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.  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.  The minimal computational expense of solPredict enables rapid, large-scale, and high-throughput screening of mAbs using sequence information alone during early antibody discovery.
solPredict
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.  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.  The minimal computational expense of solPredict enables rapid, large-scale, and high-throughput screening of mAbs using sequence information alone during early antibody discovery.
solPredict
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.  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.  The minimal computational expense of solPredict enables rapid, large-scale, and high-throughput screening of mAbs using sequence information alone during early antibody discovery.
solPredict