Jérôme Waldispühl and Jean-Marc
Steyaert
Modeling and Predicting All-alpha Transmembrane Proteins Including Helix-helix Pairing
Modeling and predicting the structure of proteins is one of the most
important challenges of computational biology. Exact physical models
are too complex to provide feasible prediction tools and other ab
initio methods only use local and probabilistic information to fold a
given sequence. We show in this paper that all-α transmembrane
protein secondary and super-secondary structures can be modeled with a
multi-tape S-attributed grammar. An efficient structure prediction
algorithm using both local and global constraints is designed and
evaluated. Comparison with existing methods shows that the prediction
rates as well as the definition level are sensibly
increased. Furthermore this approach can be generalized to more
complex proteins.
Keywords:All-α transmembrane proteins; Multi-tape
S-attribute grammar; Folding modeling; Structure prediction;
Helix-helix pairing.
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