MOTIVATION: The coiled coil is a ubiquitous α-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 ∼31% of this population. This leap in predictive power offers better opportunities for genome-scale analysis, and analyses of coiled-coil containing protein assemblies. AVAILABILITY: LOGICOIL is available via a web-interface at http://coiledcoils.chm.bris.ac.uk/LOGICOIL. Source code, training sets and supporting information can be downloaded from the same site.
MOTIVATION: The coiled coil is a ubiquitous α-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 ∼31% of this population. This leap in predictive power offers better opportunities for genome-scale analysis, and analyses of coiled-coil containing protein assemblies. AVAILABILITY: LOGICOIL is available via a web-interface at http://coiledcoils.chm.bris.ac.uk/LOGICOIL. Source code, training sets and supporting information can be downloaded from the same site.
Authors: Jaclyn LoPiccolo; Seung Joong Kim; Yi Shi; Bin Wu; Haiyan Wu; Brian T Chait; Robert H Singer; Andrej Sali; Michael Brenowitz; Anne R Bresnick; Jonathan M Backer Journal: J Biol Chem Date: 2015-10-16 Impact factor: 5.157
Authors: Kimberlee L Stern; Mason S Smith; Wendy M Billings; Taylor J Loftus; Benjamin M Conover; Dennis Della Corte; Joshua L Price Journal: Biochemistry Date: 2020-04-20 Impact factor: 3.162
Authors: Aaron A Vogan; S Lorena Ament-Velásquez; Alexandra Granger-Farbos; Jesper Svedberg; Eric Bastiaans; Alfons Jm Debets; Virginie Coustou; Hélène Yvanne; Corinne Clavé; Sven J Saupe; Hanna Johannesson Journal: Elife Date: 2019-07-26 Impact factor: 8.140
Authors: Shannon E Hill; Elaine Nguyen; Rebecca K Donegan; Athéna C Patterson-Orazem; Anthony Hazel; James C Gumbart; Raquel L Lieberman Journal: Structure Date: 2017-11-07 Impact factor: 5.006