Literature DB >> 23385452

Techniques, tools and best practices for ligand electron-density analysis and results from their application to deposited crystal structures.

Edwin Pozharski1, Christian X Weichenberger, Bernhard Rupp.   

Abstract

As a result of substantial instrumental automation and the continuing improvement of software, crystallographic studies of biomolecules are conducted by non-experts in increasing numbers. While improved validation almost ensures that major mistakes in the protein part of structure models are exceedingly rare, in ligand-protein complex structures, which in general are most interesting to the scientist, ambiguous ligand electron density is often difficult to interpret and the modelled ligands are generally more difficult to properly validate. Here, (i) the primary technical reasons and potential human factors leading to problems in ligand structure models are presented; (ii) the most common categories of building errors or overinterpretation are classified; (iii) a few instructive and specific examples are discussed in detail, including an electron-density-based analysis of ligand structures that do not contain any ligands; (iv) means of avoiding such mistakes are suggested and the implications for database validity are discussed and (v) a user-friendly software tool that allows non-expert users to conveniently inspect ligand density is provided.

Entities:  

Keywords:  evidence-based reasoning; omit difference density; protein–ligand structures; validation

Mesh:

Substances:

Year:  2013        PMID: 23385452     DOI: 10.1107/S0907444912044423

Source DB:  PubMed          Journal:  Acta Crystallogr D Biol Crystallogr        ISSN: 0907-4449


  59 in total

1.  Progress in protein crystallography.

Authors:  Zbigniew Dauter; Alexander Wlodawer
Journal:  Protein Pept Lett       Date:  2016       Impact factor: 1.890

2.  Models of protein-ligand crystal structures: trust, but verify.

Authors:  Marc C Deller; Bernhard Rupp
Journal:  J Comput Aided Mol Des       Date:  2015-02-10       Impact factor: 3.686

3.  Protein structural ensembles are revealed by redefining X-ray electron density noise.

Authors:  P Therese Lang; James M Holton; James S Fraser; Tom Alber
Journal:  Proc Natl Acad Sci U S A       Date:  2013-12-20       Impact factor: 11.205

4.  Unexpected features in the Protein Data Bank entries 3qd1 and 4i8e: the structural description of the binding of the serine-rich repeat adhesin GspB to host cell carbohydrate receptor is not a solved issue.

Authors:  Yves A Muller
Journal:  Acta Crystallogr Sect F Struct Biol Cryst Commun       Date:  2013-09-28

5.  The quality and validation of structures from structural genomics.

Authors:  Marcin J Domagalski; Heping Zheng; Matthew D Zimmerman; Zbigniew Dauter; Alexander Wlodawer; Wladek Minor
Journal:  Methods Mol Biol       Date:  2014

Review 6.  X-ray crystallography over the past decade for novel drug discovery - where are we heading next?

Authors:  Heping Zheng; Katarzyna B Handing; Matthew D Zimmerman; Ivan G Shabalin; Steven C Almo; Wladek Minor
Journal:  Expert Opin Drug Discov       Date:  2015-07-15       Impact factor: 6.098

Review 7.  Three-dimensional structures in the design of therapeutics targeting parasitic protozoa: reflections on the past, present and future.

Authors:  Wim G J Hol
Journal:  Acta Crystallogr F Struct Biol Commun       Date:  2015-04-16       Impact factor: 1.056

Review 8.  Three-dimensional structures of laccases.

Authors:  N Hakulinen; J Rouvinen
Journal:  Cell Mol Life Sci       Date:  2015-01-14       Impact factor: 9.261

Review 9.  A close look onto structural models and primary ligands of metallo-β-lactamases.

Authors:  Joanna E Raczynska; Ivan G Shabalin; Wladek Minor; Alexander Wlodawer; Mariusz Jaskolski
Journal:  Drug Resist Updat       Date:  2018-08-25       Impact factor: 18.500

10.  Automatic recognition of ligands in electron density by machine learning.

Authors:  Marcin Kowiel; Dariusz Brzezinski; Przemyslaw J Porebski; Ivan G Shabalin; Mariusz Jaskolski; Wladek Minor
Journal:  Bioinformatics       Date:  2019-02-01       Impact factor: 6.937

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