Intrinsically disordered proteins
An intrinsically disordered protein (IDP) is a protein that lacks a fixed or ordered three-dimensional structure. IDPs cover a spectrum of states from fully unstructured to partially structured and include random coils, (pre-)molten globules, and large multi-domain proteins connected by flexible linkers. They constitute one of the main types of protein (alongside globular, fibrous and membrane proteins).
The discovery of IDPs has challenged the traditional protein structure paradigm, that protein function depends on a fixed three-dimensional structure. This dogma has been challenged over the 2000s and 2010s by increasing evidence from various branches of structural biology, suggesting that protein dynamics may be highly relevant for such systems. Despite their lack of stable structure, IDPs are a very large and functionally important class of proteins. In some cases, IDPs can adopt a fixed three-dimensional structure after binding to other macromolecules. Overall, IDPs are different from structured proteins in many ways and tend to have distinct properties in terms of function, structure, sequence, interactions, evolution and regulation.
- 1 History
- 2 Biological roles
- 3 Structural aspects
- 4 Experimental validation
- 5 Disorder prediction
- 6 Disorder and disease
- 7 Computer simulations
- 8 See also
- 9 References
- 10 External links
In the 1930s -1950s, the first protein structures were solved by protein crystallography. These early structures suggested that a fixed three-dimensional structure might be generally required to mediate biological functions of proteins. When stating that proteins have just one uniquely defined configuration, Mirsky and Pauling did not recognize that Fisher's work would have supported their thesis with his 'Lock and Key' model (1894). These publications solidified the central dogma of molecular biology in that the sequence determines the structure which, in turn, determines the function of proteins. In 1950, Karush wrote about 'Configurational Adaptability' contradicting all the assumptions and research in the 19th century. He was convinced that proteins have more than one configuration at the same energy level and can choose one when binding to other substrates. In the 1960s, Levinthal's paradox suggested that the systematic conformational search of a long polypeptide is unlikely to yield a single folded protein structure on biologically relevant timescales (i.e. seconds to minutes). Curiously, for many (small) proteins or protein domains, relatively rapid and efficient refolding can be observed in vitro. As stated in Anfinsen's Dogma from 1973, the fixed 3D structure of these proteins is uniquely encoded in its primary structure (the amino acid sequence), is kinetically accessible and stable under a range of (near) physiological conditions, and can therefore be considered as the native state of such "ordered" proteins.
During the subsequent decades, however, many large protein regions could not be assigned in x-ray datasets, indicating that they occupy multiple positions, which average out in electron density maps. The lack of fixed, unique positions relative to the crystal lattice suggested that these regions were "disordered". Nuclear magnetic resonance spectroscopy of proteins also demonstrated the presence of large flexible linkers and termini in many solved structural ensembles. It is now generally accepted that proteins exist as an ensemble of similar structures with some regions more constrained than others. Intrinsically Unstructured Proteins (IUPs) occupy the extreme end of this spectrum of flexibility, whereas IDPs also include proteins of considerable local structure tendency or flexible multidomain assemblies.These highly dynamic disordered regions of proteins have subsequently been linked to functionally important phenomena such as allosteric regulation and enzyme catalysis.
In the 2000s, bioinformatic predictions of intrinsic disorder in proteins indicated that intrinsic disorder is more common in sequenced/predicted proteomes than in known structures in the protein database. Based on DISOPRED2 prediction, long (>30 residue) disordered segments occur in 2.0% of archaean, 4.2% of eubacterial and 33.0% of eukaryotic proteins. In 2001, Dunker published his paper 'Intrinsically Disordered Proteins' questioning whether the newly found information was ignored for 50 years.
In the 2010s it became clear that IDPs are highly abundant among disease-related proteins.
Many disordered proteins have the binding affinity with their receptors regulated by post-translational modification, thus it has been proposed that the flexibility of disordered proteins facilitates the different conformational requirements for binding the modifying enzymes as well as their receptors. Intrinsic disorder is particularly enriched in proteins implicated in cell signaling, transcription and chromatin remodeling functions.
Disordered regions are often found as flexible linkers or loops connecting domains. Linker sequences vary greatly in length but are typically rich in polar uncharged amino acids. Flexible linkers allow the connecting domains to freely twist and rotate to recruit their binding partners via protein domain dynamics. They also allow their binding partners to induce larger scale conformational changes by long-range allostery.
Linear motifs are short disordered segments of proteins that mediate functional interactions with other proteins or other biomolecules (RNA, DNA, sugars etc.). Many roles of linear motifs are associated with cell regulation, for instance in control of cell shape, subcellular localisation of individual proteins and regulated protein turnover. Often, post-translational modifications such as phosphorylation tune the affinity (not rarely by several orders of magnitude) of individual linear motifs for specific interactions. Relatively rapid evolution and a relatively small number of structural restraints for establishing novel (low-affinity) interfaces make it particularly challenging to detect linear motifs but their widespread biological roles and the fact that many viruses mimick/hijack linear motifs to efficiently recode infected cells underlines the timely urgency of research on this very challenging and exciting topic. Unlike globular proteins IDPs do not have spatially-disposed active pockets. Nevertheless, in 80% of IDPs (~3 dozens) subjected to detailed structural characterization by NMR there are linear motifs termed PreSMos (pre-structured motifs) that are transient secondary structural elements primed for target recognition. In several cases it has been demonstrated that these transient structures become full and stable secondary structures, e.g., helices, upon target binding. Hence, PreSMos are the putative active sites in IDPs.
Coupled folding and binding
Many unstructured proteins undergo transitions to more ordered states upon binding to their targets (e.g. Molecular Recognition Features (MoRFs)). The coupled folding and binding may be local, involving only a few interacting residues, or it might involve an entire protein domain. It was recently shown that the coupled folding and binding allows the burial of a large surface area that would be possible only for fully structured proteins if they were much larger. Moreover, certain disordered regions might serve as "molecular switches" in regulating certain biological function by switching to ordered conformation upon molecular recognition like small molecule-binding, DNA/RNA binding, ion interactions etc.
The ability of disordered proteins to bind, and thus to exert a function, shows that stability is not a required condition. Many short functional sites, for example Short Linear Motifs are over-represented in disordered proteins.
Disorder in the bound state (fuzzy complexes)
Intrinsically disordered proteins can retain their conformational freedom even when they bind specifically to other proteins. The structural disorder in bound state can be static or dynamic. In fuzzy complexes structural multiplicity is required for function and the manipulation of the bound disordered region changes activity. The conformational ensemble of the complex is modulated via post-translational modifications or protein interactions. Specificity of DNA binding proteins often depends on the length of fuzzy regions, which is varied by alternative splicing.
Therefore, their structures are strongly function-related. However, only few proteins are fully disordered in their native state. Disorder is mostly found in intrinsically disordered regions (IDRs) within an otherwise well-structured protein. The term intrinsically disordered protein (IDP) therefore includes proteins that contain IDRs as well as fully disordered proteins.
The existence and kind of protein disorder is encoded in its amino acid sequence. In general, IDPs are characterized by a low content of bulky hydrophobic amino acids and a high proportion of polar and charged amino acids, usually referred to as low hydrophobicity. This property leads to good interactions with water. Furthermore, high net charges promote disorder because of electrostatic repulsion resulting from equally charged residues. Thus disordered sequences cannot sufficiently bury a hydrophobic core to fold into stable globular proteins. In some cases, hydrophobic clusters in disordered sequences provide the clues for identifying the regions that undergo coupled folding and binding (refer to biological roles). Many disordered proteins reveal regions without any regular secondary structure These regions can be termed as flexible, compared to structured loops. While the latter are rigid and contain only one set of Ramachandran angles, IDPs involve multiple sets of angles. The term flexibility is also used for well-structured proteins, but describes a different phenomenon in the context of disordered proteins. Flexibility in structured proteins is bound to an equilibrium state, while it is not so in IDPs. Many disordered proteins also reveal low complexity sequences, i.e. sequences with over-representation of a few residues. While low complexity sequences are a strong indication of disorder, the reverse is not necessarily true, that is, not all disordered proteins have low complexity sequences. Disordered proteins have a low content of predicted secondary structure.
Intrinsically unfolded proteins, once purified, can be identified by various experimental methods. The primary method to obtain information on disordered regions of a protein is NMR spectroscopy. The lack of electron density in X-ray crystallographic studies may also be a sign of disorder.
Folded proteins have a high density (partial specific volume of 0.72-0.74 mL/g) and commensurately small radius of gyration. Hence, unfolded proteins can be detected by methods that are sensitive to molecular size, density or hydrodynamic drag, such as size exclusion chromatography, analytical ultracentrifugation, small angle X-ray scattering (SAXS), and measurements of the diffusion constant. Unfolded proteins are also characterized by their lack of secondary structure, as assessed by far-UV (170-250 nm) circular dichroism (esp. a pronounced minimum at ~200 nm) or infrared spectroscopy. Unfolded proteins also have exposed backbone peptide groups exposed to solvent, so that they are readily cleaved by proteases, undergo rapid hydrogen-deuterium exchange and exhibit a small dispersion (<1 ppm) in their 1H amide chemical shifts as measured by NMR. (Folded proteins typically show dispersions as large as 5 ppm for the amide protons.) Recently, new methods including Fast parallel proteolysis (FASTpp) have been introduced, which allow to determine the fraction folded/disordered without the need for purification. Even subtle differences in the stability of missense mutations, protein partner binding and (self)polymerisation-induced folding of (e.g.) coiled-coils can be detected using FASTpp as recently demonstrated using the tropomyosin-troponin protein interaction. Fully unstructured protein regions can be experimentally validated by their hypersusceptibility to proteolysis using short digestion times and low protease concentrations.
Bulk methods to study IDP structure and dynamics include SAXS for ensemble shape information, NMR for atomistic ensemble refinement, Fluorescence for visualising molecular interactions and conformational transitions, x-ray crystallography to highlight more mobile regions in otherwise rigid protein crystals, cryo-EM to reveal less fixed parts of proteins, light scattering to monitor size distributions of IDPs or their aggregation kinetics, Circular Dichroism to monitor secondary structure of IDPs.
Single-molecule methods to study IDPs include spFRET to study conformational flexibility of IDPs and the kinetics of structural transitions, optical tweezers for high-resolution insights into the ensembles of IDPs and their oligomers or aggregates, nanopores to reveal global shape distributions of IDPs, magnetic tweezers to study structural transitions for long times at low forces, high-speed AFM to visualise the spatio-temporal flexibility of IDPs directly.
Disorder prediction algorithms can predict Intrinsic Disorder (ID) propensity with high accuracy (approaching around 80%) based on primary sequence composition, similarity to unassigned segments in protein x-ray datasets, flexible regions in NMR studies and physico-chemical properties of amino acids.
Distinguishing IDPs from well-structured proteins
Separating disordered from ordered proteins is essential for disorder prediction. One of the first steps to find a factor that distinguishes IDPs from non-IDPs is to specify biases within the amino acid composition. The following hydrophilic, charged amino acids A, R, G, Q, S, P, E and K have been characterized as disorder-promoting amino acids, while order-promoting amino acids W, C, F, I, Y, V, L, and N are hydrophobic and uncharged. The remaining amino acids H, M, T and D are ambiguous, found in both ordered and unstructured regions. This information is the basis of most sequence-based predictors. Regions with little to no secondary structure, also known as NORS (NO Regular Secondary structure) regions, and low-complexity regions can easily be detected. However, not all disordered proteins contain such low complexity sequences.
Determining disordered regions from biochemical methods is very costly and time-consuming. Due to the variable nature of IDPs, only certain aspects of their structure can be detected, so that a full characterization requires a large number of different methods and experiments. This further increases the expense of IDP determination. In order to overcome this obstacle, computer-based methods are created for predicting protein structure and function. It is one of the main goals of bioinformatics to derive knowledge by prediction. Predictors for IDP function are also being developed, but mainly use structural information such as linear motif sites. There are different approaches for predicting IDP structure, such as neural networks or matrix calculations, based on different structural and/or biophysical properties.
Many computational methods exploit sequence information to predict whether a protein is disordered. Notable examples of such software include IUPRED and Disopred. Different methods may use different definitions of disorder. Meta-predictors show a new concept, combining different primary predictors to create a more competent and exact predictor.
Due to the different approaches of predicting disordered proteins, estimating their relative accuracy is fairly difficult. For example, neural networks are often trained on different datasets. The disorder prediction category is a part of biannual CASP experiment that is designed to test methods according accuracy in finding regions with missing 3D structure (marked in PDB files as REMARK465, missing electron densities in X-ray structures).
Disorder and disease
Intrinsically unstructured proteins have been implicated in a number of diseases. Aggregation of misfolded proteins is the cause of many synucleinopathies and toxicity as those proteins start binding to each other randomly and can lead to cancer or cardiovascular diseases. Thereby, misfolding can happen spontaneously because millions of copies of proteins are made during the lifetime of an organism. The aggregation of the intrinsically unstructured protein α-synuclein is thought to be responsible. The structural flexibility of this protein together with its susceptibility to modification in the cell leads to misfolding and aggregation. Genetics, oxidative and nitrative stress as well as mitochondrial impairment impact the structural flexibility of the unstructured α-synuclein protein and associated disease mechanisms. Many key tumour suppressors have large intrinsically unstructured regions, for example p53 and BRCA1. These regions of the proteins are responsible for mediating many of their interactions. Taking the cell's native defense mechanisms as a model drugs can be developed, trying to block the place of noxious substrates and inhibiting them, and thus counteracting the disease.
Owing to high structural heterogeneity, NMR/SAXS experimental parameters obtained will be an average over a large number of highly diverse and disordered states (an ensemble of disordered states). Hence, to understand the structural implications of these experimental parameters, there is a necessity for accurate representation of these ensembles by computer simulations. All-atom molecular dynamic simulations can be used for this purpose but their use is limited by the accuracy of current force-fields in representing disordered proteins. Nevertheless, some force-fields have been explicitly developed for studying disordered proteins by optimising force-field parameters using available NMR data for disordered proteins. (examples are CHARMM 22*, CHARMM 32, Amber ff03* etc.)
MD simulations restrained by experimental parameters (restrained-MD) have also been used to characterise disordered proteins. In principle, one can sample the whole conformational space given an MD simulation (with accurate Force-field) is run long enough. Because of very high structural heterogeneity, the time scales that needs to be run for this purpose are very large and are limited by computational power. However, other computational techniques such as accelerated-MD simulations, or replica exchange simulations, metadynamics or methods using coarse-grained representation have been used to sample broader conformational space in smaller time scales.
Moreover, various protocols and methods of analyzing IDPs, such as studies based on quantitative analysis of GC content in genes and their respective chromosomal bands, have been used to understand functional IDP segments.
- Majorek K, Kozlowski L, Jakalski M, Bujnicki, JM (December 18, 2008). "Chapter 2: First Steps of Protein Structure Prediction". In Bujnicki J. Prediction of Protein Structures, Functions, and Interactions (PDF). John Wiley & Sons, Ltd. pp. 39–62. doi:10.1002/9780470741894.ch2. ISBN 9780470517673.
- Dunker AK, Lawson JD, Brown CJ, Williams RM, Romero P, Oh JS, Oldfield CJ, Campen AM, Ratliff CM, Hipps KW, Ausio J, Nissen MS, Reeves R, Kang C, Kissinger CR, Bailey RW, Griswold MD, Chiu W, Garner EC, Obradovic Z (2001). "Intrinsically disordered protein". Journal of Molecular Graphics & Modelling. 19 (1): 26–59. doi:10.1016/s1093-3263(00)00138-8. PMID 11381529.
- Dyson HJ, Wright PE (March 2005). "Intrinsically unstructured proteins and their functions". Nature Reviews. Molecular Cell Biology. 6 (3): 197–208. doi:10.1038/nrm1589. PMID 15738986.
- Dunker AK, Silman I, Uversky VN, Sussman JL (December 2008). "Function and structure of inherently disordered proteins". Current Opinion in Structural Biology. 18 (6): 756–64. doi:10.1016/j.sbi.2008.10.002. PMID 18952168.
- Andreeva A, Howorth D, Chothia C, Kulesha E, Murzin AG (January 2014). "SCOP2 prototype: a new approach to protein structure mining". Nucleic Acids Research. 42 (Database issue): D310–4. doi:10.1093/nar/gkt1242. PMC . PMID 24293656.
- van der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, Fuxreiter M, Gough J, Gsponer J, Jones DT, Kim PM, Kriwacki RW, Oldfield CJ, Pappu RV, Tompa P, Uversky VN, Wright PE, Babu MM (July 2014). "Classification of intrinsically disordered regions and proteins". Chemical Reviews. 114 (13): 6589–631. doi:10.1021/cr400525m. PMC . PMID 24773235.
- Song J, Lee MS, Carlberg I, Vener AV, Markley JL (December 2006). "Micelle-induced folding of spinach thylakoid soluble phosphoprotein of 9 kDa and its functional implications". Biochemistry. 45 (51): 15633–43. doi:10.1021/bi062148m. PMC . PMID 17176085.
- Bu Z, Callaway DJ (2011). "Proteins move! Protein dynamics and long-range allostery in cell signaling". Advances in Protein Chemistry and Structural Biology. Advances in Protein Chemistry and Structural Biology. 83: 163–221. doi:10.1016/B978-0-12-381262-9.00005-7. ISBN 9780123812629. PMID 21570668.
- Kamerlin SC, Warshel A (May 2010). "At the dawn of the 21st century: Is dynamics the missing link for understanding enzyme catalysis?". Proteins. 78 (6): 1339–75. doi:10.1002/prot.22654. PMC . PMID 20099310.
- Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT (March 2004). "Prediction and functional analysis of native disorder in proteins from the three kingdoms of life". Journal of Molecular Biology. 337 (3): 635–45. doi:10.1016/j.jmb.2004.02.002. PMID 15019783.
- Dunker AK, Lawson JD, Brown CJ, Williams RM, Romero P, Oh JS, Oldfield CJ, Campen AM, Ratliff CM, Hipps KW, Ausio J, Nissen MS, Reeves R, Kang C, Kissinger CR, Bailey RW, Griswold MD, Chiu W, Garner EC, Obradovic Z (2001-01-01). "Intrinsically disordered protein". Journal of Molecular Graphics & Modelling. 19 (1): 26–59. doi:10.1016/s1093-3263(00)00138-8. PMID 11381529.
- Uversky VN, Oldfield CJ, Dunker AK (2008). "Intrinsically disordered proteins in human diseases: introducing the D2 concept". Annual Review of Biophysics. 37: 215–46. doi:10.1146/annurev.biophys.37.032807.125924. PMID 18573080.
- Collins MO, Yu L, Campuzano I, Grant SG, Choudhary JS (July 2008). "Phosphoproteomic analysis of the mouse brain cytosol reveals a predominance of protein phosphorylation in regions of intrinsic sequence disorder". Molecular & Cellular Proteomics. 7 (7): 1331–48. doi:10.1074/mcp.M700564-MCP200. PMID 18388127.
- Iakoucheva LM, Brown CJ, Lawson JD, Obradović Z, Dunker AK (October 2002). "Intrinsic disorder in cell-signaling and cancer-associated proteins". Journal of Molecular Biology. 323 (3): 573–84. doi:10.1016/S0022-2836(02)00969-5. PMID 12381310.
- Sandhu KS (2009). "Intrinsic disorder explains diverse nuclear roles of chromatin remodeling proteins". Journal of Molecular Recognition. 22 (1): 1–8. doi:10.1002/jmr.915. PMID 18802931.
- Lee SH, Kim DH, Han JJ, Cha EJ, Lim JE, Cho YJ, Lee C, Han KH (February 2012). "Understanding pre-structured motifs (PreSMos) in intrinsically unfolded proteins". Current Protein & Peptide Science. 13 (1): 34–54. doi:10.2174/138920312799277974. PMID 22044148.
- Mohan A, Oldfield CJ, Radivojac P, Vacic V, Cortese MS, Dunker AK, Uversky VN (October 2006). "Analysis of molecular recognition features (MoRFs)". Journal of Molecular Biology. 362 (5): 1043–59. doi:10.1016/j.jmb.2006.07.087. PMID 16935303.
- Gunasekaran K, Tsai CJ, Kumar S, Zanuy D, Nussinov R (February 2003). "Extended disordered proteins: targeting function with less scaffold". Trends in Biochemical Sciences. 28 (2): 81–5. doi:10.1016/S0968-0004(03)00003-3. PMID 12575995.
- Sandhu KS, Dash D (July 2007). "Dynamic alpha-helices: conformations that do not conform". Proteins. 68 (1): 109–22. doi:10.1002/prot.21328. PMID 17407165.
- Fuxreiter M (January 2012). "Fuzziness: linking regulation to protein dynamics". Molecular bioSystems. 8 (1): 168–77. doi:10.1039/c1mb05234a. PMID 21927770.
- Fuxreiter M, Simon I, Bondos S (August 2011). "Dynamic protein-DNA recognition: beyond what can be seen". Trends in Biochemical Sciences. 36 (8): 415–23. doi:10.1016/j.tibs.2011.04.006. PMID 21620710.
- Uversky VN (August 2011). "Intrinsically disordered proteins from A to Z". The International Journal of Biochemistry & Cell Biology. 43 (8): 1090–103. doi:10.1016/j.biocel.2011.04.001. PMID 21501695.
- Oldfield, C. (2014). "Intrinsically Disordered Proteins and Intrinsically Disordered Protein Regions". Annual Review of Biochemistry. 83: 553–584. doi:10.1146/annurev-biochem-072711.
- Minde DP, Maurice MM, Rüdiger SG (2012). Uversky VN, ed. "Determining biophysical protein stability in lysates by a fast proteolysis assay, FASTpp". PLOS One. 7 (10): e46147. Bibcode:2012PLoSO...746147M. doi:10.1371/journal.pone.0046147. PMC . PMID 23056252.
- Park C, Marqusee S (March 2005). "Pulse proteolysis: a simple method for quantitative determination of protein stability and ligand binding". Nature Methods. 2 (3): 207–12. doi:10.1038/nmeth740. PMID 15782190.
- Robaszkiewicz K, Ostrowska Z, Cyranka-Czaja A, Moraczewska J (May 2015). "Impaired tropomyosin-troponin interactions reduce activation of the actin thin filament". Biochimica et Biophysica Acta. 1854 (5): 381–90. doi:10.1016/j.bbapap.2015.01.004. PMID 25603119.
- Minde DP, Radli M, Forneris F, Maurice MM, Rüdiger SG (2013). Buckle AM, ed. "Large extent of disorder in Adenomatous Polyposis Coli offers a strategy to guard Wnt signalling against point mutations". PLOS One. 8 (10): e77257. Bibcode:2013PLoSO...877257M. doi:10.1371/journal.pone.0077257. PMC . PMID 24130866.
- Brucale M, Schuler B, Samorì B (March 2014). "Single-molecule studies of intrinsically disordered proteins". Chemical Reviews. 114 (6): 3281–317. doi:10.1021/cr400297g. PMID 24432838.
- Neupane K, Solanki A, Sosova I, Belov M, Woodside MT (2014). "Diverse metastable structures formed by small oligomers of α-synuclein probed by force spectroscopy". PLOS One. 9 (1): e86495. Bibcode:2014PLoSO...986495N. doi:10.1371/journal.pone.0086495. PMC . PMID 24475132.
- Japrung D, Dogan J, Freedman KJ, Nadzeyka A, Bauerdick S, Albrecht T, Kim MJ, Jemth P, Edel JB (February 2013). "Single-molecule studies of intrinsically disordered proteins using solid-state nanopores". Analytical Chemistry. 85 (4): 2449–56. doi:10.1021/ac3035025. PMID 23327569.
- Min D, Kim K, Hyeon C, Cho YH, Shin YK, Yoon TY (2013). "Mechanical unzipping and rezipping of a single SNARE complex reveals hysteresis as a force-generating mechanism". Nature Communications. 4 (4): 1705. Bibcode:2013NatCo...4E1705M. doi:10.1038/ncomms2692. PMC . PMID 23591872.
- Miyagi A, Tsunaka Y, Uchihashi T, Mayanagi K, Hirose S, Morikawa K, Ando T (September 2008). "Visualization of intrinsically disordered regions of proteins by high-speed atomic force microscopy". Chemphyschem. 9 (13): 1859–66. doi:10.1002/cphc.200800210. PMID 18698566.
- Schlessinger A, Schaefer C, Vicedo E, Schmidberger M, Punta M, Rost B (June 2011). "Protein disorder--a breakthrough invention of evolution?". Current Opinion in Structural Biology. 21 (3): 412–8. doi:10.1016/j.sbi.2011.03.14. PMID 21514145.
- Tompa, P. (2011). "Unstructural biology coming of age". Current Opinion in Structural Biology. 21: 419–425. doi:10.1016/j.sbi.2011.03.12.
- Ferron F, Longhi S, Canard B, Karlin D (October 2006). "A practical overview of protein disorder prediction methods". Proteins. 65 (1): 1–14. doi:10.1002/prot.21075. PMID 16856179.
- Uversky VN, Oldfield CJ, Dunker AK (2008). "Intrinsically disordered proteins in human diseases: introducing the D2 concept". Annual Review of Biophysics. 37: 215–46. doi:10.1146/annurev.biophys.37.032807.125924. PMID 18573080.
- Wise-Scira O, Dunn A, Aloglu AK, Sakallioglu IT, Coskuner O (March 2013). "Structures of the E46K mutant-type α-synuclein protein and impact of E46K mutation on the structures of the wild-type α-synuclein protein". ACS Chemical Neuroscience. 4 (3): 498–508. doi:10.1021/cn3002027. PMID 23374074.
- Dobson CM (December 2003). "Protein folding and misfolding". Nature. 426 (6968): 884–90. Bibcode:2003Natur.426..884D. doi:10.1038/nature02261. PMID 14685248.
- Best RB, Zhu X, Shim J, Lopes PE, Mittal J, Feig M, Mackerell AD (September 2012). "Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles". Journal of Chemical Theory and Computation. 8 (9): 3257–3273. doi:10.1021/ct300400x. PMC . PMID 23341755.
- Best RB (February 2017). "Computational and theoretical advances in studies of intrinsically disordered proteins". Current Opinion in Structural Biology. 42: 147–154. doi:10.1016/j.sbi.2017.01.006. PMID 28259050.
- Chong SH, Chatterjee P, Ham S (May 2017). "Computer Simulations of Intrinsically Disordered Proteins". Annual Review of Physical Chemistry. 68: 117–134. Bibcode:2017ARPC...68..117C. doi:10.1146/annurev-physchem-052516-050843. PMID 28226222.
- Fox SJ, Kannan S (September 2017). "Probing the dynamics of disorder". Progress in Biophysics and Molecular Biology. 128: 57–62. doi:10.1016/j.pbiomolbio.2017.05.008. PMID 28554553.
- Terakawa T, Takada S (September 2011). "Multiscale ensemble modeling of intrinsically disordered proteins: p53 N-terminal domain". Biophysical Journal. 101 (6): 1450–8. Bibcode:2011BpJ...101.1450T. doi:10.1016/j.bpj.2011.08.003. PMC . PMID 21943426.
- Fisher, Charles K; Stultz, Collin M (2011). "Constructing ensembles for intrinsically disordered proteins". Current Opinion in Structural Biology. 21 (3): 426. doi:10.1016/j.sbi.2011.04.001. PMID 21530234.
- Apicella A, Marascio M, Colangelo V, Soncini M, Gautieri A, Plummer CJ (June 2017). "Molecular dynamics simulations of the intrinsically disordered protein amelogenin". Journal of Biomolecular Structure & Dynamics. 35 (8): 1813–1823. doi:10.1080/07391102.2016.1196151. PMID 27366858.
- Zerze GH, Miller CM, Granata D, Mittal J (June 2015). "Free energy surface of an intrinsically disordered protein: comparison between temperature replica exchange molecular dynamics and bias-exchange metadynamics". Journal of Chemical Theory and Computation. 11 (6): 2776–82. doi:10.1021/acs.jctc.5b00047. PMID 26575570.
- Granata D, Baftizadeh F, Habchi J, Galvagnion C, De Simone A, Camilloni C, Laio A, Vendruscolo M (October 2015). "The inverted free energy landscape of an intrinsically disordered peptide by simulations and experiments". Scientific Reports. 5: 15449. Bibcode:2015NatSR...515449G. doi:10.1038/srep15449. PMC . PMID 26498066.
- Kurcinski, Mateusz; Kolinski, Andrzej; Kmiecik, Sebastian (2014-05-21). "Mechanism of Folding and Binding of an Intrinsically Disordered Protein As Revealed by ab Initio Simulations". Journal of Chemical Theory and Computation. 10 (6): 2224–2231. doi:10.1021/ct500287c. ISSN 1549-9618.
- Uversky VN (2013). "Digested disorder: Quarterly intrinsic disorder digest (January/February/March, 2013)". Intrinsically Disordered Proteins. 1 (1): e25496. doi:10.4161/idp.25496. PMC . PMID 28516015.
- Costantini S, Sharma A, Raucci R, Costantini M, Autiero I, Colonna G (March 2013). "Genealogy of an ancient protein family: the Sirtuins, a family of disordered members". BMC Evolutionary Biology. 13: 60. doi:10.1186/1471-2148-13-60. PMC . PMID 23497088.
- Intrinsically disordered protein at Proteopedia
- MobiDB: a comprehensive database or intrinsic protein disorder annotations
- IDEAL - Intrinsically Disordered proteins with Extensive Annotations and Literature
- D2P2 Database of Disordered Protein Predictions
- Gallery of images of intrinsically disordered proteins
- First IDP journal covering all topics of IDP research
- IDP Journal
- Database of experimentally validated IDPs
- IDP ensemble database