Open AccessMultiProtIdent: Identifying Proteins Using Database Search and Protein−Protein Interactions
Author(s) -
HsienDa Huang,
Tzong-Yi Lee,
Li-Cheng Wu,
Feng-Mao Lin,
HsuehFen Juan,
JorngTzong Horng,
AnnPing Tsou
Publication year - 2005
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/pr0498335
Subject(s) - false positive paradox , computational biology , database search engine , identification (biology) , proteomics , peptide mass fingerprinting , protein–protein interaction , peptide , mass spectrometry , bottom up proteomics , computer science , chemistry , bioinformatics , database , biology , data mining , tandem mass spectrometry , biochemistry , information retrieval , search engine , protein mass spectrometry , chromatography , artificial intelligence , gene , botany
Protein identification is important in proteomics. Proteomic analyses based on mass spectra (MS) constitute innovative ways to identify the components of protein complexes. Instruments can obtain the mass spectrum to an accuracy of 0.01 Da or better, but identification errors are inevitable. This study shows a novel tool, MultiProtIdent, which can identify proteins using additional information about protein-protein interactions and protein functional associations. Both single and multiple Peptide Mass Fingerprints (PMFs) are input to MultiProtIdent, which matches the PMFs to a theoretical peptide mass database. The relationships or interactions among proteins are considered to reduce false positives in PMF matching. Experiments to identify protein complexes reveal that MultiProtIdent is highly promising. The website associated with this study is http://dbms104.csie.ncu.edu.tw/.
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