Birklbauer Micha J, Müller Fränze, Geetha Sowmya Sivakumar, Matzinger Manuel, Mechtler Karl, Dorfer Viktoria
Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, Hagenberg, 4232, Austria.
Institute for Symbolic Artificial Intelligence, Johannes Kepler University Linz, Altenberger Straße 69, Linz, 4040, Austria.
Commun Chem. 2024 Dec 19;7(1):300. doi: 10.1038/s42004-024-01386-x.
The field of crosslinking mass spectrometry has seen substantial advancements over the past decades, enabling the structural analysis of proteins and protein complexes and serving as a powerful tool in protein-protein interaction studies. However, data analysis of large non-cleavable crosslink studies is still a mostly unsolved problem due to its n-squared complexity. We here introduce an algorithm for the identification of non-cleavable crosslinks implemented in our crosslinking search engine MS Annika that is based on sparse matrix multiplication and allows for proteome-wide searches on commodity hardware. We compare our algorithm to other state-of-the-art crosslinking search engines commonly used in the field and conclude that MS Annika unifies high sensitivity, accurate FDR estimation and computational performance, outperforming competing tools. Application of this algorithm enabled us to employ a proteome-wide search of C. elegans nuclei samples, where we were able to uncover previously unknown protein interactions and conclude a comprehensive structural analysis that provides a detailed view of the Box C/D complex. Moreover, our algorithm will enable researchers to conduct similar studies that were previously unfeasible.
在过去几十年中，交联质谱领域取得了重大进展，能够对蛋白质和蛋白质复合物进行结构分析，并成为蛋白质 - 蛋白质相互作用研究中的有力工具。然而，由于其n平方的复杂性，大型不可裂解交联研究的数据分析仍然是一个基本未解决的问题。我们在此介绍一种在我们的交联搜索引擎MS Annika中实现的用于鉴定不可裂解交联的算法，该算法基于稀疏矩阵乘法，允许在商用硬件上进行全蛋白质组搜索。我们将我们的算法与该领域常用的其他最先进的交联搜索引擎进行比较，得出结论：MS Annika兼具高灵敏度、准确的错误发现率（FDR）估计和计算性能，优于竞争工具。应用该算法使我们能够对秀丽隐杆线虫细胞核样本进行全蛋白质组搜索，在此过程中我们能够发现以前未知的蛋白质相互作用，并完成一项全面的结构分析，该分析提供了Box C/D复合物的详细视图。此外，我们的算法将使研究人员能够进行以前不可行的类似研究。
