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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Corn Host Plant Resistance Research » Research » Publications at this Location » Publication #186381
Title: COMPUTATIONAL TOOLS FOR PROTEIN IDENTIFICATION AND GENE ONTOLOGY ANNOTATION OF THE MAIZE PROTEOME

Author
item BRIDGES, SUSAN - MISSISSIPPI STATE UNIV
item HODGES, JULIA - MISSISSIPPI STATE UNIV
item MAGEE, GREGORY - MISSISSIPPI STATE UNIV
item WANG, NAN - MISSISSIPPI STATE UNIV
item LUTHE, DAWN - MISSISSIPPI STATE UNIV
item Williams, William

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2005
Publication Date: 10/23/2005
Citation: Bridges, S.M., Hodges, J.E., Magee, G.B., Wang, N., Luthe, D.S., Williams, W.P. 2005. Computational tools for protein identification and gene ontology annotation of the maize proteome [abstract]. In: Proceedings of the 2005 Multicrop Aflatoxin/Fumonisin Elimination & Fungal Genomics Workshop, October 23-27, 2005, Raleigh, North Carolina. p. 75.

Interpretive Summary:

Technical Abstract: Characterization of the maize proteome of the developing ear under different conditions has the potential to reveal the fundamental processes that confer resistance in some cell lines. Advances in proteomics have been made possible by high-throughput methods for gel electrophoresis and new technologies for mass spectrometry such as LC/MS/MS. We have previously reported the development of the PIE database of translated ESTs and have shown that use of this database for protein identification for a 2-dimensional gel experiment with cob proteins results in identification of 87.5% of the spots compared to a 56% identification rate with the NCBI database non-redundant green plant database. The PIE database pipeline has been parallelized and now runs on a high performance cluster, making it possible to rapidly generate updated databases. Additional tools have been developed to streamline the protein identification process and to provide the Gene Ontology annotation of the identified proteins. The multi-dimensional protein identification technology (MudPIT) can be used to separate many hundreds to thousands of peptides in a single experiment. The results obtained from Sequest analysis of MudPIT experiments can be quite challenging to analyze, particularly when the database used for queries is highly redundant. This is the case when using translated ESTs because many correspond to the same protein or to closely related proteins. Scientists typically must integrate information from several repetitions and data sources to determine confidence in an identication. The PepSort tool was developed to assist with this type of analysis. The tool combines multiple reps selecting the best score for each peptide based on a user specified scoring system. Potential protein duplicate identifications are collected and presented to the user simultaneously so the user can select the best identification and eliminate duplicates. Scores and counts for peptides are updated automatically when duplicates are removed. We have deployed the MaizeGO database, as part of Agbase www.agbase.msstate.edu, a curated, open-source, Web-accessible resource for functional analysis of agricultural plant and animal gene products.