We model loop regions of proteins based on a fragment assembly method. The fragments that comprise candidates of the local structure of a protein loop are collected from a structure database, and all loop conformations possible from a smooth assembly of the fragments are generated. For each of the fragment-assembled conformations, a Monte Carlo simulation in the conformational subspace that satisfies the loop closure constraint is performed to minimize the root-mean-square deviation of the backbone dihedral angles from the fragment angles. The side-chains are then built using a rotamer library, and the backbone and the side-chain conformations are optimized locally with the AMBER 96 force field without solvation terms to remove steric clashes. The resulting conformations are then ranked using the DFIRE potential. A test prediction for eight protein loops with sizes ranging from 8 to 12 residues is presented to show the feasibility of our method. Tests with further optimization using Monte Carlo with minimization show that extensive conformational optimization leading to deviation from the original fragment-assembled structures tend to deteriorate the prediction accuracy, suggesting that the utilization of fragment information is superior to purely energy-based methods.
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