The “dark matter” of protein variants carries a distinct DNA signature and predicts damaging variant effects

Abstract

Signatures of DNA motifs associated with distinct mutagenic exposures have been defined for somatic variants, but little is known about the consequences different mutational processes pose to the cancer cell, particularly the distribution of the resulting variants in the implied proteins and their structural regions (surface, core, interacting interface). Here we first compare the protein-level consequences of six mutational signatures (Aging, APOBEC, POLE, UV, 5-FU and Platinum) characterised by clear DNA motif preferences. By mapping individual substitution events observed in tumours to three-dimensional protein structures, we show that these common somatic mutational signatures are biased against the protein core, consistent with the lower tolerability of substitutions at such structurally important regions. On the other hand, deep mutational scanning (DMS) data allow us to probe the "dark matter" of somatic mutational landscape, exploring variants which are otherwise removed in purifying selection. A computational DMS analysis identifies mutational contexts (5'-G/C[T>G]A/G-3') which are associated with damaging mutations, by altering physicochemical characteristics of amino acids at the protein core. We argue that comprehensive DMS analysis can contribute to classification of variants according to their true impact to the stability/activity of the affected protein, decoupling this from pathogenicity prediction offered by conventional variant impact classifiers.