Menikarachchi Lochana C, Hamdalla Mai A, Hill Dennis W, Grant David F
Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, United States.
Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Road, Unit 2155 Storrs, CT 06269, United States.
Comput Struct Biotechnol J. 2013 Mar 1;5:e201302005. doi: 10.5936/csbj.201302005. eCollection 2013.
The identification of compounds in complex mixtures remains challenging despite recent advances in analytical techniques. At present, no single method can detect and quantify the vast array of compounds that might be of potential interest in metabolomics studies. High performance liquid chromatography/mass spectrometry (HPLC/MS) is often considered the analytical method of choice for analysis of biofluids. The positive identification of an unknown involves matching at least two orthogonal HPLC/MS measurements (exact mass, retention index, drift time etc.) against an authentic standard. However, due to the limited availability of authentic standards, an alternative approach involves matching known and measured features of the unknown compound with computationally predicted features for a set of candidate compounds downloaded from a chemical database. Computationally predicted features include retention index, ECOM50 (energy required to decompose 50% of a selected precursor ion in a collision induced dissociation cell), drift time, whether the unknown compound is biological or synthetic and a collision induced dissociation (CID) spectrum. Computational predictions are used to filter the initial "bin" of candidate compounds. The final output is a ranked list of candidates that best match the known and measured features. In this mini review, we discuss cheminformatics methods underlying this database search-filter identification approach.
尽管分析技术最近取得了进展，但复杂混合物中化合物的鉴定仍然具有挑战性。目前，没有一种单一的方法能够检测和量化代谢组学研究中可能感兴趣的大量化合物。高效液相色谱/质谱联用技术（HPLC/MS）通常被认为是分析生物流体的首选分析方法。对未知物的阳性鉴定需要将至少两个正交的HPLC/MS测量值（精确质量、保留指数、漂移时间等）与真实标准品进行匹配。然而，由于真实标准品的可用性有限，另一种方法是将未知化合物的已知和测量特征与从化学数据库下载的一组候选化合物的计算预测特征进行匹配。计算预测特征包括保留指数、ECOM50（在碰撞诱导解离池中分解50%选定前体离子所需的能量）、漂移时间、未知化合物是生物来源还是合成来源以及碰撞诱导解离（CID）谱。计算预测用于筛选候选化合物的初始“分类”。最终输出是与已知和测量特征最匹配的候选化合物的排名列表。在本综述中，我们讨论了这种数据库搜索-筛选鉴定方法背后的化学信息学方法。
