In this paper, we use a constraint-based local search to produce state-of-the-art results for PCLF problem and thus show the effectiveness of cubic and FCC cubic lattices.
Now, given the native structure of a protein in full atomic representation and the backbone of the given native structure B = [b.sub.1], ..., [b.sub.n], in PCLF problem, the task is to find a structure in the lattice, C = [p.sub.1], ..., [p.sub.n] such that the distance between B and C is minimized.
The optimization for PCLF starts with a chain growth initialization technique.
By minimizing dRMSD using our PCLF algorithm in Algorithm 3, we are able to generate sample structures that are within very close proximity of the native structures (experimental results show this).
(1) PCLF search: this algorithm is essentially the algorithm presented in Algorithm 3.
It is clearly visible that the PCLF search produces structures with lower dRSMD (in the plot near the v-axis) compared to other methods and thus covers the areas of the search spaces that are closer to the native structures.
In this paper, we propose a constraint-based local search framework that produces state-of-the-art results for the protein chain lattice fitting (PCLF) problem for real proteins.
Le fait que des representantes du Barreau du Haut-Canada se soient deplacees a Ottawa pour s'entretenir avec le RECLEF et les etudiants et etudiantes du PCLF en 2012 temoigne indeniablement de la bonne foi des personnes qui sont saisies de ce dossier.
L'Universite d'Ottawa et le PCLF pourraient vraisemblablement jouer un plus grand role pour supplementer la programmation offerte par l'AJEFO.