Data-Driven Approach To Determine Popular Proteins for Targeted Proteomics Translation of Six Organ Systems

Amidst
the proteomes of human tissues lie subsets of proteins that
are closely involved in conserved pathophysiological processes. Much
of biomedical research concerns interrogating disease signature proteins
and defining their roles in disease mechanisms. With advances in proteomics
technologies, it is now feasible to develop targeted proteomics assays
that can accurately quantify protein abundance as well as their post-translational
modifications; however, with rapidly accumulating number of studies
implicating proteins in diseases, current resources are insufficient
to target every protein without judiciously prioritizing the proteins
with high significance and impact for assay development. We describe
here a data science method to prioritize and expedite assay development
on high-impact proteins across research fields by leveraging the biomedical
literature record to rank and normalize proteins that are popularly
and preferentially published by biomedical researchers. We demonstrate
this method by finding priority proteins across six major physiological
systems (cardiovascular, cerebral, hepatic, renal, pulmonary, and
intestinal). The described method is data-driven and builds upon the
collective knowledge of previous publications referenced on PubMed
to lend objectivity to target selection. The method and resulting
popular protein lists may also be useful for exploring biological
processes associated with various physiological systems and research
topics, in addition to benefiting ongoing efforts to facilitate the
broad translation of proteomics technologies.

在人类组织的蛋白质组中，存在着与保守性病理生理过程密切相关的一组蛋白质。生物医学研究的大量工作集中于探究疾病标志蛋白及其在疾病机制中的作用。随着蛋白质组学技术的进步，如今已可行开发针对蛋白质丰度和其翻译后修饰的精准定量目标蛋白质组学检测方法；然而，随着越来越多的研究涉及蛋白质与疾病的关系，现有的资源在未经审慎优先考虑具有高度重要性和影响力的蛋白质进行检测方法开发的情况下，已不足以针对每一个蛋白质。在此，我们描述了一种数据科学方法，通过利用生物医学文献记录对被生物医学研究人员广泛和优先发表的蛋白质进行排序和标准化，以优先级和加速跨研究领域的具有重大影响力的蛋白质的检测方法开发。我们通过在六大主要生理系统（心血管、脑部、肝脏、肾脏、肺和肠道）中寻找优先蛋白质来展示这一方法。所述方法基于数据驱动，并构建于PubMed上引用的前期出版物所积累的集体知识之上，从而为靶点选择提供了客观性。该方法及其产生的流行蛋白质列表，对于探索与各种生理系统和研究主题相关的生物学过程，以及有助于促进蛋白质组学技术的广泛应用，也可能具有实际意义。