One critical need for the development of reliable docking and scoring (d/s) is a large dataset containing high quality experimental protein-ligand complex structures, together with accurate binding affinity data. An online d/s resource (d/sResource) will be created by taking advantage of the PIs extensive expertise in this area and through community participation. SA1 will build the largest, freely accessible database of protein-ligand complexes with experimentally determined binding affinities from literature. This new resource will build off of the two largest protein-ligand datasets in existence: Wang's PDBbind and Carlson's Binding MOAD. SA2 will generate new experimental data. The lack of consistency between binding affinity data generated from different research groups and using different experimental techniques/conditions is another major hurdle. To address this deficiency, dissociation constants (Kds) for selected protein-ligand complexes will be determined using two complementary techniques: isothermal calorimetry and surface plasmon resonance. Furthermore, important physicochemical properties for the ligands will be determined (logP/logD, pKa, and solubility), and additional crystal structures will be solved. SA3 will curate data from the community. Deposition of large datasets will be requested from pharma, the NIH, and academia.
This Aim i ncludes solving partially completed crystal structures deposited into the d/sResource and analysis of deposited data for diversity/similarity across the ligands and proteins to prioritize for further experimental investigation in SA2. SA4 outlines the proposed community outreach. The d/sResource will not be a Michigan-only endeavor. Data will be deposited in many repositories: structures into the PDB, ITC data into BindingDB, and chemical information into Pubchem and NIST databases. Collaborations will be sought with other groups. Contests and meetings for d/s will be held, and a Visiting Scholars Program will be established to encourage new developments and facilitate the sharing of resources. The project will provide a unique resource that is needed to improve in the field of structure-based drug design. Better techniques will save time and money in the development of new treatments, ultimately providing new drugs more quickly and at less expense to the greater population.
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|Carlson, Heather A (2016) Lessons Learned over Four Benchmark Exercises from the Community Structure-Activity Resource. J Chem Inf Model 56:951-4|
|Carlson, Heather A; Smith, Richard D; Damm-Ganamet, Kelly L et al. (2016) CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma. J Chem Inf Model 56:1063-77|
|Gathiaka, Symon; Liu, Shuai; Chiu, Michael et al. (2016) D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol Des 30:651-668|
|Ahmed, Aqeel; Smith, Richard D; Clark, Jordan J et al. (2015) Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures. Nucleic Acids Res 43:D465-9|
|Dunbar Jr, James B; Smith, Richard D; Damm-Ganamet, Kelly L et al. (2013) CSAR data set release 2012: ligands, affinities, complexes, and docking decoys. J Chem Inf Model 53:1842-52|
|Damm-Ganamet, Kelly L; Smith, Richard D; Dunbar Jr, James B et al. (2013) CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model 53:1853-70|
|Carlson, Heather A (2013) Check your confidence: size really does matter. J Chem Inf Model 53:1837-41|
|Smith, Richard D; Dunbar Jr, James B; Ung, Peter Man-Un et al. (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51:2115-31|
|Dunbar Jr, James B; Smith, Richard D; Yang, Chao-Yie et al. (2011) CSAR benchmark exercise of 2010: selection of the protein-ligand complexes. J Chem Inf Model 51:2036-46|
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