通过深度学习识别14-3-3互动组结合点
Laura van Weesep1, Rıza Özçelik1,2,3, Marloes Pennings1,4
1Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology Eindhoven The Netherlands l.brunsveld@tue.nl f.grisoni@tue.nl.
概括
我们开发了一个深度学习模型, 预测14-3-3蛋白质的蛋白质结合点, 该模型达到75%的准确性,并确定了5个经过实验验证的结合点.
科学领域:
- 生物化学
- 计算生物学
- 结构生物学
背景情况:
- 蛋白与蛋白的相互作用是生物过程和疾病的基础.
- 鉴定蛋白质相互作用点,特别是对内在无序的蛋白质来说,仍然是一个重大挑战.
- 14-3-3蛋白质作为细胞信号网络中的中心枢纽,使得它们的相互作用至关重要.
研究的目的:
- 开发一个深度学习框架来预测14-3-3蛋白质的蛋白质结合点.
- 在医学上相关的蛋白质中确定新的14-3-3结合点,包括内在的乱蛋白质.
- 为预测14-3-3绑定网站提供一个免费的网络资源.
主要方法:
- 对各种深度学习方法进行系统测试.
- 开发一个集体深度学习模型用于序列绑定预测.
- 该模型可用于300个医学相关的蛋白质序列.
- 使用湿实验室技术对顶部预测序列进行实验验证.
- 使用X射线结晶学和分子动力学模拟的结构分析.
主要成果:
- 一个集体深度学习模型在预测14-3-3结合的外部序列上实现了75%的平衡精度.
- 实验验证证了8个预测的序列中的5个的结合,分离常数 (Kd) 范围为1. 6±0. 1μM至70±5μM.
- 在医学上相关的蛋白质中发现了新的结合点,包括那些与阿尔茨海默氏症等疾病有关的蛋白质.
- 这项研究表明深度学习能够预测涉及内在无序蛋白质的相互作用.
结论:
- 开发的深度学习框架有效地预测了14-3-3蛋白质的蛋白质结合点.
- 发现的新结合点为研究蛋白质功能和治疗向提供了新的途径.
- 这项研究强调了深度学习在解读复杂的蛋白质相互作用方面的力量.
- 一个公开的网络工具被创建,以促进对14-3-3相互作用的进一步研究.
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