Abstract for 'Title'

We still cannot predict protein 3D structure from sequence, in general. But bioinformatics continuously improve methods predicting simplified aspects of structure. Particularly, the field of secondary structure has achieved a break-through by combining algorithms from artificial intelligence with evolutionary information. PHD, the first third generation method surmounted the 'magic' line of predicting more than 70% of all residues correctly in one of three states (helix, strand, other). Furthermore, b -strands were predicted almost twice as often correct as by methods of the first and second generation. Finally, predicted segments look like those observed. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than four percentage points to its current height around 77%. Divergent evolutionary profiles not only contain enough information to substantially improve prediction accuracy, but even to correctly predict long stretches of identical residues observed in alternative secondary structure states depending on non local conditions. An example is a method automatically identifying structural switches, and thus finding a remarkable connection between predicted secondary structure and aspects of function. Due to their remarkable success, secondary structure predictions have become the working horse for numerous methods aiming at predicting protein structure and function. Moreover, performance can be improved even further by using these methods in an 'expert' rather than in an 'automatic' mode. Have we, now reached the limit of prediction accuracy? Time will tell.

Key words: genome sequence analysis, predicting globularity, protein domains, protein structure prediction, solvent accessibility, multiple alignments, trans-membrane helices.

Top - Index of papers - Paper in HTML - Abstract - Paper as PDF - Appendix - CUBIC