Enhancing quantitative proteomics through the accurate prediction of retention time of peptides labeled with tandem mass tags

结合液相色谱和质谱（LC-MS）分析蛋白质和多肽已成为分析科学应用于人类健康、农业、能源和环境研究中不可或缺的组成部分。该技术的质谱成分一直是仪器开发的主要焦点。同时，该系统的LC组分可以提供非常有价值的信息，因为它能够预测被分析分子的保留性质。总的来说，这将使LC-MS成为一把“双刃剑”，两种方法的结果都可以用来提高分析输出。十多年来，我的实验室在肽保留时间预测领域一直处于领先地位。我们的序列特异性保留计算器（SSRCalc）模型已经成为该领域的基准工具，吸引了蛋白质组学/色谱界以及蛋白质组学设备和软件主要供应商的关注。在该提案中，我们的目标是将我们的专业知识与LC-MS蛋白质组学系统的领先供应商赛默飞世尔科学公司（TFS）结合起来。TFS开发的关键应用之一是通过串联质量标签（TMT）试剂的肽标记进行蛋白质定量，然后进行LC-MS分析。标记肽的层析性质与未标记肽的层析性质明显不同，从而限制了当前保留预测算法的应用，包括SSRCalc。TFS的另一个改进是使用了超小的色谱颗粒和更高的分离温度来降低柱背压。这也改变了预测模型的准确性。该合作研究与发展基金的重点是将SSRCalc预测方法扩展到用TMT标记的肽和使用不同温度的分离。这将导致我们的新一代模型-通用序列特异性保留计算器(USSRCalc) -因为它将以无与伦比的性能覆盖更广泛的色谱条件，并将导致临床蛋白质组学简化定量方案的发展。

The combination of liquid chromatography and mass spectrometry (LC-MS) for the analysis of proteins and peptides has become an indispensible component of analytical science applied to human health, agriculture, energy, and environmental studies. The mass-spectrometry component of this technique has been the primary focus of instrument development. At the same time, the LC component of the system can provide very valuable information, given the ability to predict the retention properties of analyzed molecules. Overall this will make LC-MS a "double-edged sword", where the outcomes from both methods can be used to improve the analysis output. My laboratory has been the leader in the peptide retention time prediction field for more than a decade. Our Sequence-Specific Retention Calculator (SSRCalc) model has become a benchmark tool in the field, attracting attention both from the proteomic/chromatographic community and from major vendors of proteomics equipment and software. In this proposal we aim to join our expertise with the leading provider of LC-MS proteomic systems, Thermo Fisher Scientific (TFS).One of the key applications developed by TFS targets protein quantitation through peptide labeling with tandem-mass tag (TMT) reagents, followed by LC-MS analysis. Chromatographic properties of the labeled peptides are significantly different from their non-labeled counterparts, thus limiting application of current retention prediction algorithms, including SSRCalc. Another improvement introduced by TFS is the use of ultra small chromatographic particles and higher separation temperature to reduce column backpressure. This alters the accuracy prediction models, too. The focus of this Collaborative Research and Development Grant is to extend the SSRCalc prediction methodology into peptides labeled with TMT and separations using various temperatures. This will result in the new generation of our model - Universal Sequence-Specific Retention Calculator (USSRCalc) - as it will cover wider range of chromatographic conditions with unmatched performance, and will lead to development of simplified quantitation protocols for clinical proteomics.