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Understanding E3 ligase and target substrate interactions are important forcell biology and therapeutic development. However, experimental identificationof E3 target relationships is not an easy task due to the labor-intensivenature of the experiments. In this article, a sequence-based E3-targetprediction model is proposed for the first time. The proposed frameworkutilizes composition of k-spaced amino acid pairs (CKSAAP) to learn therelationship between E3 ligases and their target protein. A class separablelatent space encoding scheme is also devised that provides a compressedrepresentation of feature space. A thorough ablation study is performed toidentify an optimal gap size for CKSAAP and the number of latent variables thatcan represent the E3-target relationship successfully. The proposed scheme isevaluated on an independent dataset for a variety of standard quantitativemeasures. In particular, it achieves an average accuracy of $70.63\%$ on anindependent dataset. The source code and datasets used in the study areavailable at the author's GitHub page(https://github.com/psychemistz/E3targetPred).

E3-targetPred: Prediction of E3-Target Proteins Using Deep Latent Space Encoding