Abstract：
Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is a open-source web-based analysis tool that automates the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Empirical evaluation on a collection of 22 unique MS datasetsshows fast and accurate identification of protein-metal complex adducts in deconvoluted mass spectra.
摘要：
质谱（MS）是一种用于分子鉴定的分析技术，可用于研究蛋白质-金属络合物的相互作用。一旦收集到MS数据，质谱通常手动解释，以鉴定由于蛋白质和金属基物种之间的相互作用而形成的加合物。然而,随着分辨率的提高，数据集大小，和物种的复杂性，确定加合物所需的时间和手动分配的易错性质已成为MS分析中的限制因素。AdductHunter是一个开源的基于网络的分析工具，它使用约束整数优化来自动化峰识别过程，以找到蛋白质和片段的可行组合，和动态时间规整，以计算物种的理论同位素模式与其实验同位素峰分布之间的差异。对22个独特的MS数据集的经验评估显示，在去卷积质谱中快速准确地鉴定了蛋白质-金属络合物加合物。
