KSFinder-一种知识图模型,用于对酶的新酸化基质的链接预测
Manju Anandakrishnan1, Karen E Ross2, Chuming Chen1
1Center for Bioinformatics and Computational Biology, University of Delware, Newark, DE, United States of America.
PeerJ
|October 11, 2023
概括
KSFinder使用一种新的基于网络的方法预测酶-基质链接,改善未研究的酶的覆盖范围,并识别潜在的与疾病相关的功能. 该工具增强了对人类疾病中酶调节的理解.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 异常的蛋白激酶调节与各种人类疾病有关.
- 许多人体激酶仍未得到充分研究,限制了治疗的发展.
- 现有的预测工具缺乏全面的覆盖范围,并且不考虑蛋白质网络关系.
研究的目的:
- 开发KSFinder,用于预测酶-基质链接的计算工具.
- 在一个全面的人类激酶网络中捕获异质蛋白质关联.
- 为了确定未被研究的激酶的基质,并假定它们的功能作用.
主要方法:
- 利用知识图嵌入来学习光蛋白质组网络中的语义关系.
- 在嵌入式矢量上训练了一种多层感知子分类器,以预测酶-基质相互作用.
- 实施了战略负面生成方法,并对多个数据集验证了性能.
主要成果:
- 与现有模型相比,KSFinder表现出优越的性能和泛化能力.
- 确定了17种新激酶基质预测的文献证据,包括涉及未经研究的激酶的证据.
- 预测432个激酶的基质,其中68个被确定为研究不足.
结论:
- KSFinder提供了改进的酶-基质预测,覆盖范围更高,特别是对未经研究的酶.
- 该工具有助于假设研究不足的激酶的功能,例如HIPK3和CAMKK1.
- 这种方法促进了对激酶功能的理解,以及在人类疾病中潜在的治疗点.
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