CIBR: PTMexchange: Globally harmonized re-analysis and sharing of data on post-translational modifications

蛋白质是细胞中的关键功能分子，执行多种生物学任务。这包括催化反应，为细胞成分提供结构，在不同细胞之间发出信号，以及调节其他基因的产生。蛋白质由最初形成长序列的单个氨基酸链组成，该长序列形成严格控制的3D结构，赋予每个蛋白质高度特异性的功能。基因组测序的出现将我们研究这些分子的能力转变为“大数据”学科，再加上质谱和相关计算技术的进步。“组学”的这一特殊分支被称为蛋白质组学--对给定生物样品中可检测到的所有蛋白质进行高通量研究（鉴定和定量）。蛋白质组学用于生物和生物医学研究，用于分析人类，包括植物在内的模式生物和传染病/微生物等多种系统。许多生物学功能依赖于蛋白质可以经历的化学修饰，称为翻译后修饰（PTM）。由于PTM的出现，一个特定的基因可以产生大量不同的蛋白质实体，这些蛋白质实体可能具有不同的生物学功能。PTM可以提供一种快速改变功能的机制，例如“打开”和“关闭”酶（生物催化剂）。由于其功能的重要性，蛋白质上的PTM位点经常是药物设计的目标，特别是针对癌症。在这项资助中，高质量的数据分析管道将用于研究公共领域中数百个蛋白质组学数据集的主要类型PTM的发生，涉及人类和主要模式生物（例如小鼠，大鼠和模式植物拟南芥）。蛋白质翻译后修饰的类型和位点丰富多样，为细胞提供了在不同条件下快速适应功能的机制。PTM在基础和应用生命科学研究的所有领域都得到了广泛的研究。使用质谱（MS）的蛋白质组学方法提供了检测和定位蛋白质PTM的唯一高通量手段。尽管它们在生物学上很重要，但PTM相关数据是通过不同的资源在公共领域进行整理的，缺乏数据来源。改善这种情况的一个有效方法是通过UniProtKB（http：//www.uniprot.org/），世界领先的蛋白质知识库，提供来自蛋白质组学方法的PTM信息。在公共领域有数百个相关的PTM蛋白质组学数据集，因为蛋白质组学社区现在广泛接受开放数据政策（例如通过资源PRIDE和PeptideAtlas，ProteomeXchange联盟的一部分）。我们将在云中开发和部署开放和可复制的管道，以重新分析来自人类和主要模式生物的数百个PTM相关公共数据集。将使用补充分析方法：主要基于标准蛋白质数据库，但也基于光谱库和开放式修饰搜索。将特别注意确保PTM本地化的准确性，并将在制定社区指南时考虑到这一目标。这些数据将被广泛传播到UniProtKB和其他知识库（如neXtProt），并在PRIDE、PeptideAtlas和新资源PTMeXchange上提供。这些新的PTM数据将在研究中整合，以前所未有的规模和准确性提高统计功效。最后，将进行以下几个示范研究，以了解PTM图案，功能和演变。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Proteins are the key functional molecules in cells, performing multiple biological tasks. This includes catalyzing reactions, providing structure to cellular components, signaling between different cells and regulating the production of other genes among many others. Proteins are composed of chains of individual amino acids that are formed initially into a long sequence, which forms into a strictly controlled 3D structure, giving the highly specific function to each protein. The advent of genome sequencing has transformed our ability to study these molecules into a "Big Data" discipline, coupled to advances in mass spectrometry and allied computing techniques. This particular branch of "'omics" is referred to as proteomics - the high-throughput study (identification and quantification) of all the proteins that can be detected in a given biological sample. Proteomics is used right across biological and biomedical research for profiling systems as varied as human, model organisms including plants, and infectious diseases/microbes, among many others. Many biological functions are dependent on chemical modifications that proteins can undergo, called Post-translational Modifications (PTMs). Due to the occurrence of PTMs, one particular gene can produce a great number of different protein entities which can potentially have different biological functions. PTMs can provide a rapid mechanism for changing function, such as switching an enzyme (biological catalyst) "on" and "off". Due to their functional importance, sites of PTMs on proteins are frequently the targets for drug design, particularly against cancer. In this grant, high-quality data analysis pipelines will be used to study the occurrence of the main types of PTMs across hundreds of proteomics datasets in the public domain, involving human and the main model organisms (e.g. mouse, rat and the model plant Arabidopsis). The types and sites of post-translational modifications (PTMs) on proteins are rich and diverse, providing cells with a rapid mechanism for adapting function under different conditions. PTMs are widely studied across all areas of fundamental and applied life sciences research. Proteomics approaches using mass spectrometry (MS) provide the sole high-throughput means to detect and localize protein PTMs. Despite their biological importance, PTM-relevant data is collated in the public domain via disparate resources, with a lack of data provenance. An efficient way to improve the situation is to make PTM information derived from proteomics approaches available through UniProtKB (http://www.uniprot.org/), the world-leading protein-knowledgebase. There are hundreds of relevant PTM proteomics datasets in the public domain since the proteomics community is now widely embracing open data policies (e.g. through the resources PRIDE and PeptideAtlas, part of the ProteomeXchange consortium). We will develop and deploy in the cloud open and reproducible pipelines to re-analyse consistently hundreds of PTM relevant public datasets coming from human and the main model organisms. Complementary analysis approaches will be used: primarily standard protein database-based but also spectral library-based and open modification searches. Special attention will be devoted to ensuring that PTM localization is accurate and community guidelines will be developed with that goal in mind. These data will be widely disseminated to UniProtKB and other knowledge-bases (e.g. neXtProt) and made available at PRIDE, PeptideAtlas, and a new resource PTMeXchange. These new PTM data will be integrated across studies, to increase statistical power at an unprecedented scale and accuracy. Finally, several following demonstration studies will be performed to understand PTM motifs, function and evolution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.