SCLpred‐MEM : subcellular localization prediction of membrane proteins by Deep N‐to‐1 Convolutional Neural Networks

Manaz Kaleel, Liam Ellinger, Clodagh Lalor, Gianluca Pollastri, Catherine Mooney
<span title="2021-05-13">2021</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fkowqlvuffe5lnyfwblj3fcp7i" style="color: black;">Proteins: Structure, Function, and Bioinformatics</a> </i> &nbsp;
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
more &raquo; ... 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 (MCC) 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. This article is protected by copyright. All rights reserved.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/prot.26144">doi:10.1002/prot.26144</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33983651">pmid:33983651</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qgte7amdjfd3fkggzfnodywrhq">fatcat:qgte7amdjfd3fkggzfnodywrhq</a> </span>
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