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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Soybean Genomics & Improvement Laboratory » Research » Publications at this Location » Publication #308569
Title: SCNProDB: A database for the identification of soybean cyst nematode proteins

Author
item TAVAKOLAN, MONA - Towson University
item ALKHAROUF, NADIM - Towson University
item Matthews, Benjamin
item Natarajan, Savithiry - Savi

Submitted to: Bioinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2014
Publication Date: 9/1/2014
Citation: Tavakolan, M., Alkharouf, N.W., Matthews, B.F., Natarajan, S.S. 2014. SCNProDB: A database for the identification of soybean cyst nematode proteins. Bioinformation. 10(9):400-405.

Interpretive Summary: Risk assessment of GMO crops is challenging due to the low expression of allergen proteins. To address this problem, we developed a simple, cost effective, and novel method using isopropanol extraction and characterized 107 enriched low abundant proteins. Using these proteins, we developed an on-line database which contains the characterization of low abundant proteins. This database is publicly available. The results of this study will be useful to scientists to understand the low abundant allergen protein composition of soybean seeds and to develop hypoallergenic soybeans through genetic manipulation and breeding efforts.

Technical Abstract: Soybeans are an important legume crop that contain 2 major storage proteins, ß-conglycinin and glycinin, which account about 70-80% of total seed proteins. These abundant proteins hinder the isolation and characterization of several low abundant proteins in soybean seeds. Several protein extraction methodologies were developed in our laboratory to decrease these abundant storage proteins in seed extracts and to also decrease the amount of ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO), which is normally very abundant in leaf extracts. One of the extraction methodologies used 40% isopropanol and was more effective in depleting soybean storage proteins and enhancing low abundant seed proteins than similar methods using 10-80% isopropanol. Extractions performed with 40% isopropanol decreased the amount of storage proteins by 80% and revealed 107 low abundant proteins when using the combined approaches of 2D-PAGE and Mass Spectrometry (MS). The separation of proteins was achieved by iso-electric focusing (IEF) and 2D-PAGE. The proteins were analyzed with MS techniques to provide amino acid sequence. The proteins were identified by comparing their amino acid sequences with those in different databases including NCBI-non redundant, UniprotKB and MSDB databases. In this investigation, previously published results on low abundant soybean seed proteins were used to create an online database (SoyProLow) to provide a data repository that can be used as a reference to identify and characterize low abundance proteins. This database is freely accessible to individuals using similar techniques and can be used to guide subsequent genetic manipulation to produce value added soybean traits. An intuitive user interface based on dynamic HTML enables users to browse the network and the profiles of the low abundant proteins. The SoyProLow is available at: http://bioinformatics.towson.edu/Soybean_low_abundance_proteins_2D_Gel_DB/Gel1.aspx