SCLpred‐MEM : Subcellular localization prediction of membrane proteins by deep N‐to‐1 convolutional neural networks

The knowledge of the subcellular location of a protein is a valuable source of information in genomics, drug design, and various other theoretical and analytical perspectives of bioinformatics. Due to the expensive and time‐consuming nature of experimental methods of protein subcellular location determination, various computational methods have been developed for subcellular localization prediction. We introduce “SCLpred‐MEM,” an ab initio protein subcellular localization predictor, powered by an ensemble of Deep N‐to‐1 Convolutional Neural Networks (N1‐NN) trained and tested on strict redundancy reduced datasets. SCLpred‐MEM is available as a web‐server predicting query proteins into two classes, membrane and non‐membrane proteins. SCLpred‐MEM achieves a Matthews correlation coefficient of 0.52 on a strictly homology‐reduced independent test set and 0.62 on a less strict homology reduced independent test set, surpassing or matching other state‐of‐the‐art subcellular localization predictors.