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

TL;DR

DeepIEP employs an LSTM-based RNN to accurately predict peptide isoelectric points from sequences, capturing sequence-dependent charge interactions without relying solely on pKa values.

Contribution

This study introduces a novel RNN model for peptide IEP prediction that outperforms traditional pKa-based methods and considers sequence context.

Findings

RMSE of 0.28 on test set

R² of 0.95 indicating high accuracy

Prediction aligns with known amino acid pKa rankings

Abstract

The isoelectric point (IEP or pI) is the pH where the net charge on the molecular ensemble of peptides and proteins is zero. This physical-chemical property is dependent on protonable/deprotonable sidechains and their pKa values. Here an pI prediction model is trained from a database of peptide sequences and pIs using a recurrent neural network (RNN) with long short-term memory (LSTM) cells. The trained model obtains an RMSE and R$^2$ of 0.28 and 0.95 for the external test set. The model is not based on pKa values, but prediction of constructed test sequences show similar rankings as already known pKa values. The prediction depends mostly on the existence of known acidic and basic amino acids with fine-adjusted based on the neighboring sequence and position of the charged amino acids in the peptide chain.