Extended Data Fig. 3: Machine learning models to predict PAM profile from amino acid sequence.

a, Comparison of machine learning model architectures (linear regression, random forest, and neural network) and amino acid encodings (one-hot, one-hot plus all pairwise amino acid combinations, and Georgiev⁴⁷). The R² value is shown between the experimentally determined k (via HT-PAMDA) and the predicted k (via each ML model) for an internal 5-fold cross-validation on the training set. Each validation set is sub-divided according to the minimum hamming distance (HD) of each variant to the nearest neighbor in the corresponding training set; thus, validation sets become more challenging as HD increases. b, Performance of the optimal PAM machine learning algorithm (PAMmla; comprised of a neural network with one hot encoding) on two additional 80%/20% random train-test splits. c, Proportion of test set SpCas9 enzymes that have a predominant preference for A, C, G, T in the 3^rd position of the PAM, or are inactive (based on HT-PAMDA data). d, Comparison of test set ks broken down by nucleotide preference of each test variant at the 3^rd position of the PAM (comparing ks experimentally determined by HT-PAMDA versus predicted by PAMmla). Nucleotide preference is defined as the 3^rd position nucleotide of each enzyme variant’s most preferred PAM by HT-PAMDA. e, Proportion of test set SpCas9 enzymes that have a predominant preference for A, C, G, T in the 4^th position of the PAM, or are inactive (based on HT-PAMDA data). f, Comparison of test set ks broken down by preference of each test set variant at the 4^th position of the PAM (comparing ks experimentally determined by HT-PAMDA versus predicted by PAMmla). Nucleotide preference is defined as 4^th position nucleotide of each enzyme variant’s most preferred PAM by HT-PAMDA. g, Effect of random over-sampling by most active PAM. The PAMmla model was trained with and without randomly over-sampling the training set to balance the number of enzyme variants with different PAM preferences. R² values for the two models were compared on subsets of variants within the test set with different preferences at the 3^rd and 4^th positions of the PAM. Over-sampling improved performance particularly for under-represented PAM classes (see panels c and e). h, Pearson’s correlations between HT-PAMDA replicates performed with distinct spacer sequences for a set of 28 inactive versus 28 active enzymes within the test set. Dashed line = data median. True labels for active versus inactive enzymes were determined using a cutoff value for maximum k on any PAM of 10^−4.3. Enzymes separated into active and inactive classes based on these criteria showed correlation between replicates only for active enzymes, indicating HT-PAMDA data for enzymes with maximum ks below this cutoff are likely due to non-reproducible noise in the HT-PAMDA assay. i, Correlation between ks experimentally determined by HT-PAMDA versus predicted by PAMmla for inactive variants (maximum HT-PAMDA k < 10^−4.3) within the test set; PAMmla is not predictive for background noise in the HT-PAMDA determined PAM profiles of inactive enzymes. For all panels that utilize HT-PAMDA data, the log₁₀ rate constants (k) are the mean of n = 2 replicate HT-PAMDA experiments using two distinct spacer sequences. For all scatterplots, each datapoint represents the rate constant activity of one enzyme variant against on one of 64 possible NNNN PAMs.