Decoy selection is the task of automatically extracting near-native structures from an ensemble of low-energy structures generated in silico by a template-free method. Current research shows that discriminating by energy misses near-native structures and allows the inclusion of too many non-native structures. The predominant strategy is to ignore energy and cluster structures by their similarity, offering the top-populated clusters as prediction. In this paper we show that energy can improve accuracy in decoy selection when its inclusion is carried out under the energy landscape view. Specifically, we identify basins in the energy landscape and demonstrate basin selection schemes to outperform clustering. The results are promising and point to further directions of research for improving decoy selection and decoy generation.
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