Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a “MS2-centric” approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics [“fraction of mis-identifications” (FMI) and “fraction of interprotein cross-links from known interactions” (FKI)]. We then address this problem, by designing a novel “MS3-centric” approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.

Thorough quality assessment of novel interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) studies is critical. Almost all current XL-MS studies have validated cross-links against known 3D structures of representative protein complexes. Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets, and propose a comprehensive set of four data-quality metrics to address this issue.

Background and Purpose Ectonucleotide pyrophosphatase/PDE1 (NPP1) is an ectoenzyme, which plays a role in several disorders including calcific aortic valve disease (CAVD). So far, compounds that have been developed as inhibitors of NPP1 lack potency and specificity. Quinazoline‐4‐piperidine sulfamides (QPS) have been described as potent inhibitors of NPP1. However, their mode of inhibition as well as their selectivity and capacity to modify biological processes have not been investigated. Experimental Approach In the present series of experiments, we have evaluated the efficacy of two derivatives, QPS1‐2, in inhibiting human NPP1, and we have evaluated the effect of the most potent derivative (QPS1) on other ectonucleotidases as well as on the ability of this compound to prevent phosphate‐induced mineralization of human primary aortic valve interstitial cells (VICs). Key Results The QPS1 derivative is a potent (Ki 59.3 ± 5.4 nM) and selective non‐competitive inhibitor of human NPP1. Moreover, QPS1 also significantly inhibited the K121Q NPP1 gene variant (Ki 59.2 ± 14.5 nM), which is prevalent in the general population. QPS1 did not significantly alter the activity of other nucleotide metabolizing ectoenzymes expressed at the cell surface, namely NPP3, NTPDases (1–3), ecto‐5′‐nucleotidase and ALP. Importantly, QPS1 in the low micromolar range (≤10 μM) prevented phosphate‐induced mineralization of VICs and lowered the rise of osteogenic genes as expected for NPP1 inhibition. Conclusions and Implications We have provided evidence that QPS1 is a potent and selective non‐competitive inhibitor of NPP1 and that it prevented pathological mineralization in a cellular model.

No abstract

Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry has the unique capability to detect proteinprotein interactions at a large scale along with spatial constraints between interaction partners.However, the current cross-link search algorithms follow an "MS2-centric" approach and, as a result, suffer from a high rate of mis-identified cross-links (~15%). We address this urgent problem, by designing a novel "MS3-centric" approach for cross-link identification and implemented it as a search engine called MaXLinker. MaXLinker significantly outperforms the current state of the art search engine with up to 18-fold lower false positive rate. Additionally, MaXLinker results in up to 31% more cross-links, demonstrating its superior sensitivity and specificity. Moreover, we performed proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we unveiled the most comprehensive set of 9,319 unique cross-links at 1% false discovery rate, comprising 8,051 intraprotein and 1,268 interprotein cross-links.Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.