ArticleBMC bioinformatics2019
Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- MoRFs_TransFuse: a MoRFs predictor based on multimodal feature fusion and the lightweight Transformer network.BioData mining · 2025Article
- ENO1/Hsp70 Interaction Domains: In Silico and In Vitro Insight for a Putative Therapeutic Target in Cancer.ACS omega · 2025Article
- Article
- Engagement of intrinsic disordered proteins in protein-protein interaction.Frontiers in molecular biosciences · 2023Article
- Intrinsically Disordered Proteins: An Overview.International journal of molecular sciences · 2022Review
- Prediction of protein-protein interaction sites in intrinsically disordered proteins.Frontiers in molecular biosciences · 2022Review
- Prediction of MoRFs based on sequence properties and convolutional neural networks.BioData mining · 2021Article
- Analyzing IDPs in Interactomes.Methods in molecular biology (Clifton, N.J.) · 2020Article
- APBioNet's annual International Conference on Bioinformatics (InCoB) returns to India in 2018.BMC genomics · 2019Article
- XGBPRH: Prediction of Binding Hot Spots at Protein⁻RNA Interfaces Utilizing Extreme Gradient Boosting.Genes · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMolecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function.
resultsPerformance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the model in this study achieved AUC of 0.819 using TEST set.
conclusionAchieving comparable performance, the proposed method can be used as an alternative approach for MoRF prediction.
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