The long-term goal of this proposal is to develop effective integrated computational and experimental approaches to identify interactions between proteins and major metabolites on a proteomics scale. The proposed approach allows rapid identification of ligand binding proteins on a proteomics scale and constructing protein-metabolite interaction network, which will provide crucial information for understanding biological systems. As a model case, we will identify NAD+ and NADP+ binding proteins in the E. coli proteome. In particular, the following specific aims are proposed: (1) To develop a set of computational methods for predicting proteins that bind to metabolites. We employ both sequence- based and structure-based methods. The sequence-based methods we will develop and employ include our novel function prediction methods, PFP and its variant, which are shown to have higher sensitivity and higher function assignment coverage than conventional methods. Structure-based methods include fast local protein surface shape comparison method, which directly compare shape and physicochemical property of local surface regions. (2) To apply energetics-based target identification approach to efficiently screen proteins that bind to metabolites. Proteins stabilized upon binding to NAD+ and NADP+ will be identified in a E. coli lysate by combining a brief incubation with a protease and quantitative mass spectrometry. Proteins identified by either computational or experimental methods will be cross-validated by the complementary approaches. Successful completion of this project will establish methodology for systematic identification of proteins that bind to specific metabolites and thus will enable us to provide interaction network of proteins and metabolites in cells. The methodology to be developed and the resulting interaction network will assist in the early stages of drug discovery, and hence the proposed project could have significant therapeutic utility.

Public Health Relevance

The goal of the project is the development of intergrated computational and experimental approach to determine interactions between proteins and major matabolites in cells on a systems level. The proposed approach will allow rapid identification of ligand binding proteins on a proteomics scale and will enable the construction of protein-metaboolite interaction networks. The outcome of this project will provide information crucial in understanding biological systems and useful in hypothesis generation in the early stages of drug discovery.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM097528-03
Application #
8477213
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
2011-09-01
Project End
2015-05-31
Budget Start
2013-06-01
Budget End
2014-05-31
Support Year
3
Fiscal Year
2013
Total Cost
$269,883
Indirect Cost
$86,533
Name
Purdue University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
072051394
City
West Lafayette
State
IN
Country
United States
Zip Code
47907
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Ding, Ziyun; Kihara, Daisuke (2018) Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. Curr Protoc Protein Sci 93:e62
Terashi, Genki; Kihara, Daisuke (2018) De novo main-chain modeling for EM maps using MAINMAST. Nat Commun 9:1618
Shin, Woong-Hee; Kihara, Daisuke (2018) Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method. Methods Mol Biol 1762:105-121
Khan, Ishita K; Bhuiyan, Mansurul; Kihara, Daisuke (2017) DextMP: deep dive into text for predicting moonlighting proteins. Bioinformatics 33:i83-i91
Peterson, Lenna X; Roy, Amitava; Christoffer, Charles et al. (2017) Modeling disordered protein interactions from biophysical principles. PLoS Comput Biol 13:e1005485
Zeng, Lingfei; Shin, Woong-Hee; Zhu, Xiaolei et al. (2017) Discovery of Nicotinamide Adenine Dinucleotide Binding Proteins in the Escherichia coli Proteome Using a Combined Energetic- and Structural-Bioinformatics-Based Approach. J Proteome Res 16:470-480
Peterson, Lenna; Jamroz, Michal; Kolinski, Andrzej et al. (2017) Predicting Real-Valued Protein Residue Fluctuation Using FlexPred. Methods Mol Biol 1484:175-186
Shin, Woong-Hee; Kang, Xuejiao; Zhang, Jian et al. (2017) Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models. Sci Rep 7:40629

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