Improved protein loop prediction from sequence alone

• Published in: Protein engineering Vol. 14; no. 7; pp. 473 - 478
• Main Authors: Burke, David F., Deane, Charlotte M.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-07-2001; Oxford Publishing Limited (England)
• Subjects: Algorithms; Amino Acid Motifs; Amino Acid Sequence; Databases, Factual; Hydrogen Bonding; Internet; loop prediction; Molecular Sequence Data; Peptide Fragments - chemistry; Peptides - chemistry; Protein Conformation; Proteins - chemistry; Proteins - genetics; Sequence Homology, Amino Acid; SLoop; substitution tables; protein conformation; database; SLoop; loop prediction; substitution tables
• ISSN: 0269-2139; 1741-0126; 1460-213X; 1741-0134
• DOI: 10.1093/protein/14.7.473

The SLoop database of supersecondary fragments, first described by Donate et al. (Protein Sci., 1996, 5, 2600–2616), contains protein loops, classified according to structural similarity. The database has recently been updated and currently contains over 10 000 loops up to 20 residues in length, which cluster into over 560 well populated classes. The database can be found at http://www-cryst.bioc.cam.ac.uk/~sloop. In this paper, we identify conserved structural features such as main chain conformation and hydrogen bonding. Using the original approach of Rufino and co-workers (1997), the correct structural class is predicted with the highest SLoop score for 35% of loops. This rises to 65% by considering the three highest scoring class predictions and to 75% in the top five scoring class predictions. Inclusion of residues from the neighbouring secondary structures and use of substitution tables derived using a reduced definition of secondary structure increase these prediction accuracies to 58, 78 and 85%, respectively. This suggests that capping residues can stabilize the loop conformation as well as that of the secondary structure. Further increases are achieved if only well-populated classes are considered in the prediction. These results correspond to an average loop root mean square deviation of between 0.4 and 2.6 Å for loops up to five residues in length.