  How do we determine the mutational effects in exome sequencing data with
little or no statistical evidence? Can protein structural information fill in
the gap of not having enough statistical evidence? In this work, we answer the
two questions with the goal towards determining pathogenic effects of rare
variants in rare disease. We take the approach of determining the importance of
point mutation loci focusing on protein structure features. The proposed
structure-based features contain information about geometric, physicochemical,
and functional information of mutation loci and those of structural neighbors
of the loci. The performance of the structure-based features trained on 80\% of
HumDiv and tested on 20\% of HumDiv and on ClinVar datasets showed high levels
of discernibility in the mutation's pathogenic or benign effects: F score of
0.71 and 0.68 respectively using multi-layer perceptron. Combining structure-
and sequence-based feature further improve the accuracy: F score of 0.86
(HumDiv) and 0.75 (ClinVar). Also, careful examination of the rare variants in
rare diseases cases showed that structure-based features are important in
discerning importance of variant loci.
