Molecular recognition features (MoRFs) are small (10-70 residues)
intrinsically disordered regions in
proteins
Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, respondi ...
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
The term molecular recognition refers to the specific interaction between two or more molecules through noncovalent bonding such as hydrogen bonding, metal coordination, hydrophobic forces, van der Waals forces, π-π interactions, halogen ...
. 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
Protein secondary structure is the three dimensional form of ''local segments'' of proteins. The two most common secondary structural elements are alpha helices and beta sheets, though beta turns and omega loops occur as well. Secondary struct ...
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).
Databases
mpMoRFsDBMutual Folding Induced by Binding (MFIB) database
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References
{{Reflist
Proteins