Cysteines in a protein have a tendency to form mutual disulfide bonds. This affects the secondary and tertiary structure of the protein. Therefore, automatic prediction of the bonding state of cysteines from the primary structure of proteins has long been a relevant task in bioinformatics. The paper investigates the feasibility of a predictor based on a hybrid approach that combines the dynamic encoding capabilities of a recurrent autoencoder with the short-term/long-term dependencies modeling capabilities of a dynamic probabilistic graphical model (a dynamic extension of the hybrid random field). Results obtained using 1797 proteins from the May 2010 version of the Protein Data Bank show an average accuracy of 85% by relying only on the sub-sequences of the residue chains with no additional attributes (like global descriptors, or evolutionary information provided by multiple alignment).

Bongini, M., Laveglia, V., & Trentin, E. (2016). A Hybrid Recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins. In ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION (pp.257-268). Cham : Springer [10.1007/978-3-319-46182-3_22].

A Hybrid Recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins

Trentin, E.
2016

Abstract

Cysteines in a protein have a tendency to form mutual disulfide bonds. This affects the secondary and tertiary structure of the protein. Therefore, automatic prediction of the bonding state of cysteines from the primary structure of proteins has long been a relevant task in bioinformatics. The paper investigates the feasibility of a predictor based on a hybrid approach that combines the dynamic encoding capabilities of a recurrent autoencoder with the short-term/long-term dependencies modeling capabilities of a dynamic probabilistic graphical model (a dynamic extension of the hybrid random field). Results obtained using 1797 proteins from the May 2010 version of the Protein Data Bank show an average accuracy of 85% by relying only on the sub-sequences of the residue chains with no additional attributes (like global descriptors, or evolutionary information provided by multiple alignment).
978-3-319-46181-6
978-3-319-46182-3
Bongini, M., Laveglia, V., & Trentin, E. (2016). A Hybrid Recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins. In ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION (pp.257-268). Cham : Springer [10.1007/978-3-319-46182-3_22].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/1007284