RESUMEN
Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of prediction methods are available for predicting IDRs, their accuracy is very limited on TMPs due to their special physico-chemical properties. We prepared a dataset containing membrane proteins exclusively, using X-ray crystallography data. MemDis is a novel prediction method, utilizing convolutional neural network and long short-term memory networks for predicting disordered regions in TMPs. In addition to attributes commonly used in IDR predictors, we defined several TMP specific features to enhance the accuracy of our method further. MemDis achieved the highest prediction accuracy on TMP-specific dataset among other popular IDR prediction methods.
Asunto(s)
Biología Computacional/métodos , Proteínas Intrínsecamente Desordenadas/química , Proteínas de la Membrana/química , Redes Neurales de la Computación , Secuencia de Aminoácidos , Minería de Datos/métodos , Bases de Datos de Proteínas/estadística & datos numéricos , Internet , Modelos Moleculares , Conformación Proteica , Reproducibilidad de los ResultadosRESUMEN
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). AVAILABILITY: The prediction server PolarProtPred is available at http://polarprotpred.ttk.hu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
RESUMEN
Most cells in multicellular organisms are somehow asymmetric, polarized: maintaining separate membrane domains. Typical examples are the epithelial cells (apical-basal polarization), neurons (dendritic-axonal domains), or migratory cells (with a leading and a trailing edge). Here we present the most comprehensive database containing experimentally verified mammalian proteins that display polarized sorting or secretion, focusing on epithelial polarity. In addition to the source cells or tissues, homology-based inferences and transmembrane topology (if applicable) are all provided. PolarProtDb also offers a detailed interface displaying all information that may be relevant for trafficking: including post-translational modifications (glycosylations and phosphorylations), known or predicted short linear motifs conserved across orthologs, as well as potential interaction partners. Data on polarized sorting has so far been scattered across myriads of publications, hence difficult to access. This information can help researchers in several areas, such as scanning for potential entry points of viral agents like COVID-19. PolarProtDb shall be a useful resource to design future experiments as well as for comparative analyses. The database is available at http://polarprotdb.enzim.hu.