Molecular recognition feature
Molecular recognition features (MoRFs) are small (10-70 residues) intrinsically disordered regions in proteins that undergo a disorder-to-order transition upon binding to their partners. MoRFs are implicated in protein-protein interactions, which serve as the initial step in molecular recognition. MoRFs are disordered prior to binding to their partners, whereas they form a common 3D structure after interacting with their partners. As MoRF regions tend to resemble disordered proteins with some characteristics of ordered proteins, they can be classified as existing in an extended semi-disordered state.
Categorization
MoRFs can be separated in 4 categories according to the shape they form once bound to their partners.
The categories are:
- α-MoRFs (when they form alpha-helixes)
- β-MoRFs (when they form beta-sheets)
- irregular-MoRFs (when they don't form any shape)
- complex-MoRFs (combination of the above categories)
MoRF predictors
Determining protein structures experimentally is a very time-consuming and expensive process. Therefore, recent years have seen a focus on computational methods for predicting protein structure and structural characteristics. Some aspects of protein structure, such as secondary structure and intrinsic disorder, have benefited greatly from applications of deep learning on an abundance of annotated data. However, computational prediction of MoRF regions remains a challenging task due to the limited availability of annotated data and the rarity of the MoRF class itself. Most current methods have been trained and benchmarked on the sets released by the authors of MoRFPred in 2012, as well as another set released by the authors of MoRFChibi based on experimentally-annotated MoRF data. The table below, adapted from, details some methods currently available for MoRF prediction (as well as related problems).
| Predictor | Year Published | Predicts for | Methodology | Uses MSA |
|---|---|---|---|---|
| ANCHOR | 2009 | Protein Binding Regions | Amino acid propensity and energy estimation analysis. | N |
| ANCHOR2 | 2018 | Protein Binding Regions | Amino acid propensity and energy estimation analysis. | N |
| DISOPRED3 | 2015 | Protein Intrinsic Disorder and Protein Binding Sites | Multistage component prediction (utilizing neural network, Support Vector Machine, and K-nearest neighbour models) for protein disorder prediction. Also uses an additional Support Vector Machine to interpolate binding regions from the disorder predictions. | Y |
| DisoRDPbind | 2015 | RNA, DNA, and Protein Binding Regions | Multiple logistic regression models based on predicted disorder, amino acid properties, and sequence composition. The result is aligned with transferred annotations from a functionally-annotated database. | N |
| fMoRFPred | 2016 | MoRFs | Faster version of MoRFPred without the use of multiple sequence alignments. | N |
| MoRFchibi SYSTEM | 2015 | MoRFs | Hierarchy of different in-house MoRF prediction models:
MoRFchibi: Utilizes Bayes rule to combine the outcomes of two support Vector Machine modules using amino acid composition (Sigmoid kernel) and sequence similarity (RBF kernel). MoRFchibi_light: Utilizes Bayes rule to combine MoRFchibi and disorder prediction hierarchically. MoRFchibi_web: Utilizes Bayes rule to combine MoRFchibi, disorder prediction and PSSM (MSA) hierarchically. |
N/Y |
| MoRFPred | 2012 | MoRFs | Support Vector Machine based on predicted sequence characteristics and alignment of input sequence to known MoRF database. | Y |
| MoRFPred-Plus | 2018 | MoRFs | Combined predictions from two Support Vector Machines, predicting for both MoRF regions and MoRF residues. | Y |
| OPAL | 2018 | MoRFs | Support Vector Machine based on physicochemical properties and predicted structural attributes of protein residues | Y |
| OPAL+ | 2019 | MoRFs | Ensemble of Support Vector Machines trained individually for length-specific MoRF regions. Also incorporates other predictors as a metapredictor. | Y |
| SPINE-D | 2012 | Protein Intrinsic Disorder and Semi-Disorder | Neural network for predicting both long and short disordered regions. Semi-disorder can be linearly interpolated from its predicted disorder probabilities (0.4<=P(D)<=0.7). | Y |
| SPOT-Disorder | 2017 | Protein Intrinsic Disorder and Semi-Disorder | Bidirectional Long Short-Term Memory network for predicting intrinsic disorder. Semi-disordered regions can be linearly interpolated from its predicted disorder probabilities (0.28<=P(D)<=0.69). | Y |
| SPOT-MoRF | 2019 | MoRFs | Transfer learning from the large disorder prediction tool SPOT-Disorder2 (which itself utilizes an ensemble of Bidirectional Long Short-Term Memory networks and Inception ResNets). | Y |