B cells undergo somatic hypermutation (SHM) of the Immunoglobulin (Ig) variable region to generate high-affinity antibodies. SHM relies on the activity of activation-induced deaminase (AID), which mutates C>U preferentially targeting WRC (W=A/T, R=A/G) hotspots. Downstream mutations at WA Polymerase h hotspots contribute further mutations. Computational models of SHM can describe the probability of mutations essential for vaccine responses. Previous studies using short subsequences (k-mers) failed to explain divergent mutability for the same k-mer. We developed the DeepSHM (Deep learning on SHM) model using k-mers of size 5-21, improving accuracy over previous models. Interpretation of DeepSHM identified an extended WWRCT motif with particularly high mutability. Increased mutability was further associated with lower surrounding G content. Our model also discovered a conserved AGYCTGGGGG (Y=C/T) motif within FW1 of IGHV3 family genes with unusually high T>G substitution rates. Thus, a wider sequence context increases predictive power and identifies features that drive mutational targeting.

The somatic hypermutation (SHM) of Immunoglobulin (Ig) genes is a key process during antibody affinity maturation in B cells. The mutagenic enzyme activation induced deaminase (AID) is required for SHM and has a preference for WRC hotspots in DNA. Error-prone repair mechanisms acting downstream of AID introduce further mutations, including DNA polymerase eta (Polη), part of the non-canonical mismatch repair pathway (ncMMR), which preferentially generates mutations at WA hotspots. Previously proposed mechanistic models lead to a variety of predictions concerning interactions between hotspots, for example, how mutations in one hotspot will affect another hotspot. Using a large, high-quality, Ig repertoire sequencing dataset, we evaluated pairwise correlations between mutations site-by-site using an unbiased measure similar to mutual information which we termed “mutational association” (MA). Interactions are dominated by relatively strong correlations between nearby sites (short-range MAs), which can be almost entirely explained by interactions between overlapping hotspots for AID and/or Polη. We also found relatively weak dependencies between almost all sites throughout each gene (longer-range MAs), although these arise mostly as a statistical consequence of high pairwise mutation frequencies. The dominant short-range interactions are also highest within the most highly mutating IGHV sub-regions, such as the complementarity determining regions (CDRs), where there is a high hotspot density. Our results suggest that the hotspot preferences for AID and Polη have themselves evolved to allow for greater interactions between AID and/or Polη induced mutations.

B-cells undergo somatic hypermutation (SHM) of the Immunoglobulin (Ig) variable region to generate high-affinity antibodies. SHM relies on the activity of activation-induced deaminase (AID), which mutates C>U preferentially targeting WRC (W=A/T, R=A/G) hotspots. Downstream mutations at WA Polymerase η hotspots contribute further mutations. Computational models of SHM can describe the probability of mutations essential for vaccine responses. Previous studies using short subsequences (k-mers) failed to explain divergent mutability for the same k-mer. We developed the DeepSHM (Deep learning on SHM) model using k-mers of size 5-21, improving accuracy over previous models. Interpretation of DeepSHM identified an extended DWRCT (D=A/G/T) motif with particularly high mutability. Increased mutability was further associated with lower surrounding G content. Our model also discovered a conserved AGYCTGGGGG (Y=C/T) motif within FW1 of IGHV3 family genes with unusually high T>G substitution rates. Thus, a wider sequence context increases predictive power and identifies novel features that drive mutational targeting.