Abstract
Background: The polyproline II helix (PPIIH) is an extended protein left-handed secondary structure that usually but not necessarily involves prolines. Short PPIIHs are frequently, but not exclusively, found in disordered protein regions, where they may interact with peptide-binding domains. However, no readily usable software is available to predict this state. Results: We developed PPIIPRED to predict polyproline II helix secondary structure from protein sequences, using bidirectional recurrent neural networks trained on known three-dimensional structures with dihedral angle filtering. The performance of the method was evaluated in an external validation set. In addition to proline, PPIIPRED favours amino acids whose side chains extend from the backbone (Leu, Met, Lys, Arg, Glu, Gln), as well as Ala and Val. Utility for individual residue predictions is restricted by the rarity of the PPIIH feature compared to structurally common features. Conclusion: The software, available at http://bioware.ucd.ie/PPIIPRED, is useful in large-scale studies, such as evolutionary analyses of PPIIH, or computationally reducing large datasets of candidate binding peptides for further experimental validation.
Highlights
Polyproline II helices (PPIIHs) are an important class of secondary structure which makes up approximately 2% of the protein structure database (PDB) and are enriched in protein binding regions [1,2]
Since the rarity of PPII residues results in a typical excess of false positives over true positives at many predictor thresholds, we investigated plots of sensitivity TPs royalsocietypublishing.org/journal/rsos R
Residues with a PPIIPRED score of greater than 0.2 account for 37% of true positives and 1.3% of false positives. This translates to 1828 true positive residues and 2778 false positives
Summary
Polyproline II helices (PPIIHs) are an important class of secondary structure which makes up approximately 2% of the protein structure database (PDB) and are enriched in protein binding regions [1,2]. PPIIH conformations are adopted by peptides when binding to SH3, WW, EVH1, GYF, UEV and profilin domains [3,4]. They play roles in a wide variety of contexts [5]. The polyproline II helix (PPIIH) is an extended protein left-handed secondary structure that usually but not necessarily involves prolines. Short PPIIHs are frequently, but not exclusively, found in disordered protein regions, where they may interact with peptide-binding domains. Results: We developed PPIIPRED to predict polyproline II helix secondary structure from protein sequences, using bidirectional recurrent neural networks trained on known three-dimensional structures with dihedral angle filtering. Conclusion: The software, available at http://bioware. ucd.ie/PPIIPRED, is useful in large-scale studies, such as evolutionary analyses of PPIIH, or computationally reducing large datasets of candidate binding peptides for further experimental validation
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