MS-Ion: Unveiling Ion Associations in PTM-Enriched Proteomic Data

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Title:	MS-Ion: Unveiling Ion Associations in PTM-Enriched Proteomic Data
Authors:	許珍珠;Fai, Michelle
Contributors:	系統生物與生物資訊研究所
Keywords:	關聯模式;診斷離子;FP-Growth;翻譯後修飾;軟體工具;串聯質譜;association pattern;diagnostic ion;FP-Growth;post-translational modifications;software tool;tandem mass spectra
Date:	2025-07-11
Issue Date:	2025-10-17 11:46:52 (UTC+8)
Publisher:	國立中央大學
Abstract:	診斷離子是指修飾側鏈的獨特碎片或中性損失，其特徵可用來區分修飾肽段與其未修飾的對應物及其他蛋白質修飾（PTMs）。然而，現有的診斷離子挖掘工具多侷限於偵測單一離子，往往忽略在更廣泛斷裂模式中可能存在的診斷離子關聯性。為了解決此問題，我們開發了 MS-Ion，一款應用 FP-Growth 演算法的軟體工具。MS-Ion 可接受來自 MaxQuant 及 MSFragger/FragPipe 等傳統蛋白質資料庫搜尋工具所產生的 PSM（肽段與光譜匹配）以及對應的 mzML 檔作為輸入，並根據所指派的修飾類型對 PSM 進行分類。透過建立 FP-tree 並追蹤共現離子路徑，MS-Ion 能揭示與修飾類型相關的獨特離子關聯。本研究使用了四組資料集：一組乙醯化富集的 LUAD 資料集（PDC000224）、一組磷酸化富集的 LUAD 資料集（PDC000149）、一組賴胺酸乙醯化的合成肽段資料集（PXD009449），以及一組酪胺酸磷酸化的合成肽段資料集（PXD009449）。針對乙醯化，MS-Ion 共辨識出六種關鍵離子模式，其中最具代表性的是乙醯化賴胺酸的診斷離子（m/z 126.0913 與 143.1179），其信心值在 LUAD 與合成資料集中皆高於 95%。此外也偵測到與之共現的乙醯化 y 離子（LUAD 資料集中的 m/z 189.1234 及合成資料集中的 m/z 317.2183）。在 LUAD 資料集中結合 m/z 126.0913、143.1179 和 189.1234 三個診斷離子，或在合成資料集中結合 m/z 126.0913、143.1179 和 317.2183，可達到 100% 的特異性，有效區分真正的修飾 PSM 與誤分類的未修飾 PSM。針對磷酸化，MS-Ion 驗證 m/z 216.0426 為酪胺酸磷酸化診斷離子，與過去研究結果相符。該離子與八個磷酸化的 b/y 離子在 LUAD 與合成資料集中共同出現。另有一個特徵離子（m/z 439.1701）頻繁出現在絲胺酸磷酸化的 PSM 中；而對於蘇胺酸的磷酸化則發現新的組合模式（m/z 122.0288 與 195.0815），在複合磷酸化位點中也觀察到 m/z 216.0426 與 195.0815 的共現。所有與磷酸化相關的離子模式皆展現出極高的特異性（90–100%）。統計分析（包含卡方獨立性檢定，p < .01）及 UMAP 降維方法皆進一步驗證所偵測離子模式的顯著性與區辨性。MS-Ion 具備互動式介面，使用者可自訂支持度與信心門檻，是一款可協助研究人員進行 PTM 探索的高效能工具。;Diagnostic ions are characterized by unique fragments of modified side chains or neutral losses, distinguishing modified peptides from their unmodified counterparts and other PTMs. However, current diagnostic ion mining tools are limited to detecting single ions, often ignoring potential associations between diagnostic ions across broader fragmentation patterns. To address the issue, we developed MS-Ion, a software tool that applies the FP-Growth algorithm. MS-Ion takes PSMs from conventional protein database search tools, such as MaxQuant and MSFragger/FragPipe, along with the corresponding mzML files as input. It first separates PSMs based on their assigned modifications. By constructing an FP-tree and tracing co-occurring ion paths, MS-Ion uncovers unique associations linked to modifications. Four datasets were used, including an acetylation-enriched LUAD dataset (PDC000224), a phosphorylation-enriched LUAD dataset (PDC000149), a lysine-acetylated synthetic peptide dataset (PXD009449), and a tyrosine-phosphorylated synthetic peptide dataset (PXD009449). For acetylation, MS-Ion identified six key ion patterns. The most significant involved acetylated lysine diagnostic ions (m/z 126.0913 and 143.1179), with confidence values above 95% across the LUAD and synthetic datasets. Co-occurring acetylated y ions (m/z 189.1234 in the LUAD dataset and 317.2183 in the synthetic dataset) were also detected. By combining the three diagnostic ions (m/z 126.0913, 143.1179, and 189.1234 in LUAD datasets; m/z 126.0913, 143.1179, and 317.2183 in synthetic datasets), MS-Ion achieved 100% specificity, effectively distinguishing true positives from misclassified unmodified PSMs. For phosphorylation, MS-Ion confirmed that m/z 216.0426 represented tyrosine phosphorylation, corresponding with previous studies. This ion co-occurred with eight phosphorylated b/y ions across LUAD and synthetic peptide datasets. Additionally, a unique ion (m/z 439.1701) was frequently observed in serine phosphorylation PSMs, while novel phosphorylation patterns for threonine (m/z 122.0288 with 195.0815) and combined phosphorylation sites (m/z 216.0426 with 195.0815) were discovered. All phosphorylation-related patterns showed exceptional specificity (90-100%). Statistical analyses, including Chi-square independence tests (p < .01) and the UMAP algorithm, confirmed the significance and distinctiveness of the detected ion patterns. With an interactive interface allowing customizable support and confidence thresholds, MS-Ion provides researchers with a high-performance tool for PTM discovery.
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