MolProbity: all-atom structure validation for macromolecular crystallography

By Vincent B. Chen, W. B. Arendall, J. Headd, Daniel A. Keedy, R. Immormino, Gary J. Kapral, L. Murray, Jane S. Richardson, David C Richardson. 2009

3,449 citations recorded by the source corpus.

MolProbity structure validation will diagnose most local errors in macromolecular crystal structures and help to guide their correction.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

1. David Baker
2. Andriy Kryshtafovych
3. Paul D. Adams
4. Jeffrey J. Gray
5. Frank DiMaio
6. T. Croll
7. Randy J. Read
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In Molecular Biology

1. David Baker
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4. Jeffrey J. Gray
5. Tanja Kortemme
6. Frank DiMaio
7. Yang Zhang
8. Torsten Schwede

Citations in the corpus

• Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “All were required to have both resolution and MolProbity score (Chen et al., 2010) at or below 1.4, very few bond length or angle outliers, and deposited structure-factor data.”
• A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments (2019, methodology): “Compared to the CASP12 web app, the new web app includes (a) distinct coloring of human and server groups in the scores plots (blue indicates at least one model of the cluster was submitted by a server), (c) the possibility to color-code models by residue-wise quality estimates if submitted, and (c)”
• Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta (2016, methodology): “Following refinement against the full reconstruction, model geometry is verified (using MolProbity [15]) to ensure it is not worsening during refinement against the full reconstruction.”
• AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures (2022, methodology): “PSVS runs a suite of knowledge-based software tools including PDBStat (ver 5.21.6) (Tejero et al., 2013), ProCheck (ver 3.5.4) (Laskowski et al., 1993), MolProbity (mage ver 6.35.040409) (Chen et al., 2010), and an implementation of the algorithms of FindCore2 (Snyder et al., 2014) coded in PDBStat.”
• CAD‐score: A new contact area difference‐based function for evaluation of protein structural models (2013, background): “Pearson’s correlation coefficients and Spearman’s rank correlation coefficients are indicated for each plot. conformations outside the favored regions, and side chain rotamer outliers.27 Unlike GDT-TS and CAD-score, the MolProbity score is not a reference-centric measure.”
• Relaxation of backbone bond geometry improves protein energy landscape modeling (2014, methodology): “MolProbity was used to validate bond angle and length distributions to ensure structure predictions were physically realistic.(17)”
• Topology Evaluation of Models for Difficult Targets in the 14th Round of the Critical Assessment of Protein Structure Prediction (CASP14) (2021, methodology): “12 The official rank formula used Z-score sums over the FM and FM/TBM targets for first models (Figure 1A).”
• RNA Structure Refinement using the ERRASER-Phenix pipeline (2013, methodology): “Due to the difficulty of manually fitting RNA backbones into experimental density maps, many of these crystallographic models contain myriad unlikely conformations and unusually close contacts as revealed by automated MolProbity tools for geometric evaluation of models (4, 5).”
• Protein engineering by highly parallel screening of computationally designed variants (2016, background): “To ensure that asparagine, glutamine, and histidine amino acids were correctly oriented, any position with strong evidence for flipping was corrected by MolProbity4 ( 32 ).”
• Structure of the class D GPCR Ste2 dimer coupled to two G proteins (2020, positive): “Model validation was performed in PHENIX, Molprobity 53 and EMRinger 54 .”
• AbPredict 2: A server for accurate and unstrained structure prediction of antibody variable domains (2018, background): “MolProbity scores represent the crystallographic resolution at which structures with similar stereochemical quality are observed (lower scores are stereochemically better)(Chen et al., 2010).”
• RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017, methodology): “Different metrics were used to assess different aspects of the predictions: RMSD stands for the global similarity of all the atoms; deformation index (DI) and the complete deformation profile matrix (DP) stand for prediction accuracy of the nucleotide interactions, while the interaction network fide”