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This paper proposes small tree-width graph decomposition computationalprotein design CFN instances defined according to the model [1] with protocoldefined by Simononcini et al [2] . The proteins used in the benchmark have beenselected in the PDB (not on their biological interest) to explore theefficiency of global search method based on tree-width decomposition. Theinstances are bigger than those previously proposed in the paper [2] with onebackbone relaxation and the aka Beta November 2016 Rosetta force-field [3]. Thebenchmark includes 21 proteins selected with a low level of sequences identity(40%) . Those instances have been selected on the basis of 3D criteria byapplying a decreasing average coarse volume occupancy filter by Amino Acid(-i.e. by CFN variable) . The instances characteristic (see Table 1) containfrom 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 from21 to 68, and from 0.16 to 0.34 for a normalized tree width. Those instanceshave been used for UDGVNS search algorithm[4] benchmarking. This approach issuitable for evaluation of search methods that exploit the notion of graphdecomposition.

Computation Protein Design instances with small tree-width: selection based on coarse approximated 3D average position volume