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PDB_REDO: constructive validation, more than just looking for errors.

ABSTRACT: Developments of the PDB_REDO procedure that combine re-refinement and rebuilding within a unique decision-making framework to improve structures in the PDB are presented. PDB_REDO uses a variety of existing and custom-built software modules to choose an optimal refinement protocol (e.g. anisotropic, isotropic or overall B-factor refinement, TLS model) and to optimize the geometry versus data-refinement weights. Next, it proceeds to rebuild side chains and peptide planes before a final optimization round. PDB_REDO works fully automatically without the need for intervention by a crystallographic expert. The pipeline was tested on 12?000 PDB entries and the great majority of the test cases improved both in terms of crystallographic criteria such as R(free) and in terms of widely accepted geometric validation criteria. It is concluded that PDB_REDO is useful to update the otherwise `static' structures in the PDB to modern crystallographic standards. The publically available PDB_REDO database provides better model statistics and contributes to better refinement and validation targets.

SUBMITTER: Joosten RP

PROVIDER: S-EPMC3322608 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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PDB_REDO: constructive validation, more than just looking for errors.

Joosten Robbie P RP Joosten Krista K Murshudov Garib N GN Perrakis Anastassis A

Acta crystallographica. Section D, Biological crystallography 20120316 Pt 4

Developments of the PDB_REDO procedure that combine re-refinement and rebuilding within a unique decision-making framework to improve structures in the PDB are presented. PDB_REDO uses a variety of existing and custom-built software modules to choose an optimal refinement protocol (e.g. anisotropic, isotropic or overall B-factor refinement, TLS model) and to optimize the geometry versus data-refinement weights. Next, it proceeds to rebuild side chains and peptide planes before a final optimizati ...[more]

PMID: 22505269

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Project description:PurposeDecades of quality control efforts have raised the standards of immunohistochemistry (IHC), the principle method used for biomarker testing in breast cancer; however, computational pathology and reverse transcription quantitative PCR (RT-qPCR) may also hold promise for additional substantial improvements.MethodsHerein, we investigated discrepancies in the assessment of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and marker of proliferation Ki67 comparing routinely obtained IHC (and FISH) data (ORI) with the results of manual (REV) and semi-automated (DIA) re-evaluation of the original IHC slides and then with RNA expression data from the same tissue block using the MammaTyper® (MT) gene expression assay.ResultsCorrelation for ER and PR was high between ORI IHC and the other three study methods (REV, DIA and RT-qPCR). For HER2, 10 out of 96 discrepant cases can be detected between ORI and REV that involved at least one call in the equivocal category (except for one case). For Ki67, 22 (29.1%) cases were categorized differently by either REV alone (n = 17), DIA alone (n = 15) or both (n = 10) and 28 cases (29.2%) for RT-qPCR. Most of the discrepant Ki67 cases changed from low to high between the original and following assessment and belonged to the intermediate Ki67 expression range (between 9 and 30%).ConclusionsDetermination of the breast cancer biomarkers ER, PR, HER2 and Ki67 at the mRNA level shows high degree of correlation with IHC and compares well with correlations between original with subsequent independent manual or semi-automated IHC assessments. The use of methods with wider dynamic range and higher reproducibility such as RT-qPCR may offer more precise assessment of endocrine responsiveness, improve Ki67 standardization and help resolve HER2 cases that remain equivocal or ambiguous by IHC/FISH. In summary, our findings seem to configure RT-qPCR as a complementary method to be used in cases of either equivocal results or presenting, at the traditional determination assays, biomarkers expressions close to the cut-off values.

Dataset Information

PDB_REDO: constructive validation, more than just looking for errors.

Publications

PDB_REDO: constructive validation, more than just looking for errors.

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