Antibody apparent solubility prediction from sequence by transfer learning

Abstract

Developing therapeutic monoclonal antibodies (mAbs) for the subcutaneous administration requires identifying mAbs with superior solubility that are amenable for high-concentration formulation. However, experimental screening is often material and labor intensive. Here, we present a strategy (named solPredict) that employs the embeddings from pretrained protein language modeling to predict the apparent solubility of mAbs in histidine (pH 6.0) buffer. A dataset of 220 diverse, in-house mAbs were used for model training and hyperparameter tuning through 5-fold cross validation. solPredict achieves high correlation with experimental solubility on an independent test set of 40 mAbs. Importantly, solPredict performs well for both IgG1 and IgG4 subclasses despite the distinct solubility behaviors. This approach eliminates the need of 3D structure modeling of mAbs, descriptor computation, and expert-crafted input features. The minimal computational expense of solPredict enables rapid, large-scale, and high-throughput screening of mAbs using sequence information alone during early antibody discovery.