Abstract
We develop and test a new hierarchical approach for the prediction of protein structure. An algorithm is described to assemble the 3D fold of a protein starting from its secondary structure and β-sheet topology. Reconstruction is carried out by energy minimization of a reduced protein model, where β-partners are derived from appropriate distance constraints imposed by the knowledge of β-sheet motifs. Additional constraints are imposed in the (φ, ψ) torsion space from secondary structure knowledge. Experiments show how the proposed procedure proves to be a reliable and fast predictive approach for a large fraction of proteins of interest. Arrangements of β-sheets are predicted with special recursive neural networks architectures. We first present a unifying framework for description of a large class of contextual recursive models and then show how it is possible to solve the problem at some extent of success.
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Ceroni, A., Frasconi, P., Vullo, A. (2005). Protein Structure Assembly from Knowledge of β-Sheet Motifs and Secondary Structure. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_6
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DOI: https://doi.org/10.1007/1-4020-3432-6_6
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
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