Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Approximation algorithms for protein folding prediction
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Bioinformatics and Constraints
Constraints
Refining Neural Network Predictions for Helical Transmembrane Proteins by Dynamic Programming
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
A Grammar-Based Unification of Several Alignment and Folding Algorithms
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
Towards 3D modeling of interacting TM helix pairs based on classification of helix pair sequence
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Efficient traversal of beta-sheet protein folding pathways using ensemble models
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Annotated stochastic context free grammars for analysis and synthesis of proteins
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
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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.