MemDis: Predicting Disordered Regions in Transmembrane Proteins.
Int J Mol Sci
; 22(22)2021 Nov 12.
Article
in English
| MEDLINE | ID: covidwho-1534086
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Computational Biology
/
Intrinsically Disordered Proteins
/
Membrane Proteins
Type of study:
Prognostic study
/
Reviews
Language:
English
Year:
2021
Document Type:
Article
Affiliation country:
Ijms222212270
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