SPOT-1D2: Improving Protein Secondary Structure Prediction using High Sequence Identity Training Set and an Ensemble of Recurrent and Residual-convolutional Neural Networks

Abstract

Protein secondary structure prediction has been a long-standing problem in computational biology. Recent advances in deep contextual learning have enabled its performance in three-state prediction closer to the theoretical limit at 88–90%. Here, we showed that a large training set with 95% sequence identity cutoff can improve prediction of secondary structures even for those unrelated test sequences (<25% sequence identity cutoff) compared to the use of a non-redundant training dataset with 25% sequence identity cutoff. The three-state prediction edges closer to an accuracy of 87% and eight-state at 76%.The resulting model called SPOT-1D2 is freely available to academic users at https://github.com/jas-preet/SPOT-1D2.