Computational approaches to protein identification and quantification using MS/MS

(未修改) 液相色谱(LC)与串联质谱仪(MS/MS)联用是一种广泛使用的分析平台 生物样品中蛋白质的高通量鉴定和定量。除了试验性的 正在筹备中的步骤、计算和统计程序在确定内容方面发挥着重要作用 混合物的一部分。然而，即使拥有最好的分析平台和现代软件，也只有一小部分 光谱通常被识别，因此直接影响生物样品分析的质量。如果高的话- 吞吐量蛋白质组学技术将在人群生物医学应用中成为常规应用 在规模方面，关键是要解决导致识别不充分的分析和计算因素 覆盖面和敏感性。 在过去的几年里，我们和其他人花费了大量的研究活动来理解 并为分析平台建模，并随后改进复杂结构分析的计算方法 生物混合物。虽然我们最初的拨款申请已经导致了方法和计划 为社会所接受，我们有需要作出更大的贡献，也有很大的空间。我们看到了许多 这些贡献与分析细胞和组织的动态变化有关，并涉及 蛋白质数量、蛋白质翻译后修饰(PTM)和瞬时蛋白质-蛋白质的变化 互动。基于质谱学的蛋白质组学提供了一个很好的平台来解决这些问题 挑战。因此，我们计划继续开发新的无标记量化方法，并保持密切联系 除了我们的核心优势外，还将PTMS和蛋白质相互作用作为新的研究方向 续签申请。 此应用程序包括计算(Radivojac博士、唐博士)之间相当密切的协作 和实验科学家(Arnold博士、Clemmer博士、Reilly博士)比我们最初的应用程序做得更好。这个 研究人员带来了与蛋白质相关的一系列学科的互补专业知识和经验 生物信息学、算法、机器学习以及分析化学和仪器仪表。总体而言，我们 相信这项建议将导致基于质谱学的蛋白质组学的重大进展。

(Not modified) Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) is a widely used platform for high-throughput identification and quantification of proteins in biological samples. In addition to experimental steps in the pipeline, computational and statistical procedures play important roles in determining the content of the mixture. However, even with the best analytical platforms and modern software, only a small fraction of spectra are typically identified, thus directly impacting the quality of the biological sample analysis. If high- throughput proteomics techniques are to become routinely used in biomedical applications on the population scale, it is critical to address analytical and computational factors that contribute to the inadequate identification coverage and sensitivity. Over the past several years, we and others have spent a significant amount of research activity to understand and model analytical platforms and subsequently improve computational methods for the analyses of complex biological mixtures. While our original grant application has resulted in methods and programs already accepted by the community, there is a need and significant room for further key contributions. We see many of these contributions being related to the analyses of dynamic changes in cells and tissues, and involving changes in protein quantities, protein post-translational modifications (PTMs) and transient protein-protein interactions. Mass spectrometry-based proteomics provides an excellent platform to address each of these challenges. Thus, we plan to continue to develop novel methods for label-free quantification and remain close to our core strengths, but also strongly focus on PTMs and protein-protein interactions as new directions of this renewal application. This application includes a considerably closer collaboration between computational (Dr. Radivojac, Dr. Tang) and experimental (Dr. Arnold, Dr. Clemmer, Dr. Reilly) scientists than did our original application. The investigators bring complementary expertise and experience in a range of disciplines involving protein bioinformatics, algorithms, machine learning, as well as analytical chemistry and instrumentation. Overall, we believe that this proposal will result in significant advances for mass spectrometry-based proteomics.