PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms

Accurate identification of ligand
binding sites (LBS) on a protein
structure is critical for understanding protein function and designing
structure-based drugs. As the previous pocket-centric methods are
usually based on the investigation of pseudo-surface-points outside
the protein structure, they cannot fully take advantage of the local
connectivity of atoms within the protein, as well as the global 3D
geometrical information from all the protein atoms. In this paper,
we propose a novel point clouds segmentation method, PointSite, for
accurate identification of protein ligand binding atoms, which performs
protein LBS identification at the atom-level in a protein-centric
manner. Specifically, we first transfer the original 3D protein structure
to point clouds and then conduct segmentation through Submanifold
Sparse Convolution based U-Net. With the fine-grained atom-level binding
atoms representation and enhanced feature learning, PointSite can
outperform previous methods in atom Intersection over Union (atom-IoU)
by a large margin. Furthermore, our segmented binding atoms, that
is, atoms with high probability predicted by our model can work as
a filter on predictions achieved by previous pocket-centric approaches,
which significantly decreases the false-positive of LBS candidates.
Besides, we further directly extend PointSite trained on bound proteins
for LBS identification on unbound proteins, which demonstrates the
superior generalization capacity of PointSite. Through cascaded filter
and reranking aided by the segmented atoms, state-of-the-art performance
can be achieved over various canonical benchmarks, CAMEO hard targets,
and unbound proteins in terms of the commonly used DCA criteria.