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

蛋白质组学是研究处于特定状态的细胞的全部成分。正是蛋白质“作用于”基因组中的信息，如果不详细了解“参与者”之间的活动、动态和相互作用，我们就不能真正了解细胞的功能。然而，蛋白质组学的科学和技术并不适合于可以应用于核酸的高度多元化的方法，蛋白质鉴定和量化的策略仍然高度连续，需要复杂的、有时甚至是神秘的数据处理，并且速度很慢。我们几乎完成了一个概念验证BBSRC电子科学项目，该项目旨在实现蛋白质组学中的两种常见方法：质谱预处理和多肽质量指纹数据库搜索，作为使用称为现场可编程门阵列(FPGA)的可重新配置计算机芯片的硬件实现。这个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化为优化的数字硬件处理器，通过并行运行多个分析，这些处理器可以显著加快数据处理速度。该项目的成功成果是完全实现了速度提高了2000倍的惊人成绩。我们现在希望在以前成功的基础上，利用我们迄今开发的能力，提供与最常用的基于串联质谱学的蛋白质组分析方法类似的速度提升。与此同时，我们将满足对更快和更强的量化的紧急和迫切需求，以向蛋白质组学研究人员提供新的量化方法和能力。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.