  Modern algorithms for de novo prediction of protein structures typically
output multiple full-length models (decoys) rather than a single solution.
Subsequent clustering of such decoys is used both to gauge the success of the
modelling and to decide on the most native-like conformation. At the same time,
partial protein models are sufficient for some applications such as
crystallographic phasing by molecular replacement (MR) in particular, provided
these models represent a certain part of the target structure with reasonable
accuracy. Here we propose a novel clustering algorithm that natively operates
in the space of partial models through an approach known as granular clustering
(GC). The algorithm is based on growing local similarities found in a pool of
initial decoys. We demonstrate that the resulting clusters of partial models
provide a substantially more accurate structural detail on the target protein
than those obtained upon a global alignment of decoys. As the result, the
partial models output by our GC algorithm are also much more effective towards
the MR procedure, compared to the models produced by existing software. The
source code is freely available at https://github.com/biocryst/gc
