  This paper proposes small tree-width graph decomposition computational
protein design CFN instances defined according to the model [1] with protocol
defined by Simononcini et al [2] . The proteins used in the benchmark have been
selected in the PDB (not on their biological interest) to explore the
efficiency of global search method based on tree-width decomposition. The
instances are bigger than those previously proposed in the paper [2] with one
backbone relaxation and the aka Beta November 2016 Rosetta force-field [3]. The
benchmark includes 21 proteins selected with a low level of sequences identity
(40%) . Those instances have been selected on the basis of 3D criteria by
applying a decreasing average coarse volume occupancy filter by Amino Acid
(-i.e. by CFN variable) . The instances characteristic (see Table 1) contain
from 130 up to n = 282 variables with a maximum domain size from 383 to 438,
and between 1706 and 6208 cost functions. The min-fill tree-width ranges from
21 to 68, and from 0.16 to 0.34 for a normalized tree width. Those instances
have been used for UDGVNS search algorithm[4] benchmarking. This approach is
suitable for evaluation of search methods that exploit the notion of graph
decomposition.
