What question did this study set out to answer?

Establish an unambiguous, physically motivated, and automated pattern-recognition method to define secondary structure and solvent exposure directly from protein atomic coordinates.

December 1, 1983BiopolymersOpen Access

Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features

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Authors

WKWolfgang KabschMax Planck Institute for Medical Research CSChristian SanderUniversity Hospital Leipzig

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Implication

Computational study establishes standardized secondary structure definitions across 62 globular proteins, indicating broad utility for automated protein structure prediction.

Key Points

Establish an unambiguous, physically motivated, and automated pattern-recognition method to define secondary structure and solvent exposure directly from protein atomic coordinates.
Developed a pattern-recognition algorithm to identify hydrogen-bonding patterns (turns and bridges) and geometric features (torsion, curvature, and chirality) from X-ray coordinates.
Calculated residue solvent exposure as the theoretical number of water molecules in contact with each amino acid.
Applied the standardized definitions across a dataset of 62 globular proteins containing 10,925 total residues.
Systematically classified secondary structure elements into repeating turns (helices), repeating bridges (ladders and sheets), and curved regions (bends).
Generated a comprehensive linear and computer-readable database detailing primary structure, disulfide bonds, secondary structure, and solvent exposure for all 10,925 residues.

Cite This Study

Kabsch et al. (1983) studied this question.

synapsesocial.com/papers/69d6b5abf174babf6cab33bc https://doi.org/10.1002/bip.360221211

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December 1, 1983BiopolymersOpen Access

Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features

View Full Paper

Ask AI

Bookmark

Authors

WKWolfgang KabschMax Planck Institute for Medical Research CSChristian SanderUniversity Hospital Leipzig

Discussion

Member takes

Implication

Computational study establishes standardized secondary structure definitions across 62 globular proteins, indicating broad utility for automated protein structure prediction.

Key Points

Establish an unambiguous, physically motivated, and automated pattern-recognition method to define secondary structure and solvent exposure directly from protein atomic coordinates.
Developed a pattern-recognition algorithm to identify hydrogen-bonding patterns (turns and bridges) and geometric features (torsion, curvature, and chirality) from X-ray coordinates.
Calculated residue solvent exposure as the theoretical number of water molecules in contact with each amino acid.
Applied the standardized definitions across a dataset of 62 globular proteins containing 10,925 total residues.
Systematically classified secondary structure elements into repeating turns (helices), repeating bridges (ladders and sheets), and curved regions (bends).
Generated a comprehensive linear and computer-readable database detailing primary structure, disulfide bonds, secondary structure, and solvent exposure for all 10,925 residues.

Cite This Study

Kabsch et al. (1983) studied this question.

synapsesocial.com/papers/69d6b5abf174babf6cab33bc https://doi.org/10.1002/bip.360221211

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