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Updated: Jul 23, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Overall protein structure quality assessment using hydrogen-bonding parameters
Pavel V Afonine1, Oleg V Sobolev1, Nigel W Moriarty1
1Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Refining low-resolution atomic models is difficult. New methods use hydrogen bond geometry, a conserved feature in proteins, to validate atomic models, improving accuracy without compromising existing validation tools.
Area of Science:
- Structural Biology
- Biophysics
- Computational Biology
Background:
- Atomic model refinement at low resolution presents challenges due to insufficient experimental data detail.
- Current refinement practices often employ Ramachandran plots and rotameric states, potentially diminishing their validation power.
- There is a need for novel model-validation criteria not typically used as refinement targets.
Purpose of the Study:
- To investigate the utility of hydrogen bond geometry as a novel criterion for atomic model validation.
- To establish a method for assessing atomic model quality using conserved hydrogen bond distributions.
Main Methods:
- Systematic analysis of hydrogen bond geometries in high-resolution protein models from the Protein Data Bank.
- Characterization of hydrogen donor and acceptor atom distributions and their geometric properties.
- Development of a validation approach based on observed hydrogen bond geometric patterns.
Main Results:
- Analysis revealed distinct and conserved geometric distributions for hydrogen bonds in quality-filtered protein structures.
- These conserved geometric features can be quantified and utilized for assessing atomic model quality.
- The proposed method provides an independent validation criterion for atomic models.
Conclusions:
- Hydrogen bond geometry offers a valuable and underutilized resource for atomic model validation, particularly at low resolution.
- This approach enhances the reliability of atomic models without interfering with existing validation metrics.
- The findings contribute to more accurate protein structure determination and analysis.
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