MolProbity’s Ultimate Rotamer-Library Distributions for Model Validation

By B. Hintze, Steven M. Lewis, Jane S. Richardson, David C Richardson. 2016

108 citations recorded by the source corpus.

Here we describe the updated MolProbity rotamer‐library distributions derived from an order‐of‐magnitude larger and more stringently quality‐filtered dataset of about 8000 (vs. 500) protein chains, and we explain the resulting changes and improvements to model validation as seen by users. To include only side‐chains with satisfactory justification for their given conformation, we added residue‐specific filters for electron‐density value and model‐to‐density fit. The combined new protocol retains a million residues of data, while cleaning up false‐positive noise in the multi‐ χ datapoint distributions. It enables unambiguous characterization of conformational clusters nearly 1000‐fold less frequent than the most common ones. We describe examples of local interactions that favor these rare conformations, including the role of authentic covalent bond‐angle deviations in enabling presumably strained side‐chain conformations. Further, along with favored and outlier, an allowed category (0.3–2.0% occurrence in reference data) has been added, analogous to Ramachandran validation categories. The new rotamer distributions are used for current rotamer validation in MolProbity and PHENIX, and for rotamer choice in PHENIX model‐building and refinement. The multi‐dimensional χ distributions and Top8000 reference dataset are freely available on GitHub. These rotamers are termed “ultimate” because data sampling and quality are now fully adequate for this task, and also because we believe the future of conformational validation should integrate side‐chain with backbone criteria. Proteins 2016; 84:1177–1189. © 2016 Wiley Periodicals, Inc.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix (2019, background): “Side-chain rotamer distributions have recently been updated (Hintze et al., 2016).”
Solving the scalability issue in quantum-based refinement: Q|R#1. (2017, background): “The classifications are based on the probability score (Hintze et al., 2016), with a score of better than 2% being ‘favoured’, a score of below 0.3% being an ‘outlier’ and the remainder being ‘allowed’.”
ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps (2018, background): “…pioneers (Hooft et al., 1996; Kleywegt, 2000; Laskowski et al., 1998) has grown near-comprehensive resources such as MolProbity (Chen et al., 2010; Hintze et al., 2016) and the wwPDB validation pipeline (Read et al., 2011), providing very well defined probability distributions, for example, for…”
Deep Learning in Protein Structural Modeling and Design (2020, unknown): “Similarly, in protein modeling, well-established empirical observations can help restrict the solution space, such as the Ramanchandran distribution of backbone torsion angles(214) and the Dunbrack or Richardsons library of side-chain conformations.(225,226)”
Model validation: local diagnosis, correction and when to quit (2018, methodology): “Our most recent rotamer update (Hintze et al., 2016) is in MolProbity and PHENIX but not yet in wwPDB validation.”
MolProbity: More and better reference data for improved all‐atom structure validation (2018, methodology): “See the rotamer paper 44 for methods, a complete list of rotamer names, v values and reference-data frequencies, and an explanation of why we can call this MolProbity’s “ultimate” rotamer library.”