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Periodicity in alpha-helix lengths and C-capping preferences
S Penel1, R G Morrison, R J Mortishire-Smith
1Department of Biomolecular Sciences, UMIST, Manchester, M60 1QD, UK.
Journal of Molecular Biology
|November 5, 1999
Summary
Protein alpha-helices exhibit preferred lengths, influencing their capping and surface accessibility. These findings suggest incorporating helix length into predictive algorithms for protein structure.
Area of Science:
- Structural Biology
- Protein Biochemistry
- Computational Biology
Background:
- Alpha-helices are fundamental protein secondary structures.
- Helix length and capping motifs play crucial roles in protein folding and function.
- Predictive algorithms for protein structure often overlook helix length as a key parameter.
Purpose of the Study:
- To investigate the distribution of alpha-helix lengths in protein crystal structures.
- To analyze the relationship between helix length and N- and C-terminal capping preferences.
- To provide insights for improving computational prediction of helices and capping motifs.
Main Methods:
- Surveyed 299 high-resolution, non-homologous protein crystal structures.
- Analyzed alpha-helix lengths and identified preferred and disfavored residue counts.
- Examined amino acid preferences at N- and C-termini and C-capping motifs (Schellman, alphaL).
Main Results:
- Alpha-helices show a preference for lengths close to an integral number of turns.
- Specific residue counts (e.g., 6, 7, 10, 11) are favored, while others (e.g., 8, 9, 12) are disfavored.
- Favored length helices display distinct C-capping preferences (non-polar at C4, polar at C2) compared to disfavored lengths (non-polar at C2).
- Periodic trends in C-capping motifs correlate with side-chain burial preferences.
Conclusions:
- Helix length is a significant factor influencing alpha-helix structure and capping.
- Favored helix lengths facilitate surface exposure through specific capping strategies.
- Algorithms for predicting protein helices and C-capping should incorporate helix length as a predictive feature.