Abundance of intrinsic disorder in protein associated with cardiovascular disease

Exploring Intrinsic Disorder in Human Synucleins and Associated Proteins

In this work, we explored the intrinsic disorder status of the three members of the synuclein family of proteins-α-, β-, and γ-synucleins-and showed that although all three human synucleins are highly disordered, the highest levels of disorder are observed in γ-synuclein. Our analysis of the peculiarities of the amino acid sequences and modeled 3D structures of the human synuclein family members revealed that the pathological mutations A30P, E46K, H50Q, A53T, and A53E associated with the early onset of Parkinson's disease caused some increase in the local disorder propensity of human α-synuclein. A comparative sequence-based analysis of the synuclein proteins from various evolutionary distant species and evaluation of their levels of intrinsic disorder using a set of commonly used bioinformatics tools revealed that, irrespective of their origin, all members of the synuclein family analyzed in this study were predicted to be highly disordered proteins, indicating that their intrinsically disordered nature represents an evolutionary conserved and therefore functionally important feature. A detailed functional disorder analysis of the proteins in the interactomes of the human synuclein family members utilizing a set of commonly used disorder analysis tools showed that the human α-synuclein interactome has relatively higher levels of intrinsic disorder as compared with the interactomes of human β- and γ- synucleins and revealed that, relative to the β- and γ-synuclein interactomes, α-synuclein interactors are involved in a much broader spectrum of highly diversified functional pathways. Although proteins interacting with three human synucleins were characterized by highly diversified functionalities, this analysis also revealed that the interactors of three human synucleins were involved in three common functional pathways, such as the synaptic vesicle cycle, serotonergic synapse, and retrograde endocannabinoid signaling. Taken together, these observations highlight the critical importance of the intrinsic disorder of human synucleins and their interactors in various neuronal processes.

Intrinsic Conformational Dynamics of Glycine and Alanine in Polarizable Molecular Dynamics Force Fields: Comparison to Spectroscopic Data

Molecular dynamics (MD) is a great tool for elucidating conformational dynamics of proteins and peptides in water at the atomistic level that often surpasses the level of detail available experimentally. Structure predictions, however, are limited by the accuracy of the underlying MD force field. This limitation is particularly stark in the case of intrinsically disordered peptides and proteins, which are characterized by solvent-accessible and disordered peptide regions and domains. Recent studies show that most additive MD force fields, including CHARMM36m, do not reproduce the intrinsic conformational distributions of guest amino acid residues x in cationic GxG peptides in water in line with experimental data. Positing that a lack of polarizability in additive MD force fields may be the culprit for the reported discrepancies, we here examine the conformational dynamics of guest glycine and alanine residues in cationic GxG peptides in water using two polarizable MD force fields, CHARMM Drude and AMOEBA. Our results indicate that while AMOEBA captures the experimental data better than CHARMM Drude, neither of the two polarizable force fields offers an improvement of the Ramachandran distributions of glycine and alanine residues in cationic GGG and GAG peptides, respectively, over CHARMM36m.

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins

Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA binding, it is important to identify which residues in the protein sequence bind to DNA and RNA. At present, there are few methods for specifically identifying the binding sites of disease-related protein-DNA and protein-RNA. In this study, so we combined four machine learning algorithms into an ensemble classifier (EPDRNA) to predict DNA and RNA binding sites in disease-related proteins. The dataset used in model was collated from UniProt and PDB database, and PSSM, physicochemical properties and amino acid type were used as features. The EPDRNA adopted soft voting and achieved the best AUC value of 0.73 at the DNA binding sites, and the best AUC value of 0.71 at the RNA binding sites in 10-fold cross validation in the training sets. In order to further verify the performance of the model, we assessed EPDRNA for the prediction of DNA-binding sites and the prediction of RNA-binding sites on the independent test dataset. The EPDRNA achieved 85% recall rate and 25% precision on the protein-DNA interaction independent test set, and achieved 82% recall rate and 27% precision on the protein-RNA interaction independent test set. The online EPDRNA webserver is freely available at http://www.s-bioinformatics.cn/epdrna .

Proteasome activation: A novel strategy for targeting undruggable intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are characterized by their inability to adopt well-defined tertiary structures under physiological conditions. Nonetheless, they often play pivotal roles in the progression of various diseases, including cancer, neurodegenerative disorders, and cardiovascular ailments. Owing to their inherent dynamism, conventional drug design approaches based on structural considerations encounter substantial challenges when applied to IDPs. Consequently, the pursuit of therapeutic interventions directed towards IDPs presents a complex endeavor. While there are indeed existing methodologies for targeting IDPs, they are encumbered by noteworthy constrains. Hence, there exists an imminent imperative to investigate more efficacious and universally applicable strategies for modulating IDPs. Here, we present an overview of the latest advancements in the research pertaining to IDPs, along with the indirect regulation approach involving the modulation of IDP degradation through proteasome. By comprehending these advancements in research, novel insights can be generated to facilitate the development of new drugs targeted at addressing the accumulation of IDPs in diverse pathological conditions.

MoRF_ESM: Prediction of MoRFs in disordered proteins based on a deep transformer protein language model

Molecular recognition features (MoRFs) are particular functional segments of disordered proteins, which play crucial roles in regulating the phase transition of membrane-less organelles and frequently serve as central sites in cellular interaction networks. As the association between disordered proteins and severe diseases continues to be discovered, identifying MoRFs has gained growing significance. Due to the limited number of experimentally validated MoRFs, the performance of existing MoRF's prediction algorithms is not good enough and still needs to be improved. In this research, we present a model named MoRF_ESM, which utilizes deep-learning protein representations to predict MoRFs in disordered proteins. This approach employs a pretrained ESM-2 protein language model to generate embedding representations of residues in the form of attention map matrices. These representations are combined with a self-learned TextCNN model for feature extraction and prediction. In addition, an averaging step was incorporated at the end of the MoRF_ESM model to refine the output and generate final prediction results. In comparison to other impressive methods on benchmark datasets, the MoRF_ESM approach demonstrates state-of-the-art performance, achieving [Formula: see text] higher AUC than other methods when tested on TEST1 and achieving [Formula: see text] higher AUC than other methods when tested on TEST2. These results imply that the combination of ESM-2 and TextCNN can effectively extract deep evolutionary features related to protein structure and function, along with capturing shallow pattern features located in protein sequences, and is well qualified for the prediction task of MoRFs. Given that ESM-2 is a highly versatile protein language model, the methodology proposed in this study can be readily applied to other tasks involving the classification of protein sequences.

The N-terminal intrinsically disordered region of Ncb5or docks with the cytochrome b5 core to form a helical motif that is of ancient origin

NADH cytochrome b5 oxidoreductase (Ncb5or) is a cytosolic ferric reductase implicated in diabetes and neurological conditions. Ncb5or comprises cytochrome b5 (b5 ) and cytochrome b5 reductase (b5 R) domains separated by a CHORD-Sgt1 (CS) linker domain. Ncb5or redox activity depends on proper inter-domain interactions to mediate electron transfer from NADH or NADPH via FAD to heme. While full-length human Ncb5or has proven resistant to crystallization, we have succeeded in obtaining high-resolution atomic structures of the b5 domain and a construct containing the CS and b5 R domains (CS/b5 R). Ncb5or also contains an N-terminal intrinsically disordered region of 50 residues that has no homologs in other protein families in animals but features a distinctive, conserved L34 MDWIRL40 motif also present in reduced lateral root formation (RLF) protein in rice and increased recombination center 21 in baker's yeast, all attaching to a b5 domain. After unsuccessful attempts at crystallizing a human Ncb5or construct comprising the N-terminal region naturally fused to the b5 domain, we were able to obtain a high-resolution atomic structure of a recombinant rice RLF construct corresponding to residues 25-129 of human Ncb5or (52% sequence identity; 74% similarity). The structure reveals Trp120 (corresponding to invariant Trp37 in Ncb5or) to be part of an 11-residue α-helix (S116 QMDWLKLTRT126 ) packing against two of the four helices in the b5 domain that surround heme (α2 and α5). The Trp120 side chain forms a network of interactions with the side chains of four highly conserved residues corresponding to Tyr85 and Tyr88 (α2), Cys124 (α5), and Leu47 in Ncb5or. Circular dichroism measurements of human Ncb5or fragments further support a key role of Trp37 in nucleating the formation of the N-terminal helix, whose location in the N/b5 module suggests a role in regulating the function of this multi-domain redox enzyme. This study revealed for the first time an ancient origin of a helical motif in the N/b5 module as reflected by its existence in a class of cytochrome b5 proteins from three kingdoms among eukaryotes.

The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome

Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed "the unfoldome". Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network.

DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.

Chaotic aging: intrinsically disordered proteins in aging-related processes

The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid-liquid phase separation (LLPS) probability, "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.

ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Intrinsically disordered proteins (IDPs) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT (Attention DisOrder PredicTor), a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue-level representations from Facebook's Evolutionary Scale Modeling library. The latter uses a database of nuclear magnetic resonance chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and a test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features that are relevant for the prediction performance and show that good performance can already be gained with <100 features. ADOPT is available as a stand-alone package at https://github.com/PeptoneLtd/ADOPT and as a web server at https://adopt.peptone.io/.

Enhancing the Conformational Stability of the cl-Par-4 Tumor Suppressor via Site-Directed Mutagenesis

Intrinsically disordered proteins play important roles in cell signaling, and dysregulation of these proteins is associated with several diseases. Prostate apoptosis response-4 (Par-4), an approximately 40 kilodalton proapoptotic tumor suppressor, is a predominantly intrinsically disordered protein whose downregulation has been observed in various cancers. The caspase-cleaved fragment of Par-4 (cl-Par-4) is active and plays a role in tumor suppression by inhibiting cell survival pathways. Here, we employed site-directed mutagenesis to create a cl-Par-4 point mutant (D313K). The expressed and purified D313K protein was characterized using biophysical techniques, and the results were compared to that of the wild-type (WT). We have previously demonstrated that WT cl-Par-4 attains a stable, compact, and helical conformation in the presence of a high level of salt at physiological pH. Here, we show that the D313K protein attains a similar conformation as the WT in the presence of salt, but at an approximately two times lower salt concentration. This establishes that the substitution of a basic residue for an acidic residue at position 313 alleviates inter-helical charge repulsion between dimer partners and helps to stabilize the structural conformation.

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories

The disordered nature of Intrinsically Disordered Proteins (IDPs) makes their structural ensembles particularly susceptible to changes in chemical environmental conditions, often leading to an alteration of their normal functions. A Radial Distribution Function (RDF) is considered a standard method for characterizing the chemical environment surrounding particles during atomistic simulations, commonly averaged over an entire or part of a trajectory. Given their high structural variability, such averaged information might not be reliable for IDPs. We introduce the Time-Resolved Radial Distribution Function (TRRDF), implemented in our open-source Python package SPEADI, which is able to characterize dynamic environments around IDPs. We use SPEADI to characterize the dynamic distribution of ions around the IDPs Alpha-Synuclein (AS) and Humanin (HN) from Molecular Dynamics (MD) simulations, and some of their selected mutants, showing that local ion-residue interactions play an important role in the structures and behaviors of IDPs.

Reciprocal regulatory balance within the CLEC16A-RNF41 mitophagy complex depends on an intrinsically disordered protein region

CLEC16A is an E3 ubiquitin ligase that regulates mitochondrial quality control through mitophagy and is associated with over 20 human diseases. CLEC16A forms a complex with another E3 ligase, RNF41, and a ubiquitin-specific peptidase, USP8; however, regions that regulate CLEC16A activity or the assembly of the tripartite mitophagy regulatory complex are unknown. Here, we report that CLEC16A contains an internal intrinsically disordered protein region (IDPR) that is crucial for CLEC16A function and turnover. IDPRs lack a fixed secondary structure and possess emerging, yet still equivocal roles in protein stability, interactions, and enzymatic activity. We find that the internal IDPR of CLEC16A is crucial for its degradation. CLEC16A turnover was promoted by RNF41, which binds and acts upon the internal IDPR to destabilize CLEC16A. Loss of this internal IDPR also destabilized the ubiquitin-dependent tripartite CLEC16A-RNF41-USP8 complex. Finally, the presence of an internal IDPR within CLEC16A was confirmed using NMR and circular dichroism spectroscopy. Together, our studies reveal that an IDPR is essential to control the reciprocal regulatory balance between CLEC16A and RNF41, which could be targeted to improve mitochondrial health in disease.

An intrinsically disordered protein region encoded by the human disease gene CLEC16A regulates mitophagy

CLEC16A regulates mitochondrial health through mitophagy and is associated with over 20 human diseases. However, the key structural and functional regions of CLEC16A, and their relevance for human disease, remain unknown. Here, we report that a disease-associated CLEC16A variant lacks a C-terminal intrinsically disordered protein region (IDPR) that is critical for mitochondrial quality control. IDPRs comprise nearly half of the human proteome, yet their mechanistic roles in human disease are poorly understood. Using carbon detect NMR, we find that the CLEC16A C terminus lacks secondary structure, validating the presence of an IDPR. Loss of the CLEC16A C-terminal IDPR in vivo impairs mitophagy, mitochondrial function, and glucose-stimulated insulin secretion, ultimately causing glucose intolerance. Deletion of the CLEC16A C-terminal IDPR increases CLEC16A ubiquitination and degradation, thus impairing assembly of the mitophagy regulatory machinery. Importantly, CLEC16A stability is dependent on proline bias within the C-terminal IDPR, but not amino acid sequence order or charge. Together, we elucidate how an IDPR in CLEC16A regulates mitophagy and implicate pathogenic human gene variants that disrupt IDPRs as novel contributors to diabetes and other CLEC16A-associated diseases.Abbreviations : CAS: carbon-detect amino-acid specific; IDPR: intrinsically disordered protein region; MEFs: mouse embryonic fibroblasts; NMR: nuclear magnetic resonance.

Modulation of self-organizing circuits at deforming membranes by intracellular and extracellular factors

Mechanical forces exerted to the plasma membrane induce cell shape changes. These transient shape changes trigger, among others, enrichment of curvature-sensitive molecules at deforming membrane sites. Strikingly, some curvature-sensing molecules not only detect membrane deformation but can also alter the amplitude of forces that caused to shape changes in the first place. This dual ability of sensing and inducing membrane deformation leads to the formation of curvature-dependent self-organizing signaling circuits. How these cell-autonomous circuits are affected by auxiliary parameters from inside and outside of the cell has remained largely elusive. Here, we explore how such factors modulate self-organization at the micro-scale and its emerging properties at the macroscale.

Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.

Rapid prediction and analysis of protein intrinsic disorder

Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high-efficiency web-based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome-scale structural bioinformatics and comparative genomics/proteomics. RIDAO integrates six established disorder predictors into a single, unified platform that reproduces the results of individual predictors with near-perfect fidelity. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare the efficiency and accessibility (i.e., ease of use) of RIDAO to five well-known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT-Disorder2. We show that RIDAO yields per-residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app.

Prediction of Intrinsic Disorder Using Rosetta ResidueDisorder and AlphaFold2

The combination of deep learning and sequence data has transformed protein structure prediction and modeling, evidenced in the success of AlphaFold (AF). For this reason, many methods have been developed to take advantage of this success in areas where inaccurate structural modeling may limit computational predictiveness. For example, many methods have been developed to predict protein intrinsic disorder from sequence, including our Rosetta ResidueDisorder (RRD) approach. Intrinsically disordered regions in proteins are parts of the sequence that do not form ordered, folded structures under typical physiological conditions. In the original implementation of RRD, Rosetta ab initio models were generated, and disordered regions were predicted based on residue scores (disordered residues typically exist in regions of unfavorable scores). In this work, we show that by (i) replacing the ab initio modeling with AF (using the same scoring and disorder assignment approach) and (ii) updating the score function, the predictiveness improved significantly. Residues were better ranked by the order/disorder, evidenced by an improvement in receiver operating characteristic area-under-the-curve from 0.69 to 0.78 on a large (229 protein) and balanced data set (relatively even ordered versus disordered residues). Finally, the binary prediction accuracy also improved from 62% to 74% on the same data set. Our results show that the combined AF-RRD approach was as good as or better than all existing methods by these metrics (AF-RRD had the highest prediction accuracy).

Intrinsically Disordered Proteins: Critical Components of the Wetware

Despite the wealth of knowledge gained about intrinsically disordered proteins (IDPs) since their discovery, there are several aspects that remain unexplored and, hence, poorly understood. A living cell is a complex adaptive system that can be described as a wetware─a metaphor used to describe the cell as a computer comprising both hardware and software and attuned to logic gates─capable of "making" decisions. In this focused Review, we discuss how IDPs, as critical components of the wetware, influence cell-fate decisions by wiring protein interaction networks to keep them minimally frustrated. Because IDPs lie between order and chaos, we explore the possibility that they can be modeled as attractors. Further, we discuss how the conformational dynamics of IDPs manifests itself as conformational noise, which can potentially amplify transcriptional noise to stochastically switch cellular phenotypes. Finally, we explore the potential role of IDPs in prebiotic evolution, in forming proteinaceous membrane-less organelles, in the origin of multicellularity, and in protein conformation-based transgenerational inheritance of acquired characteristics. Together, these ideas provide a new conceptual framework to discern how IDPs may perform critical biological functions despite their lack of structure.

Intrinsically Disordered Proteins: Perspective on COVID-19 Infection and Drug Discovery

Since the beginning of the COVID-19 pandemic caused by SARS-CoV-2, millions of patients have been diagnosed and many of them have died from the disease worldwide. The identification of novel therapeutic targets are of utmost significance for prevention and treatment of COVID-19. SARS-CoV-2 is a single-stranded RNA virus with a 30 kb genome packaged into a membrane-enveloped virion, transcribing several tens of proteins. The belief that the amino acid sequence of proteins determines their 3D structure which, in turn, determines their function has been a central principle of molecular biology for a long time. Recently, it has been increasingly realized, however, that there is a large group of proteins that lack a fixed or ordered 3D structure, yet they exhibit important biological activities─so-called intrinsically disordered proteins and protein regions (IDPs/IDRs). Disordered regions in viral proteins are generally associated with viral infectivity and pathogenicity because they endow the viral proteins the ability to easily and promiscuously bind to host proteins; therefore, the proteome of SARS-CoV-2 has been thoroughly examined for intrinsic disorder. It has been recognized that, in fact, the SARS-CoV-2 proteome exhibits significant levels of structural order, with only the nucleocapsid (N) structural protein and two of the nonstructural proteins being highly disordered. The spike (S) protein of SARS-CoV-2 exhibits significant levels of structural order, yet its predicted percentage of intrinsic disorder is still higher than that of the spike protein of SARS-CoV. Noteworthy, however, even though IDPs/IDRs are not common in the SARS-CoV-2 proteome, the existing ones play major roles in the functioning and virulence of the virus and are thus promising drug targets for rational antiviral drug design. Presented here is a COVID-19 perspective on the intrinsically disordered proteins, summarizing recent results on the SARS-CoV-2 proteome disorder features, their physiological and pathological relevance, and their prominence as prospective drug target sites.

Prediction of Intrinsically Disordered Proteins Using Machine Learning Based on Low Complexity Methods

Prediction of intrinsic disordered proteins is a hot area in the field of bio-information. Due to the high cost of evaluating the disordered regions of protein sequences using experimental methods, we used a low-complexity prediction scheme. Sequence complexity is used in this scheme to calculate five features for each residue of the protein sequence, including the Shannon entropy, the Topo-logical entropy, the Permutation entropy and the weighted average values of two propensities. Particularly, this is the first time that permutation entropy has been applied to the field of protein sequencing. In addition, in the data preprocessing stage, an appropriately sized sliding window and a comprehensive oversampling scheme can be used to improve the prediction performance of our scheme, and two ensemble learning algorithms are also used to verify the prediction results before and after. The results show that adding permutation entropy improves the performance of the prediction algorithm, in which the MCC value can be improved from the original 0.465 to 0.526 in our scheme, proving its universality. Finally, we compare the simulation results of our scheme with those of some existing schemes to demonstrate its effectiveness.

Regulatory Roles of the N-Terminal Intrinsically Disordered Region of Modular Src

Src, the prototype of Src family kinases (SFKs), is a modular protein consisting of SH4 (SH4) and unique (UD) domains in an N-terminal intrinsically disordered region (IDR), and SH3, SH2, and kinase (KD) folded domains conserved among SFKs. Src functions as a pleiotropic signaling hub in proliferating and post-mitotic cells, and it is related to cancer and neurological diseases. However, its regulatory mechanism is unclear because the existing canonical model is derived from crystallographic analyses of folded constructs lacking the IDR. This work reviews nuclear magnetic resonance analyses of partially structured lipid-binding segments in the flexible UD and the fuzzy intramolecular complex (FIMC) comprising IDR and SH3 domains, which interacts with lipid membranes and proteins. Furthermore, recently determined IDR-related Src characteristics are discussed, including dimerization, SH4/KD intramolecular fastener bundling of folded domains, and the sorting of adhesive structures. Finally, the modulatory roles of IDR phosphorylation in Src activities involving the FIMC are explored. The new regulatory roles of IDRs are integrated with the canonical model to elucidate the functions of full-length Src. This review presents new aspects of Src regulation, and provides a future direction for studies on the structure and function of Src, and their implications for pathological processes.

DeepIDP-2L: protein intrinsically disordered region prediction by combining convolutional attention network and hierarchical attention network

MOTIVATION

Intrinsically disordered regions (IDRs) are widely distributed in proteins. Accurate prediction of IDRs is critical for the protein structure and function analysis. The IDRs are divided into long disordered regions (LDRs) and short disordered regions (SDRs) according to their lengths. Previous studies have shown that LDRs and SDRs have different proprieties. However, the existing computational methods fail to extract different features for LDRs and SDRs separately. As a result, they achieve unstable performance on datasets with different ratios of LDRs and SDRs.

RESULTS

In this study, a two-layer predictor was proposed called DeepIDP-2L. In the first layer, two kinds of attention-based models are used to extract different features for LDRs and SDRs, respectively. The hierarchical attention network is used to capture the distribution pattern features of LDRs, and convolutional attention network is used to capture the local correlation features of SDRs. The second layer of DeepIDP-2L maps the feature extracted in the first layer into a new feature space. Convolutional network and bidirectional long short term memory are used to capture the local and long-range information for predicting both SDRs and LDRs. Experimental results show that DeepIDP-2L can achieve more stable performance than other exiting predictors on independent test sets with different ratios of SDRs and LDRs.

AVAILABILITY AND IMPLEMENTATION

For the convenience of most experimental scientists, a user-friendly and publicly accessible web-server for the new predictor has been established at http://bliulab.net/DeepIDP-2L/. It is anticipated that DeepIDP-2L will become a very useful tool for identification of intrinsically disordered regions.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Intrinsically Disordered Proteins MLLT3 (AF9) and MLLT1 (ENL) - Multimodal Transcriptional Switches With Roles in Normal Hematopoiesis, MLL Fusion Leukemia, and Kidney Cancer

AF9 (MLLT3) and ENL (MLLT1) are members of the YEATS family (named after the five proteins first shown to contain this domain: Yaf9, ENL, AF9, Taf14, Sas5) defined by the presence of a YEATS domain. The YEATS domain is an epigenetic reader that binds to acetylated and crotonylated lysines, unlike the bromodomain which can only bind to acetylated lysines. All members of this family have been shown to be components of various complexes with roles in chromatin remodeling, histone modification, histone variant deposition, and transcriptional regulation. MLLT3 is a critical regulator of hematopoiesis with a role in maintaining the hematopoietic stem or progenitor cell (HSPC) population. Approximately 10% of acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) patients harbor a translocation involving MLL (mixed lineage leukemia). In the context of MLL fusion patients with AML and ALL, MLL-AF9 and MLL-ENL fusions are observed in 34 and 31% of the patients, respectively. The intrinsically disordered C-terminal domain of MLLT3 (AHD, ANC1 homology domain) undergoes coupled binding and folding upon interaction with partner proteins AF4, DOT1L, BCOR, and CBX8. Backbone dynamics studies of the complexes suggest a role for dynamics in function. Inhibitors of the interaction of the intrinsically disordered AHD with partner proteins have been described, highlighting the feasibility of targeting intrinsically disordered regions. MLLT1 undergoes phase separation to enhance recruitment of the super elongation complex (SEC) and drive transcription. Mutations in MLLT1 observed in Wilms tumor patients enhance phase separation and transcription to drive an aberrant gene expression program.

Targeting disorders in unstructured and structured proteins in various diseases

Intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) are proteins and protein segments that usually do not acquire well-defined folded structures even under physiological conditions. They are abundantly present and challenge the "one sequence-one structure-one function" theory due to a lack of stable secondary and/or tertiary structure. Due to conformational flexibility, IDPs/IDPRs can bind with multiple interacting partners with high-specificity and low-affinity and perform essential biological functions associated with signalling, recognition and regulation. Mis-functioning and mis-regulation of IDPs and IDPRs causes disorder in disordered proteins and disordered protein segments which results in numerous human diseases, such as cancer, Parkinson's disease (PD), Alzheimer's disease (AD), diabetes, metabolic disorders, systemic disorders and so on. Due to the strong connection of IDPs/IDPRs with human diseases they are considered potentential targets for drug therapy. Since they disobey the "one sequence-one structure-one function" concept, IDPs/IDPRs are complex systems for drug targeting. This review summarises various protein disorder diseases and different methods for therapeutic targeting of disordered proteins/segments. Targeting IDPs/IDPRs for diseases will open up a new era of rational drug design and drug discovery.

Integrative structural dynamics probing of the conformational heterogeneity in synaptosomal-associated protein 25

SNAP-25 (synaptosomal-associated protein of 25 kDa) is a prototypical intrinsically disordered protein (IDP) that is unstructured by itself but forms coiled-coil helices in the SNARE complex. With high conformational heterogeneity, detailed structural dynamics of unbound SNAP-25 remain elusive. Here, we report an integrative method to probe the structural dynamics of SNAP-25 by combining replica-exchange discrete molecular dynamics (rxDMD) simulations and label-based experiments at ensemble and single-molecule levels. The rxDMD simulations systematically characterize the coil-to-molten globular transition and reconstruct structural ensemble consistent with prior ensemble experiments. Label-based experiments using Förster resonance energy transfer and double electron-electron resonance further probe the conformational dynamics of SNAP-25. Agreements between simulations and experiments under both ensemble and single-molecule conditions allow us to assign specific helix-coil transitions in SNAP-25 that occur in submillisecond timescales and potentially play a vital role in forming the SNARE complex. We expect that this integrative approach may help further our understanding of IDPs.

Sauna use as a lifestyle practice to extend healthspan

Sauna use, sometimes referred to as "sauna bathing," is characterized by short-term passive exposure to high temperatures, typically ranging from 45 °C to 100 °C (113 °F to 212 °F), depending on modality. This exposure elicits mild hyperthermia, inducing a thermoregulatory response involving neuroendocrine, cardiovascular, and cytoprotective mechanisms that work in a synergistic fashion in an attempt to maintain homeostasis. Repeated sauna use acclimates the body to heat and optimizes the body's response to future exposures, likely due to the biological phenomenon known as hormesis. In recent decades, sauna bathing has emerged as a probable means to extend healthspan, based on compelling data from observational, interventional, and mechanistic studies. Of particular interest are the findings from large, prospective, population-based cohort studies of health outcomes among sauna users that identified strong dose-dependent links between sauna use and reduced morbidity and mortality. This review presents an overview of sauna practices; elucidates the body's physiological response to heat stress and the molecular mechanisms that drive the response; enumerates the myriad health benefits associated with sauna use; and describes sauna use concerns.

What's in the BAGs? Intrinsic disorder angle of the multifunctionality of the members of a family of chaperone regulators

In humans, the family of Bcl-2 associated athanogene (BAG) proteins includes six members characterized by exceptional multifunctionality and engagement in the pathogenesis of various diseases. All of them are capable of interacting with a multitude of often unrelated binding partners. Such binding promiscuity and related functional and pathological multifacetedness cannot be explained or understood within the frames of the classical "one protein-one structure-one function" model, which also fails to explain the presence of multiple isoforms generated for BAG proteins by alternative splicing or alternative translation initiation and their extensive posttranslational modifications. However, all these mysteries can be solved by taking into account the intrinsic disorder phenomenon. In fact, high binding promiscuity and potential to participate in a broad spectrum of interactions with multiple binding partners, as well as a capability to be multifunctional and multipathogenic, are some of the characteristic features of intrinsically disordered proteins and intrinsically disordered protein regions. Such functional proteins or protein regions lacking unique tertiary structures constitute a cornerstone of the protein structure-function continuum concept. The aim of this paper is to provide an overview of the functional roles of human BAG proteins from the perspective of protein intrinsic disorder which will provide a means for understanding their binding promiscuity, multifunctionality, and relation to the pathogenesis of various diseases.

Isolation and Characterization of Human Colon Adenocarcinoma Stem-Like Cells Based on the Endogenous Expression of the Stem Markers

BACKGROUND

Cancer stem cells' (CSCs) self-maintenance is regulated via the pluripotency pathways promoting the most aggressive tumor phenotype. This study aimed to use the activity of these pathways for the CSCs' subpopulation enrichment and separating cells characterized by the OCT4 and SOX2 expression.

METHODS

To select and analyze CSCs, we used the SORE6x lentiviral reporter plasmid for viral transduction of colon adenocarcinoma cells. Additionally, we assessed cell chemoresistance, clonogenic, invasive and migratory activity and the data of mRNA-seq and intrinsic disorder predisposition protein analysis (IDPPA).

RESULTS

We obtained the line of CSC-like cells selected on the basis of the expression of the OCT4 and SOX2 stem cell factors. The enriched CSC-like subpopulation had increased chemoresistance as well as clonogenic and migration activities. The bioinformatic analysis of mRNA seq data identified the up-regulation of pluripotency, development, drug resistance and phototransduction pathways, and the downregulation of pathways related to proliferation, cell cycle, aging, and differentiation. IDPPA indicated that CSC-like cells are predisposed to increased intrinsic protein disorder.

CONCLUSION

The use of the SORE6x reporter construct for CSCs enrichment allows us to obtain CSC-like population that can be used as a model to search for the new prognostic factors and potential therapeutic targets for colon cancer treatment.

Integrating an Enhanced Sampling Method and Small-Angle X-Ray Scattering to Study Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) have been paid more and more attention over the past decades because they are involved in a multitude of crucial biological functions. Despite their functional importance, IDPs are generally difficult to investigate because they are very flexible and lack stable structures. Computer simulation may serve as a useful tool in studying IDPs. With the development of computer software and hardware, computational methods, such as molecular dynamics (MD) simulations, are popularly used. However, there is a sampling problem in MD simulations. In this work, this issue is investigated using an IDP called unique long region 11 (UL11), which is the conserved outer tegument component from herpes simplex virus 1. After choosing a proper force field and water model that is suitable for simulating IDPs, integrative modeling by combining an enhanced sampling method and experimental data like small-angle X-ray scattering (SAXS) is utilized to efficiently sample the conformations of UL11. The simulation results are in good agreement with experimental data. This work may provide a general protocol to study structural ensembles of IDPs.

IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions

Motivation

Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance.

Results

We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors.

Availability and implementation

The software is freely available at http://www.wdspdb.com/IDRMutPred.

Supplementary information

Supplementary data are available at Bioinformatics online.

IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell

Intrinsic disorder can be found in all proteomes of all kingdoms of life and in viruses, being particularly prevalent in the eukaryotes. We conduct a comprehensive analysis of the intrinsic disorder in the human proteins while mapping them into 24 compartments of the human cell. In agreement with previous studies, we show that human proteins are significantly enriched in disorder relative to a generic protein set that represents the protein universe. In fact, the fraction of proteins with long disordered regions and the average protein-level disorder content in the human proteome are about 3 times higher than in the protein universe. Furthermore, levels of intrinsic disorder in the majority of human subcellular compartments significantly exceed the average disorder content in the protein universe. Relative to the overall amount of disorder in the human proteome, proteins localized in the nucleus and cytoskeleton have significantly increased amounts of disorder, measured by both high disorder content and presence of multiple long intrinsically disordered regions. We empirically demonstrate that, on average, human proteins are assigned to 2.3 subcellular compartments, with proteins localized to few subcellular compartments being more disordered than the proteins that are localized to many compartments. Functionally, the disordered proteins localized in the most disorder-enriched subcellular compartments are primarily responsible for interactions with nucleic acids and protein partners. This is the first-time disorder is comprehensively mapped into the human cell. Our observations add a missing piece to the puzzle of functional disorder and its organization inside the cell.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Functions of short lifetime biological structures at large: the case of intrinsically disordered proteins

Although for more than a century a protein function was intimately associated with the presence of unique structure in a protein molecule, recent years witnessed a skyrocket rise of the appreciation of protein intrinsic disorder concept that emphasizes the importance of the biologically active proteins without ordered structures. In different proteins, the depth and breadth of disorder penetrance are different, generating an amusing spatiotemporal heterogeneity of intrinsically disordered proteins (IDPs) and intrinsically disordered protein region regions (IDPRs), which are typically described as highly dynamic ensembles of rapidly interconverting conformations (or a multitude of short lifetime structures). IDPs/IDPRs constitute a substantial part of protein kingdom and have unique functions complementary to functional repertoires of ordered proteins. They are recognized as interaction specialists and global controllers that play crucial roles in regulation of functions of their binding partners and in controlling large biological networks. IDPs/IDPRs are characterized by immense binding promiscuity and are able to use a broad spectrum of binding modes, often resulting in the formation of short lifetime complexes. In their turn, functions of IDPs and IDPRs are controlled by various means, such as numerous posttranslational modifications and alternative splicing. Some of the functions of IDPs/IDPRs are briefly considered in this review to shed some light on the biological roles of short-lived structures at large.

Intrinsic Disorder in Human Proteins Encoded by Core Duplicon Gene Families

Segmental duplications (i.e., highly homologous DNA fragments greater than 1 kb in length that are present within a genome at more than one site) are typically found in genome regions that are prone to rearrangements. A noticeable fraction of the human genome (∼5%) includes segmental duplications (or duplicons) that are assumed to play a number of vital roles in human evolution, human-specific adaptation, and genomic instability. Despite their importance for crucial events such as synaptogenesis, neuronal migration, and neocortical expansion, these segmental duplications continue to be rather poorly characterized. Of particular interest are the core duplicon gene (CDG) families, which are replicates sharing common "core" DNA among the randomly attached pieces and which expand along single chromosomes and might harbor newly acquired protein domains. Another important feature of proteins encoded by CDG families is their multifunctionality. Although it seems that these proteins might possess many characteristic features of intrinsically disordered proteins, to the best of our knowledge, a systematic investigation of the intrinsic disorder predisposition of the proteins encoded by core duplicon gene families has not been conducted yet. To fill this gap and to determine the degree to which these proteins might be affected by intrinsic disorder, we analyzed a set of human proteins encoded by the members of 10 core duplicon gene families, such as NBPF, RGPD, GUSBP, PMS2P, SPATA31, TRIM51, GOLGA8, NPIP, TBC1D3, and LRRC37. Our analysis revealed that the vast majority of these proteins are highly disordered, with their disordered regions often being utilized as means for the protein-protein interactions and/or targeted for numerous posttranslational modifications of different nature.

Orchestration of signaling by structural disorder in class 1 cytokine receptors

BACKGROUND

Class 1 cytokine receptors (C1CRs) are single-pass transmembrane proteins responsible for transmitting signals between the outside and the inside of cells. Remarkably, they orchestrate key biological processes such as proliferation, differentiation, immunity and growth through long disordered intracellular domains (ICDs), but without having intrinsic kinase activity. Despite these key roles, their characteristics remain rudimentarily understood.

METHODS

The current paper asks the question of why disorder has evolved to govern signaling of C1CRs by reviewing the literature in combination with new sequence and biophysical analyses of chain properties across the family.

RESULTS

We uncover that the C1CR-ICDs are fully disordered and brimming with SLiMs. Many of these short linear motifs (SLiMs) are overlapping, jointly signifying a complex regulation of interactions, including network rewiring by isoforms. The C1CR-ICDs have unique properties that distinguish them from most IDPs and we forward the perception that the C1CR-ICDs are far from simple strings with constitutively bound kinases. Rather, they carry both organizational and operational features left uncovered within their disorder, including mechanisms and complexities of regulatory functions.

CONCLUSIONS

Critically, the understanding of the fascinating ability of these long, completely disordered chains to orchestrate complex cellular signaling pathways is still in its infancy, and we urge a perceptional shift away from the current simplistic view towards uncovering their full functionalities and potential. Video abstract.

The morbid cutaneous anatomy of the human genome revealed by a bioinformatic approach

Computational approaches have been developed to prioritize candidate genes in disease gene identification. They are based on different pieces of evidences associating each gene with the given disease. In this study, 648 genes underlying genodermatoses have been compared to 1808 genes involved in other genetic diseases using a bioinformatic approach. These genes were studied at the structural, evolutionary and functional levels. Results show that genes underlying genodermatoses present longer CDS and have more exons. Significant differences were observed in nucleotide motif and amino-acid compositions. Evolutionary conservation analysis revealed that genodermatoses genes have less paralogs, more orthologs in Mouse and Dog and are less conserved. Functional analysis revealed that genodermatosis genes seem to be involved in immune system and skin layers. The Bayesian network model returned a rate of good classification of around 80%. This computational approach could help investigators working in the field of dermatology by prioritizing positional candidate genes for mutation screening.

Putative circumsporozoite protein (CSP) of Plasmodium vivax is considerably distinct from the well-known CSP and plays a role in the protein ubiquitination pathway

Amidst technical challenges which limit successful culture and genetic manipulation of P. vivax parasites, we used a computational approach to identify a critical target with evolutionary significance. The putative circumsporozoite protein on chromosome 13 of P. vivax (PvpuCSP)is distinct from the well-known vaccine candidate PfCSP. The aim of this study was to understand the role of PvpuCSP and its relatedness to the well-known CSP. The study revealed PvpuCSP as a membrane bound E3 ubiquitin ligase involved in ubiquitination. It has a species-specific tetra-peptide unit which is differentially repeated in various P. vivax strains. The PvpuCSP is different from CSP in terms of stage-specific expression and function. Since E3 ubiquitin ligases are known antimalarial drug targets targeting the proteasome pathway, PvpuCSP, with evolutionary connotation and a key role in orchestrating protein degradation in P. vivax, can be explored as a potential drug target.

•

PvpuCSP is predicted as E3 ubiquitin ligase, a part of ubiquitination pathway.

•

Tetra-peptide tandem repeat at C terminal of PvpuCSP is exclusive to P. vivax.

•

Moderately expressed during all parasitic stages in host and vector

•

Partially disordered protein with both structured domains and two distinct IDRs

•

A transmembrane protein with highly conserved functional domain across Apicomplexa

Japanese encephalitis virus - exploring the dark proteome and disorder-function paradigm

Japanese encephalitis virus (JEV) is one of the major causes of viral encephalitis all around the globe. Approximately 3 billion people in endemic areas are at risk of Japanese encephalitis. To develop a wholistic understanding of the viral proteome, it is important to investigate both its ordered and disordered proteins. However, the functional and structural significance of disordered regions in the JEV proteome has not been systematically investigated as of yet. To fill this gap, we used here a set of bioinformatics tools to analyze the JEV proteome for the predisposition of its proteins for intrinsic disorder and for the presence of the disorder-based binding regions (also known as molecular recognition features, MoRFs). We also analyzed all JEV proteins for the presence of the probable nucleic acid-binding (DNA and RNA) sites. The results of these computational studies are experimentally validated using JEV capsid protein as an illustrative example. In agreement with bioinformatic analysis, we found that the N-terminal region of the JEV capsid (residues 1-30) is intrinsically disordered. We showed that this region is characterized by the temperature response typical for highly disordered proteins. Furthermore, we have experimentally shown that this disordered N-terminal domain of a capsid protein has a noticeable 'gain-of-structure' potential. In addition, using DOPS liposomes, we demonstrated the presence of pronounced membrane-mediated conformational changes in the N-terminal region of JEV capsid. In our view, this disorder-centric analysis would be helpful for a better understanding of the JEV pathogenesis.

Intrinsic Disorder in Tetratricopeptide Repeat Proteins

Among the realm of repeat containing proteins that commonly serve as “scaffolds” promoting protein-protein interactions, there is a family of proteins containing between 2 and 20 tetratricopeptide repeats (TPRs), which are functional motifs consisting of 34 amino acids. The most distinguishing feature of TPR domains is their ability to stack continuously one upon the other, with these stacked repeats being able to affect interaction with binding partners either sequentially or in combination. It is known that many repeat-containing proteins are characterized by high levels of intrinsic disorder, and that many protein tandem repeats can be intrinsically disordered. Furthermore, it seems that TPR-containing proteins share many characteristics with hybrid proteins containing ordered domains and intrinsically disordered protein regions. However, there has not been a systematic analysis of the intrinsic disorder status of TPR proteins. To fill this gap, we analyzed 166 human TPR proteins to determine the degree to which proteins containing TPR motifs are affected by intrinsic disorder. Our analysis revealed that these proteins are characterized by different levels of intrinsic disorder and contain functional disordered regions that are utilized for protein-protein interactions and often serve as targets of various posttranslational modifications.

Expanding the understanding of the heterogeneous nature of melanoma with bioinformatics and disorder-based proteomics

The past few decades show that incidences of melanoma are on the rise. The risk associated with this disease is an interplay between genetic and host factors and sun exposure. While scientific progress in the treatment of melanoma is remarkable, additional research is needed to improve patient outcomes and to better understand the heterogenous nature of this disease. Fortunately, as the clinical community enters the era of "big data" and personalized medicine, the rise of bioinformatics that stems from recent advances in high throughout profiling of biological information offers potential for innovative treatment options. This study aims to provide an example of the usefulness of bioinformatics and disorder-based proteomics to identify the molecular pathway in melanoma, garner information on selected proteins from this pathway and uncover their intrinsically disordered proteins regions (IDPRs) and investigate functionality implicated in these IDPRs. The present study provides a new look at the melanoma heterogeneity and suggests that, in addition to the well-established genetic heterogeneity of melanoma, there is another level of heterogeneity that lies within the conformational ensembles that stem from intrinsic disorder in melanoma-related proteins. The hope is that these insights will inspire future drug discovery campaigns.

Well-Balanced Force Field ff03CMAP for Folded and Disordered Proteins

Molecular dynamics simulation as an important complement of experiment is widely used to study protein structures and functions. However, previous studies indicate that the current force fields cannot, simultaneously, provide accurate descriptions of folded proteins and intrinsically disordered proteins (IDPs). Therefore, a correction maps (CMAP)-optimized force field based on the Amber ff03 force field (termed ff03CMAP herein) was developed for a balanced sampling of folded proteins and IDPs. Extensive validations of short peptides, folded proteins, disordered proteins, and fast-folding proteins show that simulated chemical shifts, J-coupling constants, order parameters, and residual dipolar couplings (RDCs) with the ff03CMAP force field are in very good agreement with nuclear magnetic resonance measurements and are more accurate than other ff03-series force fields. The influence of solvent models was also investigated. It was found that the combination of ff03CMAP/TIP4P-Ew is suitable for folded proteins, and that of ff03CMAP/TIP4PD is better for disordered proteins. These findings confirm that the newly developed force field ff03CMAP can improve the balance of conformer sampling between folded proteins and IDPs.

Intrinsically disordered proteins and structured proteins with intrinsically disordered regions have different functional roles in the cell

Many studies about classification and the functional annotation of intrinsically disordered proteins (IDPs) are based on either the occurrence of long disordered regions or the fraction of disordered residues in the sequence. Taking into account both criteria we separate the human proteome, taken as a case study, into three variants of proteins: i) ordered proteins (ORDPs), ii) structured proteins with intrinsically disordered regions (IDPRs), and iii) intrinsically disordered proteins (IDPs). The focus of this work is on the different functional roles of IDPs and IDPRs, which up until now have been generally considered as a whole. Previous studies assigned a large set of functional roles to the general category of IDPs. We show here that IDPs and IDPRs have non-overlapping functional spectra, play different roles in human diseases, and deserve to be treated as distinct categories of proteins. IDPs enrich only a few classes, functions, and processes: nucleic acid binding proteins, chromatin binding proteins, transcription factors, and developmental processes. In contrast, IDPRs are spread over several functional protein classes and GO annotations which they partly share with ORDPs. As regards to diseases, we observe that IDPs enrich only cancer-related proteins, at variance with previous results reporting that IDPs are widespread also in cardiovascular and neurodegenerative pathologies. Overall, the operational separation of IDPRs from IDPs is relevant towards correct estimates of the occurrence of intrinsically disordered proteins in genome-wide studies and in the understanding of the functional spectra associated to different flavors of protein disorder.

A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction

Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Therefore, a comprehensive and updated review is highly required. In this regard, we give a review on the computational methods for intrinsically disordered protein and region prediction, especially focusing on the recent development in this field. These computational approaches are divided into four categories based on their methodologies, including physicochemical-based method, machine-learning-based method, template-based method and meta method. Furthermore, their advantages and disadvantages are also discussed. The performance of 40 state-of-the-art predictors is directly compared on the target proteins in the task of disordered region prediction in the 10th Critical Assessment of protein Structure Prediction. A more comprehensive performance comparison of 45 different predictors is conducted based on seven widely used benchmark data sets. Finally, some open problems and perspectives are discussed.

Identification of Intrinsically Disordered Proteins and Regions by Length-Dependent Predictors Based on Conditional Random Fields

Accurate identification of intrinsically disordered proteins/regions (IDPs/IDRs) is critical for predicting protein structure and function. Previous studies have shown that IDRs of different lengths have different characteristics, and several classification-based predictors have been proposed for predicting different types of IDRs. Compared with these classification-based predictors, the previously proposed predictor IDP-CRF exhibits state-of-the-art performance for predicting IDPs/IDRs, which is a sequence labeling model based on conditional random fields (CRFs). Motivated by these methods, we propose a predictor called IDP-FSP, which is an ensemble of three CRF-based predictors called IDP-FSP-L, IDP-FSP-S, and IDP-FSP-G. These three predictors are specially designed to predict long, short, and generic disordered regions, respectively, and they are constructed based on different features. To the best of our knowledge, IDP-FSP is the first predictor that combines a sequence labeling algorithm with IDRs of different lengths. Experimental results using two independent test datasets show that IDP-FSP achieves better or at least comparable predictive performance with 26 existing state-of-the-art methods in this field, proving the effectiveness of IDP-FSP.