Fig. 3: Development of computational models to predict polyreactivity.

Supervised models were trained on pools of high and low polyreactivity sequences. a Pipeline of computational model development from raw NGS data to held-out predictions with sequence clustering for rigorous validation. b Comparison of supervised models (one-hot and k-mer logistic regression, RNN, CNN) and biochemical properties such as hydrophobicity, isoelectric point, CDR3 lengths, and number of arginine residues. c Trained parameters of a one-hot logistic regression model, showing which amino acids at specific positions are most predictive of high polyreactivity and low polyreactivity (red, negative score and blue, positive score, respectively). d Polyreactivity scores of top motifs learned from a k-mer logistic regression model that are most predictive of low and high polyreactivity (top and bottom, respectively). e Separation of high and low polyreactivity nanobodies by each of the models and biochemical properties displayed in panel b.