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Abstract
Motivation: The coiled coil is a ubiquitous alpha-helical protein-structure domain that directs and facilitates protein-protein interactions in a wide variety of biological processes. At the protein-sequence level, the coiled coil is readily recognized via a conspicuous heptad repeat of hydrophobic and polar residues. However, structurally coiled coils are more complicated, existing in a wide range of oligomer states and topologies. As a consequence, predicting these various states from sequence remains an unmet challenge.
Results: This work introduces LOGICOIL, the first algorithm to address the problem of predicting multiple coiled-coil oligomeric states from protein-sequence information alone. By covering > 90% of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of similar to 31% of this population. This leap in predictive power offers better opportunities for genome-scale analysis, and analyses of coiled-coil containing protein assemblies.
Original language | English |
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Pages (from-to) | 69-76 |
Number of pages | 8 |
Journal | Bioinformatics |
Volume | 29 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2013 |
Keywords
- CLASSIFICATION PROBLEMS
- PACKING
- GENE SELECTION
- PROGRAM
- PROTEIN STRUCTURES
- CANCER CLASSIFICATION
- MULTINOMIAL REGRESSION
- BURIED POLAR RESIDUES
- GCN4 LEUCINE-ZIPPER
- BAYESIAN VARIABLE SELECTION
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