NIBS董梦秋研究组：定量蛋白质组学数据解析软件pQuant

　　

2014年6月3日，中科院计算所pFind研究团队与北京生命科学研究所董梦秋实验室合作开发了定量蛋白质组学数据解析软件pQuant，用计算方法排除干扰信号的影响、提高肽段和蛋白质的定量准确度并对每个定量值进行准确性评价。

基于质谱的定量蛋白质组学是现代生物学技术的生长点之一，用于测量复杂生物体系中蛋白质及其翻译后修饰在不同条件下的丰度变化，是研究蛋白质功能和药物作用机制的重要工具。已有的定量软件往往不能有效排除干扰信号，定量值的计算方法有待完善，而且缺乏准确性评价，致使输出结果“鱼龙混杂”，引起的假阳和假阴两方面的困扰都比较严重。

为了更好地解决这些问题，pQuant开发者—计算所的刘超同学—研究了几百个可疑定量值的原始质谱图和色谱图数据，找原因、攒经验，充分挖掘肽段的质谱、色谱信号特点以及从肽段定量到蛋白质定量的方法，灵活应用各种组合和统计算法，建立了一整套非常细致的数据分析流程。

为了验证pQuant的性能，董梦秋实验室的宋春青同学通过轻重SILAC或14N／15N标记哺乳动物细胞或细菌，从10:1到1:10按不同比例混合得到14套标准样品，产生了14套测试数据集。测试结果表明，pQuant定量结果的准确性明显超过定量蛋白质组学领域的两个主流软件Census和MaxQuant，主要表现在：（1）pQuant输出的非数比值数目（即不能定量的部分）占总比值数目的0.01–0.5%，远低于Census的MaxQuant的对应比例2.5–10.7%和1.8–2.7%；（2）Census和MaxQuant输出了许多不准确结果，其定量值的标准差是pQuant的1.3–2倍；（3）pQuant给出了肽段和蛋白质定量比值的置信区间，而Census和MaxQuant没有准确性评价。

上述结果以“pQuant Improves Quantitation by Keeping out Interfering Signals and Evaluating the Accuracy of Calculated Ratios”为题，于2014年6月3日在美国化学会主办的《Analytical Chemistry》期刊上发表。

中科院计算所博士生刘超和北京生命科学研究所博士生宋春青为本文共同第一作者。计算所的袁作飞、付岩、迟浩、王乐珩、樊盛博、张昆、曾文锋也参与了此项工作。贺思敏教授全程指导了pQuant算法的设计和评测。中科院计算所孙瑞祥博士和北京生命科学研究所董梦秋博士为共同通讯作者。王晓东实验室孙丽明博士和朱冰实验室徐墨博士提供了SILAC标记细胞，此外还有pFind团队邬龙、何昆和陈海丰，以及董梦秋实验室李铁梅和其他成员给予了帮助。该研究工作得到了科技部、基金委、中科院和北京市政府的资助。

原文摘要：

pQuant Improves Quantitation by Keeping out Interfering Signals and Evaluating the Accuracy of Calculated Ratios.Anal. Chem.

In relative protein abundance determination from peptide intensities recorded in full mass scans, a major complication that affects quantitation accuracy is signal interference from coeluting ions of similar m/z values. Here, we present pQuant, a quantitation software tool that solves this problem. pQuant detects interference signals, identifies for each peptide a pair of least interfered isotopic chromatograms: one for the light and one for the heavy isotope-labeled peptide. On the basis of these isotopic pairs, pQuant calculates the relative heavy/light peptide ratios along with their 99.75% confidence intervals (CIs). From the peptides ratios and their CIs, pQuant estimates the protein ratios and associated CIs by kernel density estimation. We tested pQuant, Census and MaxQuant on data sets obtained from mixtures (at varying mixing ratios from 10:1 to 1:10) of light- and heavy-SILAC labeled HeLa cells or (14)N- and (15)N-labeled Escherichia coli cells. pQuant quantitated more peptides with better accuracy than Census and MaxQuant in all 14 data sets. On the SILAC data sets, the nonquantified "NaN" (not a number) ratios generated by Census, MaxQuant, and pQuant accounted for 2.5-10.7%, 1.8-2.7%, and 0.01-0.5% of all ratios, respectively. On the (14)N/(15)N data sets, which cannot be quantified by MaxQuant, Census and pQuant produced 0.9-10.0% and 0.3-2.9% NaN ratios, respectively. Excluding these NaN results, the standard deviations of the numerical ratios calculated by Census or MaxQuant are 30-100% larger than those by pQuant. These results show that pQuant outperforms Census and MaxQuant in SILAC and (15)N-based quantitation.