Database on demand - creating customized sequence databases for efficient protein identification

蛋白质组学领域试图鉴定和表征细胞或组织的蛋白质补体。实现这些目标的最流行的分析技术是质谱分析。从这些仪器中获得的质谱通常通过与序列数据库中基于蛋白质序列的预测谱进行比较来确定。已经开发出复杂的计算机算法，例如MASCOT搜索引擎（http://www.matrixscience.com），以自动执行这一特定任务，以适应这种方法产生的大量数据。有趣的是，只有一小部分获得的光谱可以分配到已知的蛋白质。由于所有蛋白质在细胞内（或细胞外）的生命周期中都可能经历某些变化，因此构建搜索算法时会考虑到某些变化。基于所谓的翻译后修饰（如磷酸化）的质量差异通常被这些搜索引擎选择性地考虑在内。不幸的是，蛋白质水解裂解，另一种生物相关的蛋白质加工形式，没有被考虑在内。在循环系统（如血浆或血清）中发现的许多蛋白质显示出蛋白水解降解的迹象，这一事实说明了裂解事件的生物学相关性。这些蛋白质或其片段所携带的切割模式被假设为在整个身体中作为异常过程的生物标志物具有重要意义。因此，可靠和快速识别这种降解产物的能力可以在早期发现疾病方面发挥重要作用。另一个经常被搜索引擎忽视的问题是，从用于消化样品的猪胰蛋白酶到研究中细胞系的分枝杆菌或病毒感染，在样品中发现的常见污染物。最后，序列变异（通过剪接变异或单氨基酸多态性）的发生会进一步混淆鉴定过程。因此，研究这些小层序变化的频率和重要性并不简单。从上面可以清楚地看出，由于这个原因而无法识别的光谱具有很大的生物学意义。同样清楚的是，可靠地识别这些光谱的工具是可用的，因为它们可以将光谱与正确的序列相匹配。为了给这些算法提供一组增强的序列来匹配所获得的质谱，对原始序列数据库进行简单的预处理就足够了。在这个项目中，我们建议开发一个工具，允许用户获得这样一个定制的、丰富的序列数据库。用户将能够在用户友好的web表单上指定（组合）应该应用于序列数据库的预处理步骤。该软件随后将负责生成相应的数据库，并将其格式化为可以在诸如MASCOT之类的搜索引擎中轻松使用的方式。用户只需要在过程完成的通知下通过一个网络链接下载生成的数据库，并将此数据库上传到MASCOT（或任何其他搜索引擎）。该软件将是用户和序列数据库之间的高度模块化层，将使预处理步骤适合当前的蛋白质组学分析，并将很容易扩展以满足未来社区的需求。这个简单的步骤丰富了序列数据库，与质谱相匹配，将提高当前研究项目的识别效率（以及使以前的努力重新分析），并有可能解锁新的和非常有趣的生物学发现。因此，作为提高蛋白质组学实验的性价比的一种手段，该工具具有很大的前景，同时也扩大了该领域的范围。

The field of proteomics attempts to identify and characterize the protein complement of cells or tissues. The most popular analytical technique to achieve these goals is mass spectrometry. The mass spectra that are obtained from these instruments are usually identified by comparing them with predicted spectra based on protein sequences from sequence databases. Sophisticated computer algorithms such as the MASCOT search engine (http://www.matrixscience.com), have been developed to automate this particular task in order to accommodate the large amounts of data generated by this approach. Interestingly, only a minor fraction of the acquired spectra can be assigned to known proteins. Since all proteins can potentially go through certain changes during their lifetime in (or outside) the cell, the search algorithms are built to take certain changes into account. Mass differences based on the addition of so-called posttranslational modifications (e.g.: phosphorylation) are usually optionally taken into account by these search engines. Unfortunately, proteolytic cleavage, another biologically relevant form of protein processing, is not taken into consideration. The biological relevance of cleavage events is exemplified by the fact that many proteins found in the circulatory system (e.g.: in plasma or serum) show signs of proteolytic degradation. The cleavage patterns that these proteins or their fragments carry are hypothesized to have great significance as biomarkers for abnormal processes in the body at large. The ability to reliably and quickly identify such degradation products can thus serve an important role in the early detection of disease. Another point that is often overlooked by search engines concerns common contaminants found in samples / from the pig trypsin that is used to digest the samples to mycobacterial or viral infection of the cell lines under study. Finally, the occurence of sequence variations (through splice variants or single aminoacid polymorphisms) can further confound the identification process. Research of the frequency and importance of these minor sequence variations is therefore not straightforward. It is clear from the above that the spectra that elude identification for this reason are of great biological interest. It is also clear that the tools for reliably identifying such spectra are available, given that they can match the spectrum against the correct sequence. In order to furnish these algorithms with an enhanced set of sequences against which to match the acquired mass spectra, simple pre-processing steps of the original sequence database suffice. In this project, we propose to develop a tool that will allow the user to obtain such a customized, enriched sequence database. The user will be able to specify (a combination of) pre-processing steps that should be applied to the sequence database on a user-friendly web form. The software will subsequently take care of generating the corresponding database and format it in such a way that it can readily be used in search engines such as MASCOT. The user will simply need to download the generated database by following a web link upon notification of the completion of the process and upload this database into MASCOT (or any other search engine). The software will be a highly modular layer between the user and the sequence database that will enable preprocessing steps suited for current-day proteomics analyses, and will be easily extensible for future requirements from the community. This simple step of enriching the sequence database against which mass spectra are matched, will enhance the identification efficiency of current research projects (as well as enabling the re-analysis of previous efforts) and has the potential to unlock novel and highly interesting biological findings. As such, the tool holds great promise as a means to raise the value-for-money of proteomics experiments, while at the same time expanding the reach of the field.