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SCLpred‐MEM : Subcellular localization prediction of membrane proteins by deep N‐to‐1 convolutional neural networks
Author(s) -
Kaleel Manaz,
Ellinger Liam,
Lalor Clodagh,
Pollastri Gianluca,
Mooney Catherine
Publication year - 2021
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.26144
Subject(s) - subcellular localization , convolutional neural network , protein subcellular localization prediction , artificial intelligence , computer science , redundancy (engineering) , test set , computational biology , pattern recognition (psychology) , membrane protein , machine learning , chemistry , membrane , biology , biochemistry , gene , operating system
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.