Extended Data Fig. 5: Additional Protein-Protein Interaction Attributions and Comparisons.

(a) Protein heterodimer binder RNN predictor, which was trained on computationally designed (dimerizing) pairs for positive data and randomly paired binders as negative data (see Methods for details). The RNN consists of a shared GRU layer, a dropout layer, and two fully-connected layers applied to the concatenated GRU output vectors. The final output (sigmoid activation) is treated as the Bind / No Bind classification probability. (b) Supplemental benchmark of Gradient Saliency, Integrated Gradients and DeepSHAP, using only the positive-valued importance scores. Left: Prediction KL-divergence of scrambled sequences compared to original test set sequences when either replacing all but the top X% most important amino acid residues with random samples (inclusion) or, conversely, when replacing the top X% nucleotides with random samples and keeping the remaining sequence fixed (occlusion). Right: Mean ddG Difference for the top 8 most important residues according to each method, measured across the test set, and HBNet Average Precision based on each method’s importance scores. (c) Supplemental comparison of different versions of the Scrambling Neural Network (see Methods for a full description of each version). Left: KL-divergence benchmark based on the predictor RNN. Right: Mean ddG Differences and HBNet Discovery Precisions. (d) Supplemental comparison of other methods that optimize similar objectives as the Scrambler (see Methods for a full description of each method). Left: KL-divergence benchmark based on the predictor RNN. Right: Mean ddG Differences and HBNet Discovery Precisions. (e) Supplemental comparison between Scrambling Neural Networks and Sufficient Input Subsets (SIS) with ‘hot-deck’ sampled masking (the number of samples used at each iteration is varied from 1 to 32; see Methods for details). Left: KL-divergence benchmark based on the predictor RNN, Mean ddG Differences and HBNet Discovery Precisions annotated on top of the bar chart. Right: Average number of predictor queries used to interpret a single input pattern (for the Scrambler, this is the amortized cost of training divided by the number of test patterns interpreted).