Identification of Intrinsically Disordered Proteins and Regions by Length-Dependent Predictors Based on Conditional Random Fields.
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ABSTRACT:
SUBMITTER: Liu Y
PROVIDER: S-EPMC6626971 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Accurate prediction of intrinsically disordered proteins/regions is one of the most important tasks in bioinformatics, and some computational predictors have been proposed to solve this problem. How to efficiently incorporate the sequence-order effect is critical for constructing an accurate predictor because disordered region distributions show global sequence patterns. In order to capture these sequence patterns, several sequence labelling models have been applied to this field, such as conditional random fields (CRFs). However, these methods suffer from certain disadvantages. In this study, we proposed a new computational predictor called IDP⁻CRF, which is trained on an updated benchmark dataset based on the MobiDB database and the DisProt database, and incorporates more comprehensive sequence-based features, including PSSMs (position-specific scoring matrices), kmer, predicted secondary structures, and relative solvent accessibilities. Experimental results on the benchmark dataset and two independent datasets show that IDP⁻CRF outperforms 25 existing state-of-the-art methods in this field, demonstrating that IDP⁻CRF is a very useful tool for identifying IDPs/IDRs (intrinsically disordered proteins/regions). We anticipate that IDP⁻CRF will facilitate the development of protein sequence analysis.
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Project description:Many different intrinsically disordered proteins and proteins with intrinsically disordered regions are associated with neurodegenerative diseases. These types of proteins including amyloid-β, tau, α-synuclein, CHCHD2, CHCHD10, and G-protein coupled receptors are increasingly becoming evaluated as potential drug targets in the pharmaceutical-based treatment approaches. Here, we focus on the neurobiology of this class of proteins, which lie at the center of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, Huntington's disease, amyotrophic lateral sclerosis, frontotemporal dementia, Charcot-Marie-Tooth diseases, spinal muscular atrophy, and mitochondrial myopathy. Furthermore, we discuss the current treatment design strategies involving intrinsically disordered proteins and proteins with intrinsically disordered regions in neurodegenerative diseases. In addition, we emphasize that although the G-protein coupled receptors are traditionally investigated using structural biology-based models and approaches, current studies show that these receptors are proteins with intrinsically disordered regions and therefore they require new ways for their analysis.
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Project description:Autophagy is an essential eukaryotic pathway required for cellular homeostasis. Numerous key autophagy effectors and regulators have been identified, but the mechanism by which they carry out their function in autophagy is not fully understood. Our rigorous bioinformatic analysis shows that the majority of key human autophagy proteins include intrinsically disordered regions (IDRs), which are sequences lacking stable secondary and tertiary structure; suggesting that IDRs play an important, yet hitherto uninvestigated, role in autophagy. Available crystal structures corroborate the absence of structure in some of these predicted IDRs. Regions of orthologs equivalent to the IDRs predicted in the human autophagy proteins are poorly conserved, indicating that these regions may have diverse functions in different homologs. We also show that IDRs predicted in human proteins contain several regions predicted to facilitate protein-protein interactions, and delineate the network of proteins that interact with each predicted IDR-containing autophagy protein, suggesting that many of these interactions may involve IDRs. Lastly, we experimentally show that a BCL2 homology 3 domain (BH3D), within the key autophagy effector BECN1 is an IDR. This BH3D undergoes a dramatic conformational change from coil to ?-helix upon binding to BCL2s, with the C-terminal half of this BH3D constituting a binding motif, which serves to anchor the interaction of the BH3D to BCL2s. The information presented here will help inform future in-depth investigations of the biological role and mechanism of IDRs in autophagy proteins.
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Project description:Phosphorylation is a major post-translational modification that plays a central role in signaling pathways. Protein kinases phosphorylate substrates (phosphoproteins) by adding phosphate at Ser/Thr or Tyr residues (phosphosites). A large amount of data identifying and describing phosphosites in phosphoproteins has been reported but the specificity of phosphorylation is not fully resolved. In this report, data of kinase-substrate pairs identified by the Kinase-Interacting Substrate Screening (KISS) method were used to analyze phosphosites in intrinsically disordered regions (IDRs) of intrinsically disordered proteins. We compared phosphorylated and nonphosphorylated IDRs and found that the phosphorylated IDRs were significantly longer than nonphosphorylated IDRs. The phosphorylated IDR is often the longest IDR (71%) in a phosphoprotein when only a single phosphosite exists in the IDR, and when the phosphoprotein has multiple phosphosites in an IDR(s), the phosphosites are primarily localized in a single IDR (78%) and this IDR is usually the longest one (81%). We constructed a stochastic model of phosphorylation to estimate the effect of IDR length. The model that accounted for IDR length produced more realistic results when compared with a model that excluded the IDR length. We propose that the IDR length is a significant determinant for locating kinase phosphorylation sites in phosphoproteins.
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Project description:Plant-specific remorin proteins reside in subdomains of plasma membranes, originally termed membrane rafts. They probably facilitate cellular signal transduction by direct interaction with signaling proteins such as receptor-like kinases and may dynamically modulate their lateral segregation within plasma membranes. Recent evidence suggests such functions of remorins during plant-microbe interactions and innate immune responses, where differential phosphorylation of some of these proteins has been described to be dependent on the perception of the microbe-associated molecular pattern (MAMP) flg22 and the presence of the NBS-LRR resistance protein RPM1. A number of specifically phosphorylated residues in their highly variable and intrinsically disordered N-terminal regions have been identified. Sequence diversity of these evolutionary distinct domains suggests that remorins may serve a wide range of biological functions. Here, we describe patterns and features of intrinsic disorder in remorin protein and discuss possible functional implications of phosphorylation within these rapidly evolving domains.
| S-EPMC3355724 | biostudies-literature
Project description:The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs/IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents 'Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55 vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible 'disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.
| S-EPMC5429364 | biostudies-literature
Project description:MotivationPeptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood.ResultsWe present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence. DeepPeptide shows both improved precision and recall for peptide detection compared to previous methodology. We show that the model is capable of identifying peptides in underannotated proteomes.Availability and implementationDeepPeptide is available online at ku.biolib.com/DeepPeptide.
| S-EPMC10585352 | biostudies-literature
Project description:Intrinsic disorder is more abundant in eukaryotic than prokaryotic proteins. Methods predicting intrinsic disorder are based on the amino acid sequence of a protein. Therefore, there must exist an underlying difference in the sequences between eukaryotic and prokaryotic proteins causing the (predicted) difference in intrinsic disorder. By comparing proteins, from complete eukaryotic and prokaryotic proteomes, we show that the difference in intrinsic disorder emerges from the linker regions connecting Pfam domains. Eukaryotic proteins have more extended linker regions, and in addition, the eukaryotic linkers are significantly more disordered, 38% vs. 12-16% disordered residues. Next, we examined the underlying reason for the increase in disorder in eukaryotic linkers, and we found that the changes in abundance of only three amino acids cause the increase. Eukaryotic proteins contain 8.6% serine; while prokaryotic proteins have 6.5%, eukaryotic proteins also contain 5.4% proline and 5.3% isoleucine compared with 4.0% proline and ≈ 7.5% isoleucine in the prokaryotes. All these three differences contribute to the increased disorder in eukaryotic proteins. It is tempting to speculate that the increase in serine frequencies in eukaryotes is related to regulation by kinases, but direct evidence for this is lacking. The differences are observed in all phyla, protein families, structural regions and type of protein but are most pronounced in disordered and linker regions. The observation that differences in the abundance of three amino acids cause the difference in disorder between eukaryotic and prokaryotic proteins raises the question: Are amino acid frequencies different in eukaryotic linkers because the linkers are more disordered or do the differences cause the increased disorder?
| S-EPMC6675126 | biostudies-literature
Project description:Intrinsically disordered proteins (IDPs) participate in critical cellular functions that exploit the flexibility and rapid conformational fluctuations of their native state. Limited information about the native state of IDPs can be gained by the averaging over many heterogeneous molecules that is unavoidable in ensemble approaches. We used single molecule fluorescence to characterize native state conformational dynamics in five synaptic proteins confirmed to be disordered by other techniques. For three of the proteins, SNAP-25, synaptobrevin and complexin, their conformational dynamics could be described with a simple semiflexible polymer model. Surprisingly, two proteins, neuroligin and the NMDAR-2B glutamate receptor, were observed to stochastically switch among distinct conformational states despite the fact that they appeared intrinsically disordered by other measures. The hop-like intramolecular diffusion found in these proteins is suggested to define a class of functionality previously unrecognized for IDPs.
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Project description:While the crucial role of intrinsically disordered proteins (IDPs) in the cell cycle is now recognized, deciphering their molecular mode of action at the structural level still remains highly challenging and requires a combination of many biophysical approaches. Among them, small angle X-ray scattering (SAXS) has been extremely successful in the last decade and has become an indispensable technique for addressing many of the fundamental questions regarding the activities of IDPs. After introducing some experimental issues specific to IDPs and in relation to the latest technical developments, this article presents the interest of the theory of polymer physics to evaluate the flexibility of fully disordered proteins. The different strategies to obtain 3-dimensional models of IDPs, free in solution and associated in a complex, are then reviewed. Indeed, recent computational advances have made it possible to readily extract maximum information from the scattering curve with a special emphasis on highly flexible systems, such as multidomain proteins and IDPs. Furthermore, integrated computational approaches now enable the generation of ensembles of conformers to translate the unique flexible characteristics of IDPs by taking into consideration the constraints of more and more various complementary experiment. In particular, a combination of SAXS with high-resolution techniques, such as x-ray crystallography and NMR, allows us to provide reliable models and to gain unique structural insights about the protein over multiple structural scales. The latest neutron scattering experiments also promise new advances in the study of the conformational changes of macromolecules involving more complex systems.
| S-EPMC3394175 | biostudies-literature