by Adam Górka
Department of Physical Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland.
e-mail:adam.gorka@uj.edu.pl
Protein quartet model
Figure 1
Proteins are the major components of living cells. As discovered recently proteins and protein regions may exist in at least four different states of structural organization: ordered, molten globule, pre-molten globule, and coil-like (Figure 1). What is more, protein function is associated with any of these distinct states or with transitions between them. Entire proteins or proteins regions in the pre-molten globule state or coil-like state display no unique tertiary structure [1]. These Intrinsically Disordered Proteins (IDPs) and Intrinsically Disordered Regions (IDRs) are involved in key biological processes including transcription regulation, cell cycle control, recognition, and signaling [2].
Order vs. Disorder
In contrast to ordered regions (OR), IDR exist as dynamic ensembles in which atom positions and backbone Ramachandran angles vary significantly over time with no specific equilibrium values. The conformational changes of IDR are typically non-cooperative and random. The structure of IDR is dynamic and changing over time what does not exclude the temporary presence of local functional secondary structure that fluctuates in absence of stabilizing forces [3, 4].
How to become a disordered protein
The amino acid chain of IDPs is characterized by a high mean net charge and low mean hydrophobicity per protein residue. High net charge leads to formation of charge–charge repulsion forces that outweighs low hydrophobic force driving to protein collapse and prevent formation of a stable tertiary structure [5, 6].
Amino acid composition of intrinsically disordered regions
This feature make differences between amino acid composition of ordered and disordered regions in proteins. IDPs are enriched in amino acids that promote disorder including: Q, E, A, R, S, F, G, P [7, 8] and depleted in hydrophobic amino acids that promote hydrophobic collapse: W, F, L, S, V, C, F, N [7, 8]. Further differences also exist between short (< 30 amino acids) and long (>= 30 amino acids) IDRs. Short regions are enriched in G, D and have less I, V, L while the long contain more K, E, P and less Q, G, N [9, 10].
Intrinsically disordered regions in numbers
Bioinformatic analyses of proteins’ amino acid composition in known genomes show that the unstructured regions are much more frequent in eukaryotic than prokaryotic proteins. The Disopred2 algorithm predicts that 33% of eukaryotic proteins and only 2% of archaean proteins, and 4.2% of eubacterial proteins contain more than 30 amino acid long IDRs [11]. A similar prediction with the PONDR VL-XT algorithm showed that more than 30 amino acid long IDRs have 9-57% archaeal proteins, 13-52% bacterial proteins and 48-63% eukaryotic proteins. In contrast, IDRs longer than 50 amino acids were predicted only for less than 10% prokaryotic and archaeal and 25% eukaryotic proteins. [12]. It is estimated that 12% of proteins in eukaryotes are completely disordered [13]. 82-94% of transcription factors have long IDRs [14]. IDRs longer than 30 amino acids also contains 79% proteins associated with cancer and 66% of the proteins involved in signal transduction [15]. Disordered regions are common and present in proteins. IDPs are a good object for bioinformatic protein sequence analysis or molecular modeling.
Intrinsically prediction of disordered regions in practice
In practice the following approach can be used to predict globular regions with secondary structures and intrinsically disordered regions from amino acid sequences. As a first step, one should search for homologous sequences and collect useful literature data about their functional, interacting, and binding regions, posttranslational modifications, domain compositions etc. [13, 16].
Next, to avoid pitfalls one should perform an analysis of homologous sequence composition and complexity, a search for signal peptides, transmembrane regions, leucine zippers, zing fingers, coiled-coil regions, modification sites, known sequence motifs, disulfide bridges, presence of similar crystallographic structures, generate HCA and LCA graphs of sequences etc. [13, 16].
Preparation of homologous sequence alignment is the next step. Alignments have to be supplemented with secondary structure prediction and disorder prediction with few ab initio methods (Secondary structure prediction algoritms: PsiPred, Yaspin, Jpred 3, Sspro, Porter [17], Proteus 2 [18]. Disorder prediction algoritms: GLOBPLOT 2.3, IUPRED 2, PONDR VL-XT, PONDR VSL2, metaPrDOS, Metadisorder [9], PONDR FIT [19]). The conserved sequences, secondary structures and disorder regions have to be specified. Pay attention to sequence deletion and insertions that are likely to occur more often in disordered regions [20]. Consider that disordered regions commonly undergo disorder to order transition upon binding. Such sequences can form non-stable fluctuating secondary structures [13, 16].
In the next step, the domain organization of protein should be proposed. Then the CDF-plot and CH-plot analysis methods can be run to provisionally classify them as ordered, disordered [21] or chameleon morphing sequence regions that could be both [22-24]. Chameleon sequences are potential binding sites. They can be disordered when a protein is isolated and perform a function of molecular recognition features (MoRFs) undergoing disorder to order transition upon binding. MoRFs can adopt diverse secondary structures in different complexes. Their occurrence correlates with ELMs and SLiMs inside long disordered regions [10, 15, 22]
Prediction of MoRFs is a challenge. The first average class algorithms of MoRFs prediction like PONDR VL-XT [25, 26] or ANCHOR [14, 27, 28] are available. New algorithms for identifying different types of MoREs are under development [29, 30]. All collected information from sequence analysis should be compared and verified by available literature and experimental data. Bioinformatic protein sequence analysis of IDPs allow for verification of existing hypotheses and to postulate new one [13, 16].
Application of intrinsic disorder
- Figure 2
What is more, identification of MoRFs in IDPs can have more practical application. Knowledge of MoRFs can be used in rational drug design. MoRFs are short peptides (about 30 amino acids) inside long disordered regions that bind specifically to a molecular partner. Newly designed drugs can mimic MoRFs or its binding site selectively targeting specific protein-protein interactions (Figure 2). Disordered regions and MoRF prediction has been used by Molecular Kinetics, Inc. to identify 35 781 sequences in the human proteome that possess features of druggability [14, 30, 31].
Conclusion
Prediction of MoRFs in IDRs open new ways in rational drug design because protein‑protein interactions based on disorder-to-order transition of one partner can make ideal druggable targets.
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