Prediction of single-stranded DNA binding proteins with protein language model

Single-stranded DNA binding proteins (SSBs) represent one important type of nucleic acid binding proteins and are crucial for many cellular functions, such as DNA replication, transcriptional regulation, and maintaining genome stability and integrity. Compared to other types of nucleic acid binding proteins, SSBs are underexplored due to a relatively small number of annotated SSBs and experimentally solved SSB structures. Only a few computational approaches have been developed so far to classify SSBs from double-stranded DNA binding proteins (DSBs), which typically assume the inputs are known DNA binding proteins (DBPs). However, our recent study reveals that a large number of SSBs are predicted as RNA binding proteins (RBPs) for any given nucleic acid binding proteins, suggesting that SSB/DSB classification alone is not enough and SSB/RBP classification is equally important in accurately predicting novel SSBs from proteins with unknown functions. In this study, in addition to a much improved SSB/DSB classifier, we developed the first SSB/RBP classifier with machine learning models. Besides the widely used PSSM (position specific scoring matrix), we tested a structural feature and a feature extracted from a protein language model ESM2 for training and testing. Our results show that ESM2 improves prediction accuracy dramatically, up to 95% for the SSB/DSB classifier and 92% for the SSB/RBP classifier respectively with a support vector machine (SVM) approach.