OCTOPUS Friday, July 21 2017
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Motivation: As alpha-helical transmembrane proteins constitute roughly 25% of a typical genome and are vital parts of many essential biological processes, structural knowledge of these proteins is necessary for increasing our understanding of such processes. Because structural knowledge of transmembrane proteins is difficult to attain experimentally, improved methods for prediction of structural features of these proteins is important.

Functionality: OCTOPUS uses a combination of hidden Markov models and artificial neural networks. In particular, OCTOPUS is the first topology predictor to integrate modeling of reentrant-, membrane dip-, and TM hairpin regions in the topological grammar.
OCTOPUS first performs a homology search using BLAST to create a sequence profile. This is used as the input to a set of neural networks that predict both the preference for each residue to be located in a transmembrane (M), interface (I), close loop (L) or globular loop (G) environment and the preference for each residue to be on the inside (i) or outside (o) if the membrane. In the third step, these predictions are used as input to a two-track hidden Markov model, which uses them to calculate the most likely topology.
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