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The isoelectric point (IEP or pI) is the pH where the net charge on themolecular ensemble of peptides and proteins is zero. This physical-chemicalproperty is dependent on protonable/deprotonable sidechains and their pKavalues. Here an pI prediction model is trained from a database of peptidesequences and pIs using a recurrent neural network (RNN) with long short-termmemory (LSTM) cells. The trained model obtains an RMSE and R$^2$ of 0.28 and0.95 for the external test set. The model is not based on pKa values, butprediction of constructed test sequences show similar rankings as already knownpKa values. The prediction depends mostly on the existence of known acidic andbasic amino acids with fine-adjusted based on the neighboring sequence andposition of the charged amino acids in the peptide chain.

DeepIEP: a Peptide Sequence Model of Isoelectric Point (IEP/pI) using Recurrent Neural Networks (RNNs)