Targeting intrinsically disordered proteins in neurodegenerative and protein dysfunction diseases: another illustration of the D(2) concept

Abstract

Many biologically active proteins, which are usually called intrinsically disordered or natively unfolded proteins, lack stable tertiary and/or secondary structure under physiological conditions in vitro. Their functions complement the functional repertoire of ordered proteins, with intrinsically disordered proteins (IDPs) often being involved in regulation, signaling and control. Their amino acid sequences and compositions are very different from those of ordered proteins, making reliable identification of IDPs possible at the proteome level. IDPs are highly abundant in various human diseases, including neurodegeneration and other protein dysfunction maladies and, therefore, represent attractive novel drug targets. Some of the aspects of IDPs, as well as their roles in neurodegeneration and protein dysfunction diseases, are discussed in this article, together with the peculiarities of IDPs as potential drug targets.

Figure 1. Fate of a polypeptide chain

The three structures on the left representing typical intrinsically disordered proteins with different disordered levels (from top to bottom): native coil, native premolten globule and native molten globule. Top right structure illustrates a well-folded protein, whereas the bottom right structure represents one of the products of protein misfolding – a molecular model of the compact, 4-protofilament insulin fibril. Modified from [221].

Figure 2. Peculiarities of amino acid sequences of intrinsically disordered proteins associated with neurodegeneration

(A) Compositional profiling of proteins involved in neurodegenerative disease. Compositional profiling is based on the evaluation of the (C_s1−C_s2)/C_s2 values, where C_s1 is a content of a given residue in a set of interest (proteins associated with neurodegenerative diseases or typical intrinsically disordered proteins [IDPs] from DisProt), and C_s2 is the corresponding value for the set of ordered proteins. In this presentation, enrichment or depletion in each amino acid type appears as a positive or negative bar, respectively. Amino acids are indicated by the single-letter code and ordered according to their disorder promoting strength. Corresponding data for well-characterized intrinsically disordered regions from DisProt are also shown. (B) Abundance of intrinsic disorder in proteins associated with neurodegenerative diseases. Percentages of disease-associated proteins with at least 30–100 consecutive residues predicted to be disordered. The error bars represent 95% CIs and were calculated using 1000 bootstrap re-sampling. Corresponding data for signaling and ordered proteins are shown for the comparison. Analyzed protein sets included 1786 proteins associated with cancer, 2329 proteins involved in cellular signaling, 689 proteins involved in the neurodegenerative diseases, 53,630 nonredundant eukaryotic proteins from Swiss-Prot, and 1138 non-homologous ordered proteins from PDB Select 25 (this dataset contained only the ordered parts of the proteins). Redrawn from [64].

Figure 3. Predicted intrinsic disorder in proteins associated with amyotrophic lateral sclerosis

Abundance of intrinsic disorder in human proteins associated with amyotrophic lateral sclerosis was evaluated using the PONDR® VSL2 algorithm [214], which is one of the most accurate predictors of intrinsic disorder for proteins containing both ordered and disorder regions. Each plot represents a distribution of the predicted intrinsic disorder propensity (PONDR VSL2 score) within the amino acid sequence of a given protein. Analyzed proteins are: (A) SOD1, (B) TDP-43, (C) progranulin, (D) angiogenin, (E) fused in sarcoma/translated in liposarcoma, (F) senataxin and (G) survival motor neuron protein. Shaded areas in each plot correspond to the scores associated with predicted intrinsic disorder.

Figure 4. Intrinsic disorder in p53 and interactions with different binding partners

A structure versus disorder prediction on the p53 amino acid sequence is shown in the center of the figure, along with the structures of various regions of p53 bound to 14 different partners. Disorder predictions were done using the PONDR VLXT algorithm. The plot represents the distribution of the disorder propensity (PONDR score, up = disorder, down = order) within the amino acid sequence of p53. The results of the disorder predictions (the central region was predicted to be ordered, whereas the N- and C-termini were predicted to be disordered) have been confirmed experimentally for p53. The various regions of p53 are color coded to show their structures in the complex, and to map the binding segments to the amino acid sequence. Starting with the p53–DNA complex (top-left: magenta = protein; blue = DNA), and moving in a clockwise direction, the Protein Data Bank IDs and partner names are given as follows for the 14 complexes: 1tsr–DNA, 1gzh–53BP1, 1q2d–gcn5, 3sak–p53 (tetrametization domain), 1xqh–set9, 1h26–cyclinA, 1ma3–sirtuin, 1jsp–CBP bromo domain, 1dt7–s100ββ, 2h1l–sv40 large T antigen, 1ycs–53BP2, 2gs0–PH, 1ycr–Mdm2 and 2b3g–rpa70. Reproduced from [7].

Figure 5. Abundance of intrinsic disorder within the p53 regulatory network

Selected upstream regulators, downstream effectors and output terms are shown. A simplified evolutionary schematic of the p53 regulatory network, which was presented in [162], was used as a basis for this figure. Color-gradient coding related to the range of conservation of various members of the p53 regulatory network in vertebrates and in invertebrates is removed. The new color coding corresponds to the various pathways within the p53 network. Several p53 effectors related to the regulation of metabolism were added to the original plot together with the new output term ‘tumor invasion/metastasis’. Percentages in brackets reflect the information on abundance of predicted disorder in various members of this network.