Comprehensive Analysis of Constraint on the Spatial Distribution of Missense Variants in Human Protein Structures

Abstract

The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and provides insight into the functional importance of protein regions and the potential pathogenicity of protein alterations. Here, we comprehensively evaluate the 3D spatial patterns of human germline and somatic variation in 6,604 experimentally derived protein structures and 33,144 computationally derived homology models covering 77% of all human proteins. Using a systematic approach, we quantify differences in the spatial distributions of neutral germline variants, disease-causing germline variants, and recurrent somatic variants. Neutral missense variants exhibit a general trend toward spatial dispersion, which is driven by constraint on core residues. In contrast, germline disease-causing variants are generally clustered in protein structures and form clusters more frequently than recurrent somatic variants identified from tumor sequencing. In total, we identify 215 proteins with significant spatial constraints on the distribution of disease-causing missense variants in experimentally derived protein structures, only 65 (30%) of which have been previously reported. This analysis identifies many clusters not detectable from sequence information alone; only 12% of proteins with significant clustering in 3D were identified from similar analyses of linear protein sequence. Furthermore, spatial analyses of mutations in homology-based structural models are highly correlated with those from experimentally derived structures, supporting the use of computationally derived models. Our approach highlights significant differences in the spatial constraints on different classes of mutations in protein structure and identifies regions of potential function within individual proteins.

Using structural analysis in silico to assess the impact of missense variants in MEN1

“…Our data also suggests the possible existence of an as-yet unidentified interaction of Menin, as evidenced by the cluster of pathogenic variants lying on the protein surface opposite the JunD binding pocket. Notably, MEN1 has recently been identified as one of the genes exhibiting significant spatial clustering of pathogenic variants (47); our analysis suggests that this clustering is likely to apply both to regions of structural importance, which are buried in the interior of the protein, and to surface regions which form essential interactions with binding partners.…”

Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1

“…This improvement in classification ability for LOF variants in SCN5A when adding functional density for peak current (0.69 without vs. 0.78 with, p = .01) suggests structure-based features contribute information not contained in other predictive features (Fig. S8) an observation gaining appreciation elsewhere [ [42] , [43] ].…”

Integrating structural and evolutionary data to interpret variation and pathogenicity in adapter protein complex 4

“…Our previous work 34,35 has shown that evaluating the Euclidean distance in 3D space of uncharacterized variants relative to pathogenic and benign variants can aid in variant prioritization. Sivley et al 34,35 developed a program called PathProx that evaluates the relative 3D proximity of a variant to known pathogenic and benign variants.…”

“…Our previous work 34,35 has shown that evaluating the Euclidean distance in 3D space of uncharacterized variants relative to pathogenic and benign variants can aid in variant prioritization. Sivley et al 34,35 developed a program called PathProx that evaluates the relative 3D proximity of a variant to known pathogenic and benign variants. To explore the spatial distribution of variants in AP‐4, we mapped onto the AP‐4 homology model four reported pathogenic variants (curated from the literature) 13–16,36 and six variants annotated as likely pathogenic in ClinVar (Table 3).…”

“…We quantified the spatial proximity of each residue in the AP‐4 model to annotated pathogenic variants and genetic variants observed in individuals without severe disease using the previously described PathProx score 34,35 . The data set of reported pathogenic variants was derived from clinical cases and variants annotated from ClinVar as either pathogenic or likely pathogenic 32 .…”