Deep Learning Approaches for the Protein Scaffold Filling Problem

We are on the verge of a post-genomics era in which whole protein sequencing will be quickly carried out. Protein se-quencing plays an important role in identifying protein functions, analyzing protein-protein interactions, and characterizing post-translational modifications, etc. The protein sequencing problem is to determine the complete sequence of amino acids in proteins. De novo protein sequencing using top-down and bottom-up tandem mass spectrometry suffers from the problem of producing only partial sequences of target proteins, namely scaffold. In this paper, we explore the possibility of using deep learning techniques to perform the task of predicting amino acids in partially sequenced proteins by two phases. First, our methods involve querying the NCBI Protein Blast server to find closest matching homologous sequences to a scaffold as a training dataset. Second, we train several deep learning models based on a convolutional neural network and long short term memory to predict missing amino acids in the scaffold in the forward and reverse directions. We comprehensively evaluate our proposed methods on an alemtuzumab dataset and our results show that the proposed methods achieve high sequence coverage and high sequence accuracy with 100 % on the the light chain of alemtuzumab scaffold data.