预测突变对蛋白质溶解度的影响,使用图形卷积网络和蛋白质语言模型表示
Jing Wang1,2, Sheng Chen2, Qianmu Yuan2
1Guangzhou institute of technology, Xidian University, Guangzhou, China.
Journal of computational chemistry
|November 7, 2023
概括
DeepMutSol使用图形卷积神经网络预测突变导致的蛋白质溶解度变化. 这种方法改善了疾病突变预测,并优于现有的计算方法.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质溶解度至关重要,并受到突变的影响,可能导致疾病.
- 实验性溶解度的确定是昂贵和耗时的.
- 当前的in-silico方法依赖于进化数据,并且经常忽视3D结构,限制性能.
研究的目的:
- 开发一种高效且准确的in-silico方法,用于预测因突变而导致的蛋白质溶解性变化.
- 利用预测的蛋白质结构和先进的机器学习来提高预测的准确性.
主要方法:
- 提出了DeepMutSol,一种使用图形卷积网络 (GCN) 的基于序列的方法.
- 从AlphaFold2.2.使用预测结构启动了蛋白质图表.
- 采用蛋白质语言嵌入用于残留物表示.
- 预训练了绝对蛋白质溶解性的模型,以解决有限的突变数据.
主要成果:
- 在基准测试中,DeepMutSol与最先进的方法相比,表现优越.
- 该方法成功地从ClinVar数据库中区分了临床相关基因中的致病突变.
- 预测的溶解性变化与突变致病性相关.
结论:
- DeepMutSol提供了一种强大而准确的工具,用于预测突变诱导的蛋白质溶解性变化.
- 该方法整合了预测结构和深度学习,用于增强生物信息学预测.
- 这种方法在疾病基因变异解释和蛋白质设计方面具有潜在的应用.
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