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

将液-质联用(LC-MS)用于蛋白质和多肽的分析，已成为应用于人类健康、农业、能源和环境研究的分析科学不可或缺的组成部分。这项技术的质谱学部分一直是仪器开发的主要焦点。同时，考虑到预测被分析分子的保留性质的能力，该系统的LC组分可以提供非常有价值的信息。总体而言，这将使LC-MS成为一把“双刃剑”，两种方法的结果都可以用来改进分析结果。十多年来，我的实验室在多肽保留时间预测领域一直处于领先地位。我们的序列特定保留计算器(SSRCalc)模型已成为该领域的基准工具，吸引了蛋白质组学/色谱界以及主要蛋白质组学设备和软件供应商的注意。在这项提案中，我们的目标是与领先的LC-MS蛋白质组系统提供商Thermo Fisher Science(TFS)联合使用我们的专业知识。**TFS开发的关键应用程序之一是通过串联质量标签(TMT)试剂的多肽标记来进行蛋白质定量，然后进行LC-MS分析。标记多肽的层析性质与非标记多肽显著不同，从而限制了当前保留预测算法的应用，包括SSRCalc。TFS引入的另一项改进是使用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。这项合作研究和开发基金的重点是将SSRCalc预测方法扩展到标记TMT的多肽和使用不同温度的分离。这将导致我们的新一代模型-通用序列特定保留计算器(USSRCalc)-因为它将涵盖更广泛的色谱条件，具有无与伦比的性能，并将导致临床蛋白质组学简化定量方案的开发。**

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.**