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Updated: Sep 12, 2025

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XL-Ranker:一种计算工作流程,用于从交叉链接质谱数据中优先考虑蛋白质-蛋白质相互作用

Wenrong Chen, John M Elizarraras, Fenglong Jiao

bioRxiv : the preprint server for biology

|August 8, 2025

概括

XL-Ranker解决了交叉链接质谱 (XL-MS) 数据的模糊性,以准确识别蛋白质与蛋白质相互作用 (PPI). 这种计算框架提高了生物研究中PPI发现的可靠性.

科学领域:

蛋白质组学是指蛋白质组学.
计算生物学计算生物学
生物化学生物化学

背景情况:

蛋白与蛋白相互作用 (PPI) 对生物功能至关重要,并与疾病有关.
交联质谱法 (XL-MS) 是一种检测相互作用的方法,以推断PPI.
解释XL-MS数据是具有挑战性的,因为片映射模两可,导致不准确的PPI网络.

研究的目的:

开发一个计算框架,XL-Ranker,用于解决XL-MS数据中的片映射模糊性.
从XL-MS数据推断出高可信度蛋白质与蛋白质相互作用 (PPI).
使用XL-MS提高PPI发现的可靠性和准确性.

主要方法:

开发了XL-Ranker,这是一个计算框架,集成了一个集覆盖图算法和机器学习.
将XL-Ranker应用于来自HEK293细胞的XL-MS数据集.
系统地解决了基映射的模糊性,以推断PPI.

主要成果:

XL-Ranker确定了一个高可信度的PPI网络,包含880个相互作用,涉及964个基因.
通过XL-Ranker选择的相互作用在STRING数据库中显示出明显更高的得分,与被排除的相互作用相比.
网络分析证实,确定的相互作用形成了具有生物学意义的集群.

结论:

XL-Ranker提供了一个有效的计算解决方案,用于XL-MS数据中的片映射模糊性.
该框架提高了蛋白质-蛋白质相互作用发现的可靠性.
XL-Ranker提高了从XL-MS衍生的PPI网络的准确性,有助于下游生物解释.

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