FPGA supercomputing technology for high-throughput identification and quantitation in proteomics

蛋白质组学是研究特定状态下细胞的整个补体。正是蛋白质在基因组中“表现”信息，如果不详细了解“演员”之间的活动，动力学和相互作用，我们就无法真正了解细胞功能。然而，蛋白质组学的科学和技术并不适用于可以应用于核酸的相同的高度多路复用的方法，并且用于蛋白质鉴定和定量的策略仍然是高度连续的，需要复杂的并且有时是《双城之战》的数据处理，并且是缓慢的。我们几乎完成了一个概念验证BBSRC电子科学项目，旨在实现蛋白质组学中的两种常用方法：质谱预处理和肽质量指纹数据库搜索，作为一个硬件实现，使用可重构计算机芯片称为现场可编程门阵列（FPGA）。该计算平台的一个关键特征是，通常作为软件程序实现的生物信息学算法被转换为优化的数字硬件处理器，该处理器可以通过并行运行多个分析来显著更快地处理数据。该项目的成功结果是完全实施，实现了惊人的2000倍速度增长。我们现在希望在我们以前的成功基础上，利用我们迄今为止开发的能力，并提供与最常用的基于串联质谱的蛋白质组分析方法相似的速度增益。与此同时，我们将解决对更快和更强的定量的紧急和迫切需求，为蛋白质组学研究人员提供新的定量方法和能力。如果蛋白质组学要实现我们对它作为一门科学的期望，这些工具是至关重要的。

Proteomics is the study of the entire complement of a cell in a particular state. It is the proteins that 'act out' the information in the genome, and we cannot really understand cellular function without a detailed knowledge of the activity, dynamics and interplay between the 'actors'. .However, the science and technology of proteomics does not lend itself to the same highly multiplexed approaches that can be applied to nucleic acids, and strategies for protein identification and quantification are still highly serial, require complex and sometimes arcane data processing, and are slow. We have almost completed a proof-of-concept BBSRC e-science project that aimed to implement two common methods in proteomics: mass spectrum preprocessing and peptide mass fingerprint database searching, as a hardware implementation using reconfigurable computer chips known as field programmable gate arrays (FPGAs). A key feature of this computational platform is that the bioinformatics algorithms which are normally implemented as a software program were translated into optimized digital hardware processors that could process data significantly faster by running multiple analyses in parallel. The successful outcome of this project was a complete implementation that has achieved a phenomenal 2000-fold speed increase. We now wish to build on our previous success, capitalize upon the capabilities we have developed thus far, and deliver similar speed gains to the most commonly used method of proteome analysis, based on tandem mass spectrometry. At the same time, we will address an emergent and pressing need for faster and enhanced quantification to deliver new quantitative approaches and capabilities to proteomics researchers. Such tools are critical if proteomics is to deliver what we expect of it as a science.