Repeated local ellipsoid protrusion supplements HLA surface characterization

Abstract

Allorecognition of donor HLA is a major risk factor for long-term kidney graft survival. Although several molecular matching algorithms have been proposed that compare physiochemical and structural features of the donors' and recipients' HLA proteins in order to predict their compatibility, the exact underlying mechanisms are still not fully understood. We hypothesized that the ElliPro approach of single ellipsoid fitting and protrusion ranking lacks sensitivity for the characteristic shape of HLA molecules and developed a prediction pipeline named Snowball that is fitting smaller ellipsoids iteratively to substructures. Aggregated protrusion ranks of locally fitted ellipsoids were calculated for 712 publicly available HLA structures and 78 predicted structures using AlphaFold 2. Amino-acid sequence and protrusion ranks were used to train deep neural network predictors to infer protrusion ranks for all known HLA sequences. Snowball protrusion ranks appear to be more sensitive than ElliPro scores in fine parts of the HLA such as the helix structures forming the HLA binding groove in particular when the ellipsoids are fitted to substructures considering atoms within a 15 Å radius. A cloud-based web service was implemented based on amino-acid matching considering both protein- and position-specific surface area and protrusion ranks extending the previously presented Snowflake prediction pipeline.