Protein Sequencing Tools for Biological Therapeutics

描述（由申请人提供）：质谱法已成为鉴定和表征复杂混合物中少量蛋白质的首选方法。然而，以高通量方式进行鉴定的能力依赖于蛋白质序列数据库的可用性。这意味着来自具有未测序基因组的生物体的蛋白质（例如肽毒素）和响应于环境而快速改变其一级序列的蛋白质（例如抗体）已被排除在高通量分析之外。 我们建议沿着改进实验室方法来开发算法和软件，使抗体和肽毒素的测序成为快速且低成本的工作。这将使我们能够获得用于临床应用（包括疫苗开发）的个体循环抗体库，并获得用于基础研究和离子通道药物开发的大量生物活性毒液成分。 对于实验室改进，抗体肽和毒素将被化学标记以提高光谱质量，我们将使用不同类型的质谱裂解。将优化数据采集以促进诊断相关肽的鉴定，并将使用气相消化策略来增加较大肽的序列覆盖率。 我们建议开发用于抗体和肽毒素测序的改进算法。这些将整合从头和数据库测序，并将包括候选生成算法，纳入多个渠道的信息：光谱从不同的电荷状态和碎片化方法，同源性约束，组成约束，并在计算机突变的数据库。改进的评分算法也将开发使用微妙的光谱线索，目前只用于手动从头测序。 我们将制作原型软件，并将其与手动注释的质谱进行基准测试。然后，该软件将被应用于自动测序来自HIV的长期非进展者的大量抗体数据以及蜘蛛和锥形蜗牛毒素数据。

DESCRIPTION (provided by applicant): Mass spectrometry has become a method of choice for identifying and characterizing small quantities of proteins in complex mixtures. However, the ability to perform the identification in a high- throughput fashion has depended on the availabilit of protein sequence databases. This means that proteins from organisms with unsequenced genomes (e.g. peptide toxins) and proteins that modify their primary sequence rapidly in response to the environment (e.g. antibodies) have been excluded from high-throughput analysis. We propose to develop algorithms and software along with improving laboratory methods that make sequencing of antibodies and peptide toxins a fast and low-cost effort. This will allow us to access the circulating antibody repertoire of individuals for clinical application including vaccine development, and to access the vast number of bioactive venom components for basic research and ion-channel drug development. For the laboratory improvements, antibody peptides and toxins will be chemically labeled to improve spectral quality and we will use different types of mass spectrometric fragmentation. Data acquisition will be optimized to facilitate identification of diagnostically relevant peptides and a gas- phase digestion strategy will be used to increase the sequence coverage for larger peptides. We propose to develop improved algorithms for sequencing of antibodies and peptide toxins. These will integrate de novo and database sequencing and will include candidate generation algorithms incorporating multiple channels of information: spectra from different charge states and fragmentation methods, homology constraints, composition constraints, and in silico mutation of databases. Improved scoring algorithms will also be developed using subtle spectrum clues, currently used only in manual de novo sequencing. We will produce prototype software, and benchmark it against manually annotated mass spectra. The software will then be applied to automatically sequence a large set of antibody data from long- term non-progressors of HIV, and spider and cone snail toxin data.