Accurate Protein Identification using Peptide Separation Information and Tandem M

描述（由申请人提供）：目前阻碍蛋白质组学研究的一个重要问题是从质谱数据中识别肽和蛋白质。这个问题表现在难以建立蛋白质和多肽鉴定的置信限。一个严重的问题是出现假阳性分配的趋势。集成蛋白鉴定平台的开发将大大降低此类分配的风险。这将使预测生理学和医学公司（PPM）能够对人类血浆中的蛋白质进行无与伦比的表征。有了这种能力，PPM将能够进行必要的人口研究，以创建与健康和疾病相关的蛋白质谱，称为可寻址数字阵列图（ADAM）。能够区分健康和疾病的ADAMs将大大提高PPM在个性化医疗领域的竞争力。此外，该平台本身也具有市场价值。

DESCRIPTION (provided by applicant): A significant problem currently hindering proteomics studies is the identification of peptides and proteins from mass spectral data. This problem is manifest in the difficulty to establish confidence limits of protein and peptide identifications. A serious concern is the tendency for the occurrence of false positive assignments. The development of an integrated protein identification platform will greatly reduce the risk of such assignments. This will allow Predictive Physiology and Medicine, Inc. (PPM) to produce unrivaled characterizations of the proteins in human plasma. With this capability, PPM will be able to perform the population studies necessary to create the protein profiles associated with health and disease, termed Addressable Digital Array Maps (ADAM). The ability to distinguish healthy and disease ADAMs will greatly enhance the competitiveness of PPM in the field of personalized medicine. Additionally, the platform itself will be marketable. The integrated protein identification platform will be created using information that is generally discarded in typical liquid chromatography-mass spectrometry experiments (LC-MS). Algorithms will be developed aimed at predicting peptide separation profiles for two-dimensional (2D) ion mobility spectrometry (IMS, a technology currently only utilized by PPM for proteomics studies). The predicted profiles can be used to decrease protein database sizes which decrease the identity thresholds for assignments; effectively this increases the confidence of peptide assignments as well as increases the number of confident assignments. Additionally, algorithms will be developed to allow the use of multiple protein database search programs. A scoring scheme will be developed based on the union and intersection of resulting assignments. Finally, the algorithms and predictive capabilities will be combined in a single integrated system to determine an accurate p-value for protein assignments which are entered into the ADAMs.The development of the integrated protein identification platform will have a substantial impact on the field of personalized medicine. Such a platform will enable the development of unprecedented plasma protein databases that can be used to determine the disease susceptibility and therapeutic response of different individuals.