Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning

By Jaswinder Singh, Jack Hanson, Rhys Heffernan, Kuldip K. Paliwal, Yuedong Yang, Yaoqi Zhou. 2018

14 citations recorded by the source corpus.

It has been long established that cis conformations of amino acid residues play many biologically important roles despite their rare occurrence in protein structure. Because of 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rarity, few methods have been developed for predicting cis isomers from protein sequences, most of which are based on outdated datasets and lack the means for independent testing. In this work, using a database of >10000 high-resolution protein structures, we update the statistics of cis isomers and develop a sequence-based prediction technique using an ensemble of residual convolutional and long short-term memory bidirectional recurrent neural networks that allow learning from the whole protein sequence. We show that ensembling eight neural network models yields maximum Matthews correlation coefficient values of approximately 0.35 for cis-Pro isomers and 0.1 for cis-nonPro residues. The method should be useful for prioritizing functionally important residues in cis isomers for experimental validations and improving the sampling of rare protein conformations for ab initio protein structure prediction.

Subfields: Molecular Biology

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SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “…has been enhanced by utilizing an ensemble set of hybrid models consisting of both LSTM-BRNNs and residual convolutional neural networks (residual CNNs, called ResNets) [24] for protein contact map prediction [25], protein x angle prediction [26], and protein secondary structure prediction [27].”