Characterization and sequence prediction of structural variations in [alpha] -helix

• Published in: BMC bioinformatics 2011-01, Vol.12 (Suppl 1), p.S20
• Main Authors: Tendulkar, Ashish V, Wangikar, Pramod P
• Format: Article
• Language: English
• Subjects: Bioinformatics; Computer science; Geometry; Standard deviation; Studies
• ISSN: 1471-2105
• DOI: 10.1186/1471-2105-12-S1-S20

Abstract Background: The structure conservation in various α -helix subclasses reveals the sequence and context dependent factors causing distortions in the α -helix. The sequence-structure relationship in these subclasses can be used to predict structural variations in α -helix purely based on its sequence. We train support vector machine(SVM) with dot product kernel function to discriminate between regular α -helix and non-regular α -helices purely based on the sequences, which are represented with various overall and position specific propensities of amino acids. Results: We characterize the structural distortions in five α -helix subclasses. The sequence structure correlation in the subclasses reveals that the increased propensity of proline, histidine, serine, aspartic acid and aromatic amino acids are responsible for the distortions in regular α -helix. The N-terminus of regular α -helix prefers neutral and acidic polar amino acids, while the C-terminus prefers basic polar amino acid. Proline is preferred in the first turn of regular α -helix , while it is preferred to produce kinked and curved subclasses. The SVM discriminates between regular α -helix and the rest with precision of 80.97% and recall of 88.05%. Conclusions: The correlation between structural variation in helices and their sequences is manifested by the performance of SVM based on sequence features. The results presented here are useful for computational design of helices. The results are also useful for prediction of structural perturbations in helix sequence purely based on its sequence.