Molecular recognition features (MoRFs) in three domains of life.

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SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “For comparison with other models, besides the web servers of MoRFpred, fMoRFpred, and Dis-oRDPbind [58,59] (http://biomine.cs.vcu.edu/#webservers), we also used ANCHOR2 [60] (https://iupred2a.elte.hu/), (https://gsponerlab.msl.ubc.ca/software/morf_chibi/), and the local version of MoRFPred-plus…”
IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding (2018, methodology): “MoRFpred and fMoRFpred utilize SVM models in their predictions incorporating sequence conservation data and amino-acid physicochemical properties, in addition to predictions of intrinsic disorder, relative solvent accessibility and residue flexibility (30,31).”
MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences (2016, methodology): “MoRFs are found in proteins that are involved in diverse cellular processes in all three domains of life (8).”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “fMoRFpred 2015 [130] Protein binding WS Yes Yes http://biomine.”