Improving Ligand Geometry in Protein Data Bank Structures Computationally

Miller, Beck; Hudson, Brian P.; Shao, Chenghua; Wang, Lu; Zardecki, Christine; Burley, Stephen K.

doi:10.1096/fasebj.2019.33.1_supplement.784.8

Title: Improving Ligand Geometry in Protein Data Bank Structures Computationally

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Abstract

Information about the three‐dimensional structures of complex biological molecules is an indispensable research tool for many fields of study, including biology, chemistry, pharmacology, and computer science. The Protein Data Bank (PDB) is an public digital archive providing access to structures of nucleic acids, proteins, and large macromolecular machines determined experimentally by X‐ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and 3‐dimensional electron microscopy (3DEM). PDB structures undergo validation for structural plausibility upon their deposition into the PDB using the program Mogul, which cross references each deposited structure against similar data in the Cambridge Structural Database (CSD). Ligands associated with PDB structures are a common source of structural inaccuracy. In this study, a programmatic pipeline was developed to determine if ligand geometric accuracy could be improved using known computational tools. Ligands with dubious geometry were identified through a Python mining code. A sample set of the identified structures was remodeled into minimum energy conformations using the molecular dynamics program AMBER and then fit to the original experimental density maps using the molecular modeling program Coot. The refined models were compared to the original models in the PDB as well as to similar structures in the CSD using the deposition statistical validation server.more »« less

Authors:

Miller, Beck ^[1]; Hudson, Brian P. ^[2]; Shao, Chenghua ^[2]; Wang, Lu ^[3]; Zardecki, Christine ^[2]; Burley, Stephen K. ^[2]

Smith College Northampton MA, RCSB PDB The State University of New Jersey Piscataway NJ
RCSB PDB The State University of New Jersey Piscataway NJ, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey Piscataway NJ
Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey Piscataway NJ

Publication Date:: Mon Apr 01 00:00:00 EDT 2019

Sponsoring Org.:: USDOE

OSTI Identifier:: 1786658

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: FASEB Journal

Additional Journal Information:: Journal Name: FASEB Journal Journal Volume: 33 Journal Issue: S1; Journal ID: ISSN 0892-6638

Publisher:: FASEB

Country of Publication:: United States

Language:: English

Citation Formats


                    Miller, Beck, Hudson, Brian P., Shao, Chenghua, Wang, Lu, Zardecki, Christine, and Burley, Stephen K. Improving Ligand Geometry in Protein Data Bank Structures Computationally.  United States: N. p., 2019. 
Web.  doi:10.1096/fasebj.2019.33.1_supplement.784.8.

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                    Miller, Beck, Hudson, Brian P., Shao, Chenghua, Wang, Lu, Zardecki, Christine, & Burley, Stephen K. Improving Ligand Geometry in Protein Data Bank Structures Computationally.  United States.  https://doi.org/10.1096/fasebj.2019.33.1_supplement.784.8

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                    Miller, Beck, Hudson, Brian P., Shao, Chenghua, Wang, Lu, Zardecki, Christine, and Burley, Stephen K. Mon .  
"Improving Ligand Geometry in Protein Data Bank Structures Computationally".  United States.  https://doi.org/10.1096/fasebj.2019.33.1_supplement.784.8.

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@article{osti_1786658,

  title        = {Improving Ligand Geometry in Protein Data Bank Structures Computationally},

  author       = {Miller, Beck and Hudson, Brian P. and Shao, Chenghua and Wang, Lu and Zardecki, Christine and Burley, Stephen K.},

  abstractNote = {Information about the three‐dimensional structures of complex biological molecules is an indispensable research tool for many fields of study, including biology, chemistry, pharmacology, and computer science. The Protein Data Bank (PDB) is an public digital archive providing access to structures of nucleic acids, proteins, and large macromolecular machines determined experimentally by X‐ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and 3‐dimensional electron microscopy (3DEM). PDB structures undergo validation for structural plausibility upon their deposition into the PDB using the program Mogul, which cross references each deposited structure against similar data in the Cambridge Structural Database (CSD). Ligands associated with PDB structures are a common source of structural inaccuracy. In this study, a programmatic pipeline was developed to determine if ligand geometric accuracy could be improved using known computational tools. Ligands with dubious geometry were identified through a Python mining code. A sample set of the identified structures was remodeled into minimum energy conformations using the molecular dynamics program AMBER and then fit to the original experimental density maps using the molecular modeling program Coot. The refined models were compared to the original models in the PDB as well as to similar structures in the CSD using the deposition statistical validation server. Preliminary results suggest that the pipeline effectively identifies and improves ligand structures that possess inaccurate geometry. This method can be used to evaluate and improve the accuracy of specific ligands in structures both archived and pending deposition. Support or Funding Information This work was supported by an NSF REU and the RCSB PDB [NSF (DBI‐1338415), NIH, and DOE.] This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .},

  doi          = {10.1096/fasebj.2019.33.1_supplement.784.8},

  journal      = {FASEB Journal},

  number       = S1,

  volume       = 33,

  place        = {United States},

  year         = {Mon Apr 01 00:00:00 EDT 2019},

  month        = {Mon Apr 01 00:00:00 EDT 2019}

}

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