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NBPMF: Novel Peptide Mass Fingerprinting Based on Network Inference

INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2017年第4期11卷 41-65页

作者： Liang, Zhewei Lajoie, Gilles Zhang, Kaizhong Univ Western Ontario Dept Comp Sci London ON Canada Univ Western Ontario Dept Biochem London ON Canada

Mass spectrometry (MS) is an analytical technique for determining the composition of a sample. In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. In this article, the authors developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, they designed new peptide protein matching score functions. They present two methods: the static one, ProbS, is based on an independent probability framework;and the dynamic one, HeatS, depicts input data as dependent peptides. The authors also use linear regression to adjust the matching score according to the masses of proteins. In addition, they consider the order of retention time to further correct the score function. In post processing, a peak can only be assigned to one peptide in order to reduce random matches. Finally, the authors try to filter out false positive proteins. The experiments on simulated and real data demonstrate that their NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.

关键词： Bioinformatics Data Mining knowledge-discovery in databases Mass Spectrometry Network-Based Inference Peptide Mass Fingerprinting Protein Identification Proteomics Recommender Systems

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NBPMF: Novel Network-Based Inference Methods for Peptide Mass Fingerprinting

NBPMF: Novel Network-Based Inference Methods for Peptide Mas...

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作者： Zhewei Liang The University of Western Ontario

学位级别：博士

Proteins are large, complex molecules that perform a vast array of functions in every liv- ing cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches. MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modifi- cation (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting in- tact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin. In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite net- work, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to ad- just the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In th

关键词： Network-Based Inference Peptide Mass Fingerprinting Proteomics Protein Identification Mass Spectrometry knowledge-discovery in databases Recommender Systems Bioinformatics Data Mining

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