Protein secondary structure prediction by the analysis of variation and conservation in multiple alignments.
A number of methods exist for the prediction of protein secondary structure from primary sequence. One method identifies variable charged and conserved hydrophobic residues within large multiple alignments as a means of indicating outside and inside sites respectively in the protein structure. These sites are then manually fitted to secondary structure templates to generate a secondary structure prediction. Using the existing theoretical bases of this method, we present an algorithm (STAMA) which automatically carries out the initial variation/conservation analysis of the alignment. We also test the accuracy of complete predictions carried out by manual fitting of the STAMA-derived assignments to structure templates, using five large multiple alignments each including a protein of known structure. The method was found on average to predict only 57% of residues in the correct secondary structure, and was only as accurate as predictions carried out using the established and automated method of Garnier, Osguthorpe and Robson (1978) applied to a single sequence. When used in conjunction with other secondary structure prediction methods, however, the resulting consensus predictions were found to be very accurate, with 78% of the elements (alpha helices or beta strands) for which a consensus could be obtained being predicted correctly. The algorithm presented here, plus the assessment of the accuracy of prediction generated by this method, should enable this predictive approach to receive informed general use.
Tuckwell
D S
DS
School of Biological Sciences, University of Manchester, UK. mbdxt@seqnet.dl.ac.uk
Humphries
M J
MJ
Brass
A
A
D000465:Algorithms; D004563:Electrochemistry; D005069:Evaluation Studies as Topic; D014644:Genetic Variation; D015394:Molecular Structure; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins