Improved prediction for N-termini of alpha-helices using empirical information.
The prediction of the secondary structure of proteins from their amino acid sequences remains a key component of many approaches to the protein folding problem. The most abundant form of regular secondary structure in proteins is the alpha-helix, in which specific residue preferences exist at the N-terminal locations. Propensities derived from these observed amino acid frequencies in the Protein Data Bank (PDB) database correlate well with experimental free energies measured for residues at different N-terminal positions in alanine-based peptides. We report a novel method to exploit this data to improve protein secondary structure prediction through identification of the correct N-terminal sequences in alpha-helices, based on existing popular methods for secondary structure prediction. With this algorithm, the number of correctly predicted alpha-helix start positions was improved from 30% to 38%, while the overall prediction accuracy (Q3) remained the same, using cross-validated testing. Although the algorithm was developed and tested on multiple sequence alignment-based secondary structure predictions, it was also able to improve the predictions of start locations by methods that use single sequences to make their predictions. Furthermore, the residue frequencies at N-terminal positions of the improved predictions better reflect those seen at the N-terminal positions of alpha-helices in proteins. This has implications for areas such as comparative modeling, where a more accurate prediction of the N-terminal regions of alpha-helices should benefit attempts to model adjacent loop regions. The algorithm is available as a Web tool, located at http://rocky.bms.umist.ac.uk/elephant.
Wilson
Claire L
CL
Department of Biomolecular Sciences, University of Manchester Institute of Science and Technology, Manchester, United Kingdom.
Boardman
Paul E
PE
Doig
Andrew J
AJ
Hubbard
Simon J
SJ
D030562:Databases, Protein; D036262:Empirical Research; D010446:Peptide Fragments / Q000737:chemistry; D010455:Peptides / Q000737:chemistry*; D011237:Predictive Value of Tests; D017434:Protein Structure, Tertiary; D012107:Research Design; D016415:Sequence Alignment / Q000379:methods; D012984:Software; D012986:Software Validation
D010446:Peptide Fragments; D010455:Peptides