, Indrek Kalvet1,2,3
1Department of Biochemistry, University of Washington, Seattle, WA 98105.
概括
我们开发了PLACER,这是一个图形神经网络,用于蛋白质 - 连接体原子结构整体解析. 这种工具快速生成多样化的分子结构,改善酶设计和预测蛋白质-小分子相互作用.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 人工智能在药物发现中的作用
背景情况:
- 在蛋白质-小分子相互作用中建模构型异质性至关重要,但具有挑战性.
- 原子级描述在速度和通用性方面为在折叠状态下探测相互作用提供了潜在的优势.
研究的目的:
- 开发一种用于建模蛋白质-小分子相互作用的新型计算工具.
- 为小分子和蛋白质 - 连接体复合体生成精确的原子级构造组合.
- 通过评估活性位点预组织和精度来增强酶设计.
主要方法:
- 开发了一个图形神经网络模型PLACER (蛋白质连接体原子形态组合解析器).
- 在剑桥结构数据库和蛋白质数据库中的受损结构中的原子位置上训练PLACER.
- 利用PLACER生成小分子结构,蛋白质侧链和蛋白质-小分子对接组合.
主要成果:
- 根据构成和结合,PLACER精确地产生了各种各样的有机小分子结构.
- 该模型有效地构建小分子和蛋白质侧链结构以进行对接.
- 使用PLACER进行活性位点评估的酶设计导致了更高的成功率和活动,包括具有11,000M-1的KM-1的逆相酶.
结论:
- PLACER提供了一种快速和随机的方法来生成构造组合.
- 该工具对小分子和蛋白质连接体系统都有效.
- 在改善酶设计和预测分子相互作用方面,PLACER显示出显著的前景.
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