ArticleInternational journal of molecular sciences2021
MemDis: Predicting Disordered Regions in Transmembrane Proteins.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025Article
- Evaluation of predictions of disordered binding regions in the CAID2 experiment.Computational and structural biotechnology journal · 2025Review
- Assessment of Disordered Linker Predictions in the CAID2 Experiment.Biomolecules · 2024Article
- UniTmp: unified resources for transmembrane proteins.Nucleic acids research · 2024Article
- How AlphaFold2 shaped the structural coverage of the human transmembrane proteome.Scientific reports · 2023Article
- Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.Nature protocols · 2023Review
- LeishMANIAdb: a comparative resource for Leishmania proteins.Database : the journal of biological databases and curation · 2023Article
- Structural Space of the Duffy Antigen/Receptor for Chemokines' Intrinsically Disordered Ectodomain 1 Explored by Temperature Replica-Exchange Molecular Dynamics Simulations.International journal of molecular sciences · 2023Article
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins.Computational and structural biotechnology journal · 2023Article
- Assortment of Frontiers in Protein Science.International journal of molecular sciences · 2022Article
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.