G-PLIP:知识图神经网络用于无结构蛋白质-连接体生物活性预测
Simon J Crouzet1,2,3, Anja Maria Lieberherr1,4, Kenneth Atz1,5
1Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland.
Computational and structural biotechnology journal
|August 7, 2024
概括
这项研究引入了一种新的,无结构的图形神经网络 (GNN),用于高效地预测蛋白质 - 连接体相互作用 (PLIs). 该模型利用各种生物和化学数据,与结构意识方法相比,实现竞争性或优异的性能.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 蛋白质 - 配体相互作用 (PLI) 对药物的有效性和安全性至关重要.
- 当前的PLI预测方法通常需要结构数据或广泛的计算.
- 需要低成本,无结构的PLI预测模型.
研究的目的:
- 开发和验证一个轻量级图形神经网络 (GNN),用于无结构预测定量蛋白-连接体相互作用 (PLI).
- 为了证明GNN可以预测PLI强度而不需要复杂的直接结构信息.
- 探索各种生物和化学数据的整合,以提高PLI预测.
主要方法:
- 一个轻量级图形神经网络 (GNN) 使用有限数据集的定量PLI进行训练.
- 构建了一个异质图,编码蛋白质序列,基因表达,蛋白质-蛋白质相互作用和连接体结构相似性.
- 在没有直接结构输入的情况下,GNN模型处理了这个图表来预测PLI强度.
主要成果:
- 与现有的结构意识方法相比,无结构GNN模型实现了竞争性或优异的性能.
- 该模型成功地预测了未见的蛋白质-连接体相互作用的强度.
- 在图表中编码全面的生物和化学信息增强了预测能力.
结论:
- 使用图形神经网络,以较低的计算成本实现无结构PLI预测.
- 将不同的数据源集成到异质图中可以提高PLI预测的准确性.
- 嵌入生物和化学知识的表示学习技术可以推进现有的PLI预测方法.
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