MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation

By Chun Fang, Tamotsu Noguchi, Daisuke Tominaga, Hayato Yamana. 2013

64 citations recorded by the source corpus.

Molecular recognition features (MoRFs) are short binding regions located in longer intrinsically disordered protein regions. Although these short regions lack a stable structure in the natural state, they readily undergo disorder-to-order transitions upon binding to their partner molecules. MoRFs play critical roles in the molecular interaction network of a cell, and are associated with many human genetic diseases. Therefore, identification of MoRFs is an important step in understanding functional aspects of these proteins and in finding applications in drug design. Here, we propose a novel method for identifying MoRFs, named as MFSPSSMpred (Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based Predictor). Firstly, a masking method is used to calculate the average local conservation scores of residues within a masking-window length in the position-specific scoring matrix (PSSM). Then, the scores below the average are filtered out. Finally, a smoothing method is used to incorporate the features of flanking regions for each residue to prepare the feature sets for prediction. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the PSSM of sequence only. Experimental results show that, comparing with other methods tested on the same datasets, our method achieves the best performance: achieving 0.004~0.079 higher AUC than other methods when tested on TEST419, and achieving 0.045~0.212 higher AUC than other methods when tested on TEST2012. In addition, when tested on an independent membrane proteins-related dataset, MFSPSSMpred significantly outperformed the existing predictor MoRFpred. This study suggests that: 1) amino acid composition and physicochemical properties in the flanking regions of MoRFs are very different from those in the general non-MoRF regions; 2) MoRFs contain both highly conserved residues and highly variable residues and, on the whole, are highly locally conserved; and 3) combining contextual information with local conservation information of residues facilitates the prediction of MoRFs.

Subfields: Molecular Biology

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Citations in the corpus

IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding (2018, methodology): “MFSPSSMpred and DISOPRED3 predict MoRFs based on an SVM with a radial basis function kernel, and using sequence-derived features and evolutionary profiles as inputs (32,33).”
DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences (2016, unknown): “They include methods that address protein binding (Disfani et al., 2012; Dosztanyi et al., 2009; Fang et al., 2013; Jones and Cozzetto, 2014; VC The Author 2016.”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “Figure 1A shows predictions of disordered protein binding regions from DisoRDPbind and putative MoRF regions generated by MoRFpred, ANCHOR, MFSPSSMpred, and MoRFCHiBiWeb for anophelin protein (DisProt ID: DP00824).”
MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences (2016, methodology): “Then, we used the much smaller TEST EXP9 set to compare performances with those of MFSPSSMpred and DISOPRED3 (Table 4).”
Computational identification of MoRFs in protein sequences (2015, methodology): “Various computational methods have been developed to identify SLiMs and MoRFs in protein sequences including MoRFpred (Disfani et al., 2012), MFSPSSMpred (Fang et al., 2013), PepBindPred (Khan et al., 2013); ANCHOR (Mészá ros et al., 2009), SLiMpred (Mooney et al., 2012), SLiMDisc (Davey et al., 200”
Computational Identification of MoRFs in Protein Sequences Using Hierarchical Application of Bayes Rule (2015, methodology): “Although TEST464 is composed of sequence sets that have previously been used to test the performance of MoRF predictors [13, 14, 16], it may not be ideal for the task for two main reasons; Firstly, TEST464 is not free of redundancy as more than a third of its sequences share 90% or more identity…”