PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning

Motivation: Cell polarity refers to the asymmetric organization of cellular components in various cells. Epithelial cells are the best-known examples of polarized cells, featuring apical and basolateral membrane domains. Mounting evidence suggests that short linear motifs play a major role in protein trafficking to these domains, although the exact rules governing them are still elusive. Results: In this study we prepared neural networks that capture recurrent patterns to classify transmembrane proteins localizing into apical and basolateral membranes. Asymmetric expression of drug transporters results in vectorial drug transport, governing the pharmacokinetics of numerous substances, yet the data on how proteins are sorted in epithelial cells is very scattered. The provided method may offer help to experimentalists to identify or better characterize molecular networks regulating the distribution of transporters or surface receptors (including viral entry receptors like that of COVID-19).

Automated summary

This study utilized neural networks to classify transmembrane proteins on apical and basolateral membranes, aiding in understanding drug transport and its effects. The method provided may assist researchers in identifying or characterizing molecular networks regulating the distribution of transporters or receptors.