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Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

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Related Experiment Video

Updated: Mar 23, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

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Published on: April 13, 2022

Molprobity's ultimate rotamer-library distributions for model validation.

Bradley J Hintze¹, Steven M Lewis¹, Jane S Richardson¹

¹Department of Biochemistry, Duke University, Durham North Carolina 27710.

|March 29, 2016

Summary

This summary is machine-generated.

Updated protein rotamer libraries, derived from a larger, quality-filtered dataset, improve conformational analysis and model validation. These "ultimate" rotamers enhance characterization of rare side-chain conformations.

Keywords:

Phenix high-quality dataset protein conformation rare side-chain conformations side-chain rotamer library structural bioinformatics structure validation

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Last Updated: Mar 23, 2026

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Published on: April 13, 2022

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Structural Biology
Computational Biology
Biochemistry

Background:

Protein structure validation relies on accurate rotamer libraries.
Existing libraries may lack sufficient data for rare side-chain conformations.
Improvements in data quality and quantity are needed for robust model validation.

Purpose of the Study:

To describe updated MolProbity rotamer-library distributions.
To explain improvements in protein model validation using enhanced rotamer data.
To introduce a new 'allowed' category for side-chain conformations.

Main Methods:

Utilized an order-of-magnitude larger dataset (~8000 protein chains) than previous studies.
Implemented residue-specific filters for electron-density value and model-to-density fit.
Developed new multi-dimensional chi (χ) distributions for conformational analysis.

Main Results:

Enhanced rotamer distributions derived from a high-quality, large-scale dataset.
Improved ability to characterize rare side-chain conformational clusters.
Introduction of an 'allowed' category for side-chain conformations, analogous to Ramachandran validation.

Conclusions:

The updated rotamer libraries provide a more comprehensive and accurate resource for protein structure validation.
These 'ultimate' rotamers integrate side-chain and backbone criteria for future conformational validation.
The new distributions are actively used in MolProbity and PHENIX software for validation and refinement.