Abstract
Intrinsically disordered regions (IDRs) without stable structure are important for protein structures and functions. Some IDRs can be combined with molecular fragments to make itself completed the transition from disordered to ordered, which are called molecular recognition features (MoRFs). There are five main functions of MoRFs: molecular recognition assembler (MoR_assembler), molecular recognition chaperone (MoR_chaperone), molecular recognition display sites (MoR_display_sites), molecular recognition effector (MoR_effector), and molecular recognition scavenger (MoR_scavenger). Researches on functions of molecular recognition features are important for pharmaceutical and disease pathogenesis. However, the existing computational methods can only predict the MoRFs in proteins, failing to distinguish their different functions. In this paper, we treat MoRF function prediction as a multi-label learning task and solve it with the Binary Relevance (BR) strategy. Finally, we use Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) as basic models to construct MoRF-FUNCpred through ensemble learning. Experimental results show that MoRF-FUNCpred performs well for MoRF function prediction. To the best knowledge of ours, MoRF-FUNCpred is the first predictor for predicting the functions of MoRFs. Availability and Implementation: The stand alone package of MoRF-FUNCpred can be accessed from https://github.com/LiangYu-Xidian/MoRF-FUNCpred.
Highlights
Disordered regions (IDRs) and intrinsically disordered proteins (IDPs) are sequence regions and proteins lack stable 3D structures (Deng et al, 2012; Deng et al, 2015)
The innovation of this work lies in the following: 1) we construct a dataset of inherently disordered proteins with MoRF functions annotation; 2) we take advantage of an ensemble model to integrate the different advantages of models; 3) we propose the first model, MoRF-FUNCpred, for predicting the functions of molecular recognition features in intrinsically disordered proteins
The first MoRF function predictor is proposed called MoRF-FUNCpred, which predicts the functions of MoRFs regarding residues
Summary
Disordered regions (IDRs) and intrinsically disordered proteins (IDPs) are sequence regions and proteins lack stable 3D structures (Deng et al, 2012; Deng et al, 2015). Research on IDPs and IDRs contributes to biomedicine and biology, such as drug discovery and protein structure prediction. Molecular recognition features (MoRFs) are regions that can make the IDR complete the transformation from disordered state to ordered state (Cheng et al, 2007). With the studies of MoRFs, these functional sites may play a role as druggable disease targets, and some drugs are discovered through these sites of action (Kumar et al, 2017; Li et al, 2020; Wang et al, 2020; Zhang et al, 2020; Lv et al, 2021a; Joshi et al, 2021; Shaker et al, 2021; Yan et al, 2021).
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