Accurate Protein Identification using Peptide Separation Information and Tandem M

Predictive Physiology And Medicine, Inc., Bloomington IN

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Abstract

[unreadable] 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. [unreadable] [unreadable] 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. [unreadable] [unreadable] [unreadable]