Manisha asked > It is probably widely accepted that the center of the helix or stand > is predicted more accurately than the boundaries of these elements > (start and end positions). A number of studies focusing on improving > the prediction of alpha-helical ends have been reported but I havn't > come across any similar work for the prediction of Beta-strand > ends. Is it because Beta-stand ends are already predicted with much > higher accuracy than the helix ends? I havn't been able to find > reference to any such comparison or observation either. There are at least two causes for the difficulty in predicting the ends of alpha helices: 1) the helix/non-helix decision is a bit arbitrary at the ends of helices (witness the differences in helix labels from DSSP and Stride) 2) different homologs may end the helices in different places, and most of the prediction methods are basing the predictions on a multiple alignment of similar sequences. If the sequences don't have identical structures in some position, the prediction is going to have to be flawed for some of them. Beta strands have a bit less ambiguity in definition (though still some, particularly in low-quality models), so that source of error is reduced. Beta strands also tend to be short than helices, so the end points are a greater fraction of the training set, making them less subject to being overwhelmed in the training. Still, I certainly see lower confidence at the edges of beta strands than in the middles of them---particularly for amphipathic anti-parallel strands. Although I have worked on improving the accuracy of my local-structure predictors in various ways, I have not found it valuable to focus on the ends of helices and strands. Instead I have increased the alphabet size to get a finer division of local structures. For example, the "str" alphabet subdivides DSSP's beta strand letter into 6 letters: A anti-parallel middle strand, Z anti-parallel edge strand, P parallel middle strand, Q parallel edge strand, M mixed middle strand, E other (identified by DSSP as a strand, but not easily classified by the hbonds of itself and neighbors). The str2 alphabet further subdivides Z into Y and Z according to whether the residue or its neighbor has the hydrogen bonds. This finer subdivision of local structure has helped with fold recognition and alignment. One could use this approach by labeling the ends of helices or strands with different letters (perhaps like the I-sites classes for turns). I have not tried precisely that, because I am not convinced that the ends of helices and strands are well enough conserved to justify such a labeling. There are undoubtedly many such enriched alphabets that could be tried, and we have only looked at a few of them. Rachel Karchin and I developed a protocol for evaluating new local-structure alphabets, which seems to work fairly well: @string{prosfg= "Proteins: Structure, Function, and Genetics"} @article{karchin03-backbone-geometries, author = {Rachel Karchin and Melissa Cline and Yael Mandel-Gutfreund and Kevin Karplus}, title = {Hidden {Markov} models that use predicted local structure for fold recognition: alphabets of backbone geometry}, year = {2003}, month=jun, journal = prosfg, volume = {51}, number={4}, pages = {504--514} } @article{karchin-burial-alphabets, author = "Rachel Karchin and Melissa Cline and Kevin Karplus", title= "Evaluation of local structure alphabets based on residue burial", journal = prosfg, year = "2004", month = "5 "#mar, volume = "55", number="3", pages = "508-518", note = "Online: http://www3.interscience.wiley.com/cgi-bin/abstract/107632554/ABSTRACT" } ------------------------------------------------------------ Kevin Karplus karplus at soe.ucsc.edu http://www.soe.ucsc.edu/~karplus Professor of Biomolecular Engineering, University of California, Santa Cruz Undergraduate and Graduate Director, Bioinformatics (Senior member, IEEE) (Board of Directors, ISCB) life member (LAB, Adventure Cycling, American Youth Hostels) Effective Cycling Instructor #218-ck (lapsed) Affiliations for identification only.