Detection of Cis-Trans Conformation in Protein Structure using Deep Learning Neural Network Techniques

Abstract

Proteins are important biological macromolecules that play critical roles in most biological processes. The functionality of protein depends on its three dimensional structure, which further depends on the protein's amino acid sequence. Direct prediction of 3D structure of protein from amino acid is challenging task. Therefore, prediction of three dimensional protein structure is divided into small sub-problems like one and two-dimensional properties of protein structure. The solution of these sub-problems can lead to successful three-dimensional structure prediction of protein. Accurate prediction of Cis ð€€€ Trans conformation in amino acid residues is one such sub-problem of protein structure prediction. It has been long established that cis conformations of amino acid residues play many biologically important roles and are implicated in cancer and neurodegenerative diseases, despite their exceptionally rare occurrence in protein structure (99.6% in trans). Due to this rarity, few methods have been developed for predicting cis-isomers from protein sequences, most of which are based on outdated datasets and lack the means for independent testing. This report presents several machine learning algorithm for the prediction of Cis ð€€€ Trans conformation of amino acid residues. In this research work, using a database of more than 10000 high-resolution protein structures, we update the statistics of cis-isomers available in literature and develop a sequence-based prediction technique using an ensemble of residual convolutional and Long Short-Term Memory bidirectional recurrent neural networks which allows for learning from the whole protein sequence. We show that ensembling 8 neural network models yields the maximum MCC value of approximately 0.35 for cis-Pro-isomers, and 0.1 for cis-nonPro residues. The method should be useful to prioritize functionally important residues in cis-isomers for experimental validations and improve sampling of rare protein conformations for ab initio protein structure prediction.