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.
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.
|Peterson, Lenna X; Kim, Hyungrae; Esquivel-Rodriguez, Juan et al. (2016) Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions. Proteins :|
|Shin, Woong-Hee; Bures, Mark Gregory; Kihara, Daisuke (2016) PatchSurfers: Two methods for local molecular property-based binding ligand prediction. Methods 93:41-50|
|Kim, Hyungrae; Kihara, Daisuke (2016) Protein structure prediction using residue- and fragment-environment potentials in CASP11. Proteins 84 Suppl 1:105-17|
|Gardner, Nathan W; Monroe, Lyman K; Kihara, Daisuke et al. (2016) Energetic Coupling between Ligand Binding and Dimerization in Escherichia coli Phosphoglycerate Mutase. Biochemistry 55:1711-23|
|Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke (2016) Ensemble-based evaluation for protein structure models. Bioinformatics 32:i314-i321|
|Chitale, Meghana; Khan, Ishita K; Kihara, Daisuke (2016) Missing gene identification using functional coherence scores. Sci Rep 6:31725|
|Jiang, Yuxiang; Oron, Tal Ronnen; Clark, Wyatt T et al. (2016) An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biol 17:184|
|Shin, Woong-Hee; Christoffer, Charles W; Wang, Jibo et al. (2016) PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation. J Chem Inf Model 56:1676-91|
|Kingsley, Laura J; Esquivel-RodrÃguez, Juan; Yang, Ying et al. (2016) Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations. J Comput Chem 37:1861-5|
|Zhu, Xiaolei; Shin, Woong-Hee; Kim, Hyungrae et al. (2016) Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014. J Chem Inf Model 56:1088-99|
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