Protein Structure Assembly from Knowledge of β -Sheet Motifs and Secondary Structure

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 βsheet 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.