  Identifying one or more biologically-active/native decoys from millions of
non-native decoys is one of the major challenges in computational structural
biology. The extreme lack of balance in positive and negative samples (native
and non-native decoys) in a decoy set makes the problem even more complicated.
Consensus methods show varied success in handling the challenge of decoy
selection despite some issues associated with clustering large decoy sets and
decoy sets that do not show much structural similarity. Recent investigations
into energy landscape-based decoy selection approaches show promises. However,
lack of generalization over varied test cases remains a bottleneck for these
methods. We propose a novel decoy selection method, ML-Select, a machine
learning framework that exploits the energy landscape associated with the
structure space probed through a template-free decoy generation. The proposed
method outperforms both clustering and energy ranking-based methods, all the
while consistently offering better performance on varied test-cases. Moreover,
ML-Select shows promising results even for the decoy sets consisting of mostly
low-quality decoys. ML-Select is a useful method for decoy selection. This work
suggests further research in finding more effective ways to adopt machine
learning frameworks in achieving robust performance for decoy selection in
template-free protein structure prediction.
