Targeting intrinsically disordered proteins in rational drug discovery

PredIDR2: Improving accuracy of protein intrinsic disorder prediction by updating deep convolutional neural network and supplementing DisProt data

Intrinsically disordered proteins (IDPs) or regions (IDRs) are widespread in proteomes, and involved in several important biological processes and implicated in many diseases. Many computational methods for IDR prediction are being developed to decrease the gap between the low speed of experimental determination of annotated proteins and the rapid increase of non-annotated proteins, and their performances are blindly tested by the community-driven experiment, the Critical Assessment of protein Intrinsic Disorder (CAID). In this paper, we developed PredIDR2 series, an updated version of PredIDR tested in CAID2 in order to accurately predict intrinsically disordered regions from protein sequence. It includes four methods depending on the input features and the producing mode of the negative samples of the training set. PredIDR2 series (AUC_ROC = 0.952) perform remarkably better than our previous PredIDR (AUC_ROC = 0.933) for Disorder-PDB dataset of CAID2, which seems to be mainly attributed to the introduction of a new deep convolutional neural network and the augmentation of the training data, especially from DisProt database. PredIDR2 series outperform the state-of-the-art IDR prediction methods participated in CAID2 in terms of AUC_ROC, AUC_PR and DC_mae and belong to the seven top-performing methods in terms of MCC. PredIDR2 series can be freely used through the CAID Prediction Portal available at https://caid.idpcentral.org/portal or downloaded as a Singularity container from https://biocomputingup.it/shared/caid-predictors/.

The Intrinsically Disordered Region of HBx and Virus-Host Interactions: Uncovering New Therapeutic Approaches for HBV and Cancer

Human viral infections remain a significant global health challenge, contributing to a substantial number of cancer cases worldwide. Among them, infections with oncoviruses such as hepatitis B virus (HBV) and hepatitis C virus (HCV) are key drivers of hepatocellular carcinoma (HCC). Despite the availability of an effective HBV vaccine since the 1980s, millions remain chronically infected due to the persistence of covalently closed circular DNA (cccDNA) as a reservoir in hepatocytes. Current antiviral therapies, including nucleos(t)ide analogs and interferon, effectively suppress viral replication but fail to eliminate cccDNA, underscoring the urgent need for innovative therapeutic strategies. Direct-acting antiviral agents (DAAs), which have revolutionized HCV treatment with high cure rates, offer a promising model for HBV therapy. A particularly attractive target is the intrinsically disordered region (IDR) of the HBx protein, which regulates cccDNA transcription, viral replication, and oncogenesis by interacting with key host proteins. DAAs targeting these interactions could inhibit viral persistence, suppress oncogenic signaling, and overcome treatment resistance. This review highlights the potential of HBx-directed DAAs to complement existing therapies, offering renewed hope for a functional HBV cure and reduced cancer risk.

AIUPred - Binding: Energy Embedding to Identify Disordered Binding Regions

Intrinsically disordered regions (IDRs) play critical roles in various cellular processes, often mediating interactions through disordered binding regions that transition to ordered states. Experimental characterization of these functional regions is highly challenging, underscoring the need for fast and accurate computational tools. Despite their importance, predicting disordered binding regions remains a significant challenge due to limitations in existing datasets and methodologies. In this study, we introduce AIUPred-binding, a novel prediction tool leveraging a high dimensional mathematical representation of structural energies - we call energy embedding - and pathogenicity scores from AlphaMissense. By employing a transfer learning approach, AIUPred-binding demonstrates improved accuracy in identifying functional sites within IDRs. Our results highlight the tool's ability to discern subtle features within disordered regions, addressing biases and other challenges associated with manually curated datasets. We present AIUPred-binding integrated into the AIUPred web framework as a versatile and efficient resource for understanding the functional roles of IDRs. AIUPred-binding is freely accessible at https://aiupred.elte.hu.

Peptide Inhibitor Assay for Allocating Functionally Important Accessible Sites Throughout a Protein Chain: Restriction Endonuclease EcoRI as a Model Protein System

Functionally important amino acid sequences in proteins are often located at multiple sites. Three-dimensional structural analysis and site-directed mutagenesis may be performed to allocate functional sites for understanding structure‒function relationships and for developing novel inhibitory drugs. However, such methods are too demanding to comprehensively cover potential functional sites throughout a protein chain. Here, a peptide inhibitor assay (PIA) was devised to allocate functionally important accessible sites in proteins. This simple method presumes that protein‒ligand interactions, intramolecular interactions, and dimerization interactions can be partially inhibited by high concentrations of competitive "endogenous" peptides of the protein of interest. Focusing on the restriction endonuclease EcoRI as a model protein system, many endogenous peptides (6mer-14mer) were synthesized, covering the entire EcoRI protein chain. Some of them were highly inhibitory, but interestingly, the nine most effective peptides were located outside the active sites, with the exception of one. Relatively long peptides with aromatic residues (F, H, W, and Y) corresponding to secondary structures were generally effective. Because synthetic peptides are flexible enough to change length and amino acid residues, this method may be useful for quickly and comprehensively understanding structure‒function relationships and developing novel drugs or epitopes for neutralizing antibodies.

IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models

Intrinsically disordered Proteins (IDPs) constitute a large and structureless class of proteins with significant functions. The existence of IDPs challenges the conventional notion that the biological functions of proteins rely on their three-dimensional structures. Despite lacking well-defined spatial arrangements, they exhibit diverse biological functions, influencing cellular processes and shedding light on disease mechanisms. However, it is expensive to run experiments or simulations to characterize this class of proteins. Consequently, we designed an ML model that relies solely on amino acid sequences. In this study, we introduce the IDP-Bert model, a deep-learning architecture leveraging Transformers and Protein Language Models to map sequences directly to IDP properties. Our experiments demonstrate accurate predictions of IDP properties, including Radius of Gyration, end-to-end Decorrelation Time, and Heat Capacity.

Selection of Nucleotide-Encoded Mass Libraries of Macrocyclic Peptides for Inaccessible Drug Targets

Technological advances and breakthrough developments in the pharmaceutical field are knocking at the door of the "undruggable" fortress with increasing insistence. Notably, the 21st century has seen the emergence of macrocyclic compounds, among which cyclic peptides are of particular interest. This new class of potential drug candidates occupies the vast chemical space between classic small-molecule drugs and larger protein-based therapeutics, such as antibodies. As research advances toward clinical targets that have long been considered inaccessible, macrocyclic peptides are well-suited to tackle these challenges in a post-rule of 5 pharmaceutical landscape. Facilitating their discovery is an arsenal of high-throughput screening methods that exploit massive randomized libraries of genetically encoded compounds. These techniques benefit from the incorporation of non-natural moieties, such as non- proteinogenic amino acids or stabilizing hydrocarbon staples. Exploiting these features for the strategic architectural design of macrocyclic peptides has the potential to tackle challenging targets such as protein-protein interactions, which have long resisted research efforts. This Review summarizes the basic principles and recent developments of the main high-throughput techniques for the discovery of macrocyclic peptides and focuses on their specific deployment for targeting undruggable space. A particular focus is placed on the development of new design guidelines and principles for the cyclization and structural stabilization of cyclic peptides and the resulting success stories achieved against well-known inaccessible drug targets.

How to drug a cloud? Targeting intrinsically disordered proteins

Biologically active proteins/regions without stable structure (i.e., intrinsically disordered proteins and regions (IDPs and IDRs)) are commonly found in all proteomes. They have a unique functional repertoire that complements the functionalities of ordered proteins and domains. IDPs/IDRs are multifunctional promiscuous binders capable of folding at interaction with specific binding partners on a template- or context-dependent manner, many of which undergo liquid-liquid phase separation, leading to the formation of membrane-less organelles and biomolecular condensates. Many of them are frequently related to the pathogenesis of various human diseases. All this defines IDPs/IDRs as attractive targets for the development of novel drugs. However, their lack of unique structures, multifunctionality, binding promiscuity, and involvement in unusual modes of action preclude direct use of traditional structure-based drug design approaches for targeting IDPs/IDRs, and make disorder-based drug discovery for these "protein clouds" challenging. Despite all these complexities there is continuing progress in the design of small molecules affecting IDPs/IDRs. This article describes the major structural features of IDPs/IDRs and the peculiarities of the disorder-based functionality. It also discusses the roles of IDPs/IDRs in various pathologies, and shows why the approaches elaborated for finding drugs targeting ordered proteins cannot be directly used for the intrinsic disorder-based drug design, and introduces some novel methodologies suitable for these purposes. Finally, it emphasizes that regardless of their multifunctionality, binding promiscuity, lack of unique structures, and highly dynamic nature, "protein clouds" are principally druggable. Significance Statement Intrinsically disordered proteins and regions are highly abundant in nature, have multiple important biological functions, are commonly involved in the pathogenesis of a multitude of human diseases, and are therefore considered as very attractive drug targets. Although dealing with these unstructured multifunctional protein/regions is a challenging task, multiple innovative approaches have been designed to target them by small molecules.

When an underdog becomes a major player: the role of protein structural disorder in the Atg8 conjugation system

The noncanonical ubiquitin-like conjugation cascade involving the E1 (Atg7), E2 (Atg3, Atg10), and E3 (Atg12-Atg5-Atg16 complex) enzymes is essential for incorporation of Atg8 into the growing phagophore via covalent linkage to PE. This process is an indispensable step in autophagy. Atg8 and E1-E3 enzymes are the first subset from the core autophagy protein machinery structures that were investigated in earlier studies by crystallographic analyses of globular domains. However, research over the past decade shows that many important functions in the conjugation machinery are mediated by intrinsically disordered protein regions (IDPRs) - parts of the protein that do not adopt a stable secondary or tertiary structure, which are inherently dynamic and well suited for protein-membrane interactions but are invisible in protein crystals. Here, we summarize earlier and recent findings on the autophagy conjugation machinery by focusing on the IDPRs. This summary reveals that IDPRs, originally considered dispensable, are in fact major players and a driving force in the function of the autophagy conjugation system. Abbreviation: AD, activation domain of Atg7; AH, amphipathic helix; AIM, Atg8-family interacting motif; CL, catalytic loop (of Atg7); CTD, C-terminal domain; FR, flexible region (of Atg3 or Atg10); GUV, giant unilammelar vesicles; HR, handle region (of Atg3); IDPR, intrinsically disordered protein region; IDPs: intrinsically disordered proteins; LIR, LC3-interacting region; NHD: N-terminal helical domain; NMR, nuclear magnetic resonance; PE, phosphatidylethanolamine; UBL, ubiquitin like.

A novel gain-of-function phosphorylation site modulates PTPN22 inhibition of TCR signaling

Protein tyrosine phosphatase nonreceptor type 22 (PTPN22) is encoded by a major autoimmunity gene and is a known inhibitor of T cell receptor (TCR) signaling and drug target for cancer immunotherapy. However, little is known about PTPN22 posttranslational regulation. Here, we characterize a phosphorylation site at Ser325 situated C terminal to the catalytic domain of PTPN22 and its roles in altering protein function. In human T cells, Ser325 is phosphorylated by glycogen synthase kinase-3 (GSK3) following TCR stimulation, which promotes its TCR-inhibitory activity. Signaling through the major TCR-dependent pathway under PTPN22 control was enhanced by CRISPR/Cas9-mediated suppression of Ser325 phosphorylation and inhibited by mimicking it via glutamic acid substitution. Global phospho-mass spectrometry showed Ser325 phosphorylation state alters downstream transcriptional activity through enrichment of Swi3p, Rsc8p, and Moira domain binding proteins, and next-generation sequencing revealed it differentially regulates the expression of chemokines and T cell activation pathways. Moreover, in vitro kinetic data suggest the modulation of activity depends on a cellular context. Finally, we begin to address the structural and mechanistic basis for the influence of Ser325 phosphorylation on the protein's properties by deuterium exchange mass spectrometry and NMR spectroscopy. In conclusion, this study explores the function of a novel phosphorylation site of PTPN22 that is involved in complex regulation of TCR signaling and provides details that might inform the future development of allosteric modulators of PTPN22.

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

Physiology and pharmacological targeting of phase separation

Liquid-liquid phase separation (LLPS) in biology describes a process by which proteins form membraneless condensates within a cellular compartment when conditions are met, including the concentration and posttranslational modifications of the protein components, the condition of the aqueous solution (pH, ionic strength, pressure, and temperature), and the existence of assisting factors (such as RNAs or other proteins). In these supramolecular liquid droplet-like inclusion bodies, molecules are held together through weak intermolecular and/or intramolecular interactions. With the aid of LLPS, cells can assemble functional sub-units within a given cellular compartment by enriching or excluding specific factors, modulating cellular function, and rapidly responding to environmental or physiological cues. Hence, LLPS is emerging as an important means to regulate biology and physiology. Yet, excessive inclusion body formation by, for instance, higher-than-normal concentrations or mutant forms of the protein components could result in the conversion from dynamic liquid condensates into more rigid gel- or solid-like aggregates, leading to the disruption of the organelle's function followed by the development of human disorders like neurodegenerative diseases. In summary, well-controlled formation and de-formation of LLPS is critical for normal biology and physiology from single cells to individual organisms, whereas abnormal LLPS is involved in the pathophysiology of human diseases. In turn, targeting these aggregates or their formation represents a promising approach in treating diseases driven by abnormal LLPS including those neurodegenerative diseases that lack effective therapies.

Characterization of Intrinsically Disordered Proteins in Healthy and Diseased States by Nuclear Magnetic Resonance

INTRODUCTION

Intrinsically Disordered Proteins (IDPs) are active in different cellular procedures like ordered assembly of chromatin and ribosomes, interaction with membrane, protein, and ligand binding, molecular recognition, binding, and transportation via nuclear pores, microfilaments and microtubules process and disassembly, protein functions, RNA chaperone, and nucleic acid binding, modulation of the central dogma, cell cycle, and other cellular activities, post-translational qualification and substitute splicing, and flexible entropic linker and management of signaling pathways.

METHODS

The intrinsic disorder is a precise structural characteristic that permits IDPs/IDPRs to be involved in both one-to-many and many-to-one signaling. IDPs/IDPRs also exert some dynamical and structural ordering, being much less constrained in their activities than folded proteins. Nuclear magnetic resonance (NMR) spectroscopy is a major technique for the characterization of IDPs, and it can be used for dynamic and structural studies of IDPs.

RESULTS AND CONCLUSION

This review was carried out to discuss intrinsically disordered proteins and their different goals, as well as the importance and effectiveness of NMR in characterizing intrinsically disordered proteins in healthy and diseased states.

The Intrinsically Disordered N Terminus in Atg12 from Yeast Is Necessary for the Functional Structure of the Protein

The Atg12 protein in yeast is an indispensable polypeptide in the highly conserved ubiquitin-like conjugation system operating in the macroautophagy/autophagy pathway. Atg12 is covalently conjugated to Atg5 through the action of Atg7 and Atg10; the Atg12-Atg5 conjugate binds Atg16 to form an E3 ligase that functions in a separate conjugation pathway involving Atg8. Atg12 is comprised of a ubiquitin-like (UBL) domain preceded at the N terminus by an intrinsically disordered protein region (IDPR), a domain that comprises a major portion of the protein but remains elusive in its conformation and function. Here, we show that the IDPR in unconjugated Atg12 is positioned in proximity to the UBL domain, a configuration that is important for the functional structure of the protein. A major deletion in the IDPR disrupts intactness of the UBL domain at the unconjugated C terminus, and a mutation in the predicted α0 helix in the IDPR prevents Atg12 from binding to Atg7 and Atg10, which ultimately affects the protein function in the ubiquitin-like conjugation cascade. These findings provide evidence that the IDPR is an indispensable part of the Atg12 protein from yeast.

Intrinsic disorder may drive the interaction of PROS1 and MERTK in uveal melanoma

BACKGROUND

Class 2 uveal melanomas are associated with the inactivation of the BRCA1 ((breast cancer type 1 susceptibility protein)-associated protein 1 (BAP1)) gene. Inactivation of BAP1 promotes the upregulation of vitamin K-dependent protein S (PROS1), which interacts with the tyrosine-protein kinase Mer (MERTK) receptor on M2 macrophages to induce an immunosuppressive environment.

METHODS

We simulated the interaction of PROS1 with MERTK with ColabFold. We evaluated PROS1 and MERTK for the presence of intrinsically disordered protein regions (IDPRs) and disorder-to-order (DOT) regions to understand their protein-protein interaction (PPI). We first evaluated the structure of each protein with AlphaFold. We then analyzed specific sequence-based features of the PROS1 and MERTK with a suite of bioinformatics tools.

RESULTS

With high-resolution, moderate confidence, we successfully modeled the interaction between PROS1 and MERTK (predicted local distance difference test score (pDLLT) = 70.68). Our structural analysis qualitatively demonstrated IDPRs (i.e., spaghetti-like entities) in PROS1 and MERK. PROS1 was 23.37 % disordered, and MERTK was 23.09 % disordered, classifying them as moderately disordered and flexible proteins. PROS1 was significantly enriched in cysteine, the most order-promoting residue (p-value <0.05). Our IUPred analysis demonstrated that there are two disorder-to-order transition (DOT) regions in PROS1. MERTK was significantly enriched in proline, the most disorder-promoting residue (p-value <0.05), but did not contain DOT regions. Our STRING analysis demonstrated that the PPI network between PROS1 and MERTK is more complex than their assumed one-to-one binding (p-value <2.0 × 10-6).

CONCLUSION

Our findings present a novel prediction for the interaction between PROS1 and MERTK. Our findings show that PROS1 and MERTK contain elements of intrinsic disorder. PROS1 has two DOT regions that are attractive immunotherapy targets. We recommend that IDPRs and DOT regions found in PROS1 and MERTK should be considered when developing immunotherapies targeting this PPI.

Ultrasmall Gold Nanoparticles as Clients of Biomolecular Condensates

Liquid-liquid phase separation (LLPS) of biopolymers to form condensates is a widespread phenomenon in living cells. Agents that target or alter condensation can help uncover elusive physiological and pathological mechanisms. Owing to their unique material properties and modes of interaction with biomolecules, nanoparticles represent attractive condensate-targeting agents. Our work focused on elucidating the interaction between ultrasmall gold nanoparticles (usGNPs) and diverse types of condensates of tau, a representative phase-separating protein associated with neurodegenerative disorders. usGNPs attract considerable interest in the biomedical community due to unique features, including emergent optical properties and good cell penetration. We explored the interaction of usGNPs with reconstituted self-condensates of tau, two-component tau/polyanion and three-component tau/RNA/alpha-synuclein coacervates. The usGNPs were found to concentrate into condensed liquid droplets, consistent with the formation of dynamic client (nanoparticle) - scaffold (tau) interactions, and were observable thanks to their intrinsic luminescence. Furthermore, usGNPs were capable to promote LLPS of a protein domain which is unable to phase separate on its own. Our study demonstrates the ability of usGNPs to interact with and illuminate protein condensates. We anticipate that nanoparticles will have broad applicability as nanotracers to interrogate phase separation, and as nanoactuators controlling the formation and dissolution of condensates.

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/.

Dynamics-Based Regulatory Switches of Type II Antitoxins: Insights into New Antimicrobial Discovery

Type II toxin-antitoxin (TA) modules are prevalent in prokaryotes and are involved in cell maintenance and survival under harsh environmental conditions, including nutrient deficiency, antibiotic treatment, and human immune responses. Typically, the type II TA system consists of two protein components: a toxin that inhibits an essential cellular process and an antitoxin that neutralizes its toxicity. Antitoxins of type II TA modules typically contain the structured DNA-binding domain responsible for TA transcription repression and an intrinsically disordered region (IDR) at the C-terminus that directly binds to and neutralizes the toxin. Recently accumulated data have suggested that the antitoxin's IDRs exhibit variable degrees of preexisting helical conformations that stabilize upon binding to the corresponding toxin or operator DNA and function as a central hub in regulatory protein interaction networks of the type II TA system. However, the biological and pathogenic functions of the antitoxin's IDRs have not been well discussed compared with those of IDRs from the eukaryotic proteome. Here, we focus on the current state of knowledge about the versatile roles of IDRs of type II antitoxins in TA regulation and provide insights into the discovery of new antibiotic candidates that induce toxin activation/reactivation and cell death by modulating the regulatory dynamics or allostery of the antitoxin.

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

Protein intrinsic disorder is increasingly recognized for its biological and disease-driven functions. However, it represents significant challenges for biophysical studies due to its high conformational flexibility. In addressing these challenges, we highlight the complementary and distinct capabilities of a range of experimental and computational methods and further describe integrative strategies available for combining these techniques. Integrative biophysics methods provide valuable insights into the sequence–structure–function relationship of disordered proteins, setting the stage for protein intrinsic disorder to become a promising target for drug discovery. Finally, we briefly summarize recent advances in the development of new small molecule inhibitors targeting the disordered N-terminal domains of three vital transcription factors.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

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.

Intrinsic Disorder in BAP1 and Its Association with Uveal Melanoma

Background: Specific subvariants of uveal melanoma (UM) are associated with increased rates of metastasis compared to other subvariants. BRCA1 (BReast CAncer gene 1)-associated protein-1 (BAP1) is encoded by a gene that has been linked to aggressive behavior in UM. Methods: We evaluated BAP1 for the presence of intrinsically disordered protein regions (IDPRs) and its protein−protein interactions (PPI). We evaluated specific sequence-based features of the BAP1 protein using a set of bioinformatic databases, predictors, and algorithms. Results: We show that BAP1’s structure contains extensive IDPRs as it is highly enriched in proline residues (the most disordered amino acid; p-value < 0.05), the average percent of predicted disordered residues (PPDR) was 57.34%, and contains 9 disorder-based binding sites (ie. molecular recognition features (MoRFs)). BAP1’s intrinsic disorder allows it to engage in a complex PPI network with at least 49 partners (p-value < 1.0 × 10−16). Conclusion: These findings show that BAP1 contains IDPRs and an intricate PPI network. Mutations in UM that are associated with the BAP1 gene may alter the function of the IDPRs embedded into its structure. These findings develop the understanding of UM and may provide a target for potential novel therapies to treat this aggressive neoplasm.

BoMiProt 2.0: An update of the bovine milk protein database

Milk is a biofluid with various functions, containing carbohydrates, lipids, proteins, vitamins, and minerals. Owing to its importance and availability of vast proteomics information, our research group designed a database for bovine milk proteins (N = 3159) containing the primary and secondary information called BoMiProt. Due to the gaining interest and intensively published literature in the last three years, BoMiProt has been upgraded with newer identified proteins (N = 7459) from peer-reviewed journals, significantly expanding the database from different milk fractions (e.g., whey, fat globule membranes, and exosomes). Additionally, class, architecture, topology, and homology, structural classification of proteins, known and predicted disorder, predicted transmembrane helices, and structures have been included. Each protein entry in the database is thoroughly cross-referenced, including 1392 BoMiProt defined proteins provided with secondary information, such as protein function, biochemical properties, post-translational modifications, significance in milk, domains, fold, AlphaFold predicted models and crystal structures. The proteome data in the database can be retrieved using several search parameters using protein name, accession IDs, and FASTA sequence. Overall, BoMiProt represents an extensive compilation of newer proteins, including structural, functional, and hierarchical information, to help researchers better understand mammary gland pathophysiology, including their potential application in improving the nutritional quality of dairy products.

Camouflaged Fluorescent Silica Nanoparticles Target Aggregates and Condensates of the Amyloidogenic Protein Tau

Intrinsically disordered proteins (IDPs) are increasingly found to be associated with irreversible neurodegenerative disorders. The protein tau is a prototypical IDP whose abnormal aggregation into insoluble filaments is a major hallmark of Alzheimer's disease. The view has emerged that aggregation may proceed via alternative pathways involving oligomeric intermediates or phase-separated liquid droplets. Nanoparticles (NPs) offer significant potential for probing the mechanisms of protein fibrillation and may be capable of redirecting conformational transitions. Here, we camouflaged dye-doped silica NPs through functionalization with tau molecules to impart them the ability to associate with protein assemblies such as aggregates or condensates. The prepared NP-tau conjugates showed little influence on the aggregation kinetics and morphology of filamentous aggregates of tau but were found to associate with the filaments. Moreover, NP-tau conjugates were recruited and concentrated into polyanion-induced condensates of tau, driven by multivalent electrostatic interactions, thereby illuminating liquid droplets and their time-dependent transformation, as observed by fluorescence microscopy. NP-tau conjugates were capable of entering human neuroglioma cells and were not cytotoxic. Hence, we propose that NP-tau conjugates could serve as nanotracers for in vitro and in-cell studies to target and visualize tau assemblies and condensates, contributing to an explanation for the molecular mechanisms of abnormal protein aggregation.

Integrative Conformational Ensembles of Sic1 Using Different Initial Pools and Optimization Methods

Intrinsically disordered proteins play key roles in regulatory protein interactions, but their detailed structural characterization remains challenging. Here we calculate and compare conformational ensembles for the disordered protein Sic1 from yeast, starting from initial ensembles that were generated either by statistical sampling of the conformational landscape, or by molecular dynamics simulations. Two popular, yet contrasting optimization methods were used, ENSEMBLE and Bayesian Maximum Entropy, to achieve agreement with experimental data from nuclear magnetic resonance, small-angle X-ray scattering and single-molecule Förster resonance energy transfer. The comparative analysis of the optimized ensembles, including secondary structure propensity, inter-residue contact maps, and the distributions of hydrogen bond and pi interactions, revealed the importance of the physics-based generation of initial ensembles. The analysis also provides insights into designing new experiments that report on the least restrained features among the optimized ensembles. Overall, differences between ensembles optimized from different priors were greater than when using the same prior with different optimization methods. Generating increasingly accurate, reliable and experimentally validated ensembles for disordered proteins is an important step towards a mechanistic understanding of their biological function and involvement in various diseases.

Design, Synthesis, and Biological Evaluation of Potent 20S Proteasome Activators for the Potential Treatment of α-Synucleinopathies

While neurodegenerative diseases affect millions of patients worldwide, there are insufficient available therapeutics to halt or slow down the progression of these diseases. A key pathological feature of several neurodegenerative diseases is the oligomerization and aggregation of specific intrinsically disordered proteins (IDPs) creating neuronal deposits, such as Lewy bodies in Parkinson's disease. Clearance of these pathogenic, aggregation-prone IDPs is mediated by the 20S isoform of the human proteasome. Thus, enhancing the 20S proteasome-mediated proteolysis could be a very useful therapeutic pathway to prevent neurotoxicity. Here, we report the successful development of sub-microM 20S proteasome activators based on a phenothiazine scaffold. This class of compounds prevented the accumulation of pathologically relevant IDPs, such as the pathogenic A53T mutated α-synuclein, in vitro and in mammalian cell lines.

Insights into Membrane Curvature Sensing and Membrane Remodeling by Intrinsically Disordered Proteins and Protein Regions

Cellular membranes are highly dynamic in shape. They can rapidly and precisely regulate their shape to perform various cellular functions. The protein’s ability to sense membrane curvature is essential in various biological events such as cell signaling and membrane trafficking. As they are bound, these curvature-sensing proteins may also change the local membrane shape by one or more curvature driving mechanisms. Established curvature-sensing/driving mechanisms rely on proteins with specific structural features such as amphipathic helices and intrinsically curved shapes. However, the recent discovery and characterization of many proteins have shattered the protein structure–function paradigm, believing that the protein functions require a unique structural feature. Typically, such structure-independent functions are carried either entirely by intrinsically disordered proteins or hybrid proteins containing disordered regions and structured domains. It is becoming more apparent that disordered proteins and regions can be potent sensors/inducers of membrane curvatures. In this article, we outline the basic features of disordered proteins and regions, the motifs in such proteins that encode the function, membrane remodeling by disordered proteins and regions, and assays that may be employed to investigate curvature sensing and generation by ordered/disordered proteins.

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor-associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA-binding of transcriptional factors, and inhibition of the protein-protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

Deep learning in prediction of intrinsic disorder in proteins

Intrinsic disorder prediction is an active area that has developed over 100 predictors. We identify and investigate a recent trend towards the development of deep neural network (DNN)-based methods. The first DNN-based method was released in 2013 and since 2019 deep learners account for majority of the new disorder predictors. We find that the 13 currently available DNN-based predictors are diverse in their topologies, sizes of their networks and the inputs that they utilize. We empirically show that the deep learners are statistically more accurate than other types of disorder predictors using the blind test dataset from the recent community assessment of intrinsic disorder predictions (CAID). We also identify several well-rounded DNN-based predictors that are accurate, fast and/or conveniently available. The popularity, favorable predictive performance and architectural flexibility suggest that deep networks are likely to fuel the development of future disordered predictors. Novel hybrid designs of deep networks could be used to adequately accommodate for diversity of types and flavors of intrinsic disorder. We also discuss scarcity of the DNN-based methods for the prediction of disordered binding regions and the need to develop more accurate methods for this prediction.

Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena

Molecular dynamics simulation is the most popular computational technique for investigating the structural and dynamical behaviour of proteins, in search of the molecular basis of their function. Far from being a completely settled field of research, simulations are still evolving to best capture the essential features of the atomic interactions that govern a protein's inner motions. Modern force fields are becoming increasingly accurate in providing a physical description adequate to this purpose, and allow us to model complex biological systems under fairly realistic conditions. Furthermore, the use of accelerated sampling techniques is improving our access to the observation of progressively larger molecular structures, longer time scales, and more hidden functional events. In this review, the basic principles of molecular dynamics simulations and a number of key applications in the area of protein science are summarized, and some of the most important results are discussed. Examples include the study of the structure, dynamics and binding properties of 'difficult' targets, such as intrinsically disordered proteins and membrane receptors, and the investigation of challenging phenomena like hydration-driven processes and protein aggregation. The findings described provide an overall picture of the current state of this research field, and indicate new perspectives on the road ahead to the upcoming future of molecular simulations.

A FRET-Based Biosensor for the Src N-Terminal Regulatory Element

In signaling proteins, intrinsically disordered regions often represent regulatory elements, which are sensitive to environmental effects, ligand binding, and post-translational modifications. The conformational space sampled by disordered regions can be affected by environmental stimuli and these changes trigger, vis a vis effector domain, downstream processes. The disordered nature of these regulatory elements enables signal integration and graded responses but prevents the application of classical approaches for drug screening based on the existence of a fixed three-dimensional structure. We have designed a genetically encodable biosensor for the N-terminal regulatory element of the c-Src kinase, the first discovered protooncogene and lead representative of the Src family of kinases. The biosensor is formed by two fluorescent proteins forming a FRET pair fused at the two extremes of a construct including the SH4, unique and SH3 domains of Src. An internal control is provided by an engineered proteolytic site allowing the generation of an identical mixture of the disconnected fluorophores. We show FRET variations induced by ligand binding. The biosensor has been used for a high-throughput screening of a library of 1669 compounds with seven hits confirmed by NMR.

E3 ligases: a potential multi-drug target for different types of cancers and neurological disorders

Ubiquitylation is a posttranslational modification of proteins that is necessary for a variety of cellular processes. E1 ubiquitin activating enzyme, E2 ubiquitin conjugating enzyme, and E3 ubiquitin ligase are all involved in transferring ubiquitin to the target substrate to regulate cellular function. The objective of this review is to provide an overview of different aspects of E3 ubiquitin ligases that can lead to major biological system failure in several deadly diseases. The first part of this review covers the important characteristics of E3 ubiquitin ligases and their classification based on structural domains. Further, the authors provide some online resources that help researchers explore the data relevant to the enzyme. The following section delves into the involvement of E3 ubiquitin ligases in various diseases and biological processes, including different types of cancer and neurological disorders.

Selective 1 Hα NMR Methods Reveal Functionally Relevant Proline cis/trans Isomers in Intrinsically Disordered Proteins: Characterization of Minor Forms, Effects of Phosphorylation, and Occurrence in Proteome

It is important to identify proline cis/trans isomers that appear in several regulatory mechanisms of proteins, and to characterize minor species that are present due to the conformational heterogeneity in intrinsically disordered proteins (IDPs). To obtain residue level information on these mobile systems we introduce two 1 Hα -detected, proline selective, real-time homodecoupled NMR experiments and analyze the proline abundant transactivation domain of p53. The measurements are sensitive enough to identify minor conformers present in 4-15 % amounts; moreover, we show the consequences of CK2 phosphorylation on the cis/trans-proline equilibrium. Using our results and available literature data we perform a statistical analysis on how the amino acid type effects the cis/trans-proline distribution. The methods are applicable under physiological conditions, they can contribute to find key proline isomers in proteins, and statistical analysis results may help in amino acid sequence optimization for biotechnological purposes.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

In a flash of light: X-ray free electron lasers meet native mass spectrometry

During the last years, X-ray free electron lasers (XFELs) have emerged as X-ray sources of unparalleled brightness, delivering extreme amounts of photons in femtosecond pulses. As such, they have opened up completely new possibilities in drug discovery and structural biology, including studying high resolution biomolecular structures and their functioning in a time resolved manner, and diffractive imaging of single particles without the need for their crystallization. In this perspective, we briefly review the operation of XFELs, their immediate uses for drug discovery and focus on the potentially revolutionary single particle diffractive imaging technique and the challenges which remain to be overcome to fully realize its potential to provide high resolution structures without the need for crystallization, freezing or the need to keep proteins stable at extreme concentrations for long periods of time. As the issues have been to a large extent sample delivery related, we outline a way for native mass spectrometry to overcome these and enable so far impossible research with a potentially huge impact on structural biology and drug discovery, such as studying structures of transient intermediate species in viral life cycles or during functioning of molecular machines.

Therapeutics-how to treat phase separation-associated diseases

Liquid-liquid phase separation has drawn attention as many neurodegeneration or cancer-associated proteins are able to form liquid membraneless compartments (condensates) by liquid-liquid phase separation. Furthermore, there is rapidly growing evidence that disease-associated mutation or post-translational modification of these proteins causes aberrant location, composition or physical properties of the condensates. It is ambiguous whether aberrant condensates are always causative in disease mechanisms, however they are likely promising potential targets for therapeutics. The conceptual framework of liquid-liquid phase separation provides opportunities for novel therapeutic approaches. This review summarises how the extensive recent advances in understanding control of nucleation, growth and composition of condensates by protein post-translational modification has revealed many possibilities for intervention by conventional small molecule enzyme inhibitors. This includes the first proof-of-concept examples. However, understanding membraneless organelle formation as a physical chemistry process also highlights possible physicochemical mechanisms of intervention. There is huge demand for innovation in drug development, especially for challenging diseases of old age including neurodegeneration and cancer. The conceptual framework of liquid-liquid phase separation provides a new paradigm for thinking about modulating protein function and is very different from enzyme lock-and-key or structured binding site concepts and presents new opportunities for innovation.

flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions

Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn's webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/.

The diversity of molecular interactions involving intrinsically disordered proteins: A molecular modeling perspective

Intrinsically Disordered Proteins and Regions (IDPs/IDRs) are key components of a multitude of biological processes. Conformational malleability enables IDPs/IDRs to perform very specialized functions that cannot be accomplished by globular proteins. The functional role for most of these proteins is related to the recognition of other biomolecules to regulate biological processes or as a part of signaling pathways. Depending on the extent of disorder, the number of interacting sites and the type of partner, very different architectures for the resulting assemblies are possible. More recently, molecular condensates with liquid-like properties composed of multiple copies of IDPs and nucleic acids have been proven to regulate key processes in eukaryotic cells. The structural and kinetic details of disordered biomolecular complexes are difficult to unveil experimentally due to their inherent conformational heterogeneity. Computational approaches, alone or in combination with experimental data, have emerged as unavoidable tools to understand the functional mechanisms of this elusive type of assemblies. The level of description used, all-atom or coarse-grained, strongly depends on the size of the molecular systems and on the timescale of the investigated mechanism. In this mini-review, we describe the most relevant architectures found for molecular interactions involving IDPs/IDRs and the computational strategies applied for their investigation.

A Tale of Loops and Tails: The Role of Intrinsically Disordered Protein Regions in R-Loop Recognition and Phase Separation

R-loops are non-canonical, three-stranded nucleic acid structures composed of a DNA:RNA hybrid, a displaced single-stranded (ss)DNA, and a trailing ssRNA overhang. R-loops perform critical biological functions under both normal and disease conditions. To elucidate their cellular functions, we need to understand the mechanisms underlying R-loop formation, recognition, signaling, and resolution. Previous high-throughput screens identified multiple proteins that bind R-loops, with many of these proteins containing folded nucleic acid processing and binding domains that prevent (e.g., topoisomerases), resolve (e.g., helicases, nucleases), or recognize (e.g., KH, RRMs) R-loops. However, a significant number of these R-loop interacting Enzyme and Reader proteins also contain long stretches of intrinsically disordered regions (IDRs). The precise molecular and structural mechanisms by which the folded domains and IDRs synergize to recognize and process R-loops or modulate R-loop-mediated signaling have not been fully explored. While studying one such modular R-loop Reader, the Fragile X Protein (FMRP), we unexpectedly discovered that the C-terminal IDR (C-IDR) of FMRP is the predominant R-loop binding site, with the three N-terminal KH domains recognizing the trailing ssRNA overhang. Interestingly, the C-IDR of FMRP has recently been shown to undergo spontaneous Liquid-Liquid Phase Separation (LLPS) assembly by itself or in complex with another non-canonical nucleic acid structure, RNA G-quadruplex. Furthermore, we have recently shown that FMRP can suppress persistent R-loops that form during transcription, a process that is also enhanced by LLPS via the assembly of membraneless transcription factories. These exciting findings prompted us to explore the role of IDRs in R-loop processing and signaling proteins through a comprehensive bioinformatics and computational biology study. Here, we evaluated IDR prevalence, sequence composition and LLPS propensity for the known R-loop interactome. We observed that, like FMRP, the majority of the R-loop interactome, especially Readers, contains long IDRs that are highly enriched in low complexity sequences with biased amino acid composition, suggesting that these IDRs could directly interact with R-loops, rather than being “mere flexible linkers” connecting the “functional folded enzyme or binding domains”. Furthermore, our analysis shows that several proteins in the R-loop interactome are either predicted to or have been experimentally demonstrated to undergo LLPS or are known to be associated with phase separated membraneless organelles. Thus, our overall results present a thought-provoking hypothesis that IDRs in the R-loop interactome can provide a functional link between R-loop recognition via direct binding and downstream signaling through the assembly of LLPS-mediated membrane-less R-loop foci. The absence or dysregulation of the function of IDR-enriched R-loop interactors can potentially lead to severe genomic defects, such as the widespread R-loop-mediated DNA double strand breaks that we recently observed in Fragile X patient-derived cells.

Disaggregation mechanism of prion amyloid for tweezer inhibitor

The aggregation of amyloid has been an important event in the pathology of amyloidogenicity. A number of small molecules have been designed for Amyloidosis treatment. Molecular tweezer CLR01, a potential drug for misfolded β-amyloids inhibition, was reportedly bind directly to Lysine residues and interrupt oligomerization. However, the disaggregation mechanism of amyloid for this inhibitor is unclear. Here we used long timescale of molecular dynamic simulation to reveal the mechanism of disaggregation for pentamer prion amyloid. Molecular docking and molecular dynamics simulation demonstrate that CLR01 is attached with Lysine₂₂₂ nitrogen by π-cation interaction of its nine aromatic rings and formation of salt bridge/hydrogen bond of one of the two rotatable peripheral anionic phosphate groups. Upon CLR01 binding, we found a major shifting occurs in initial conformation of the oligomer and stretch out the N-terminal chain A from the rest of the amyloid which seems to be the first stage of disaggregated the fibrils slowly yet efficiently. Moreover, the CLR01 remodelled the pentamer Prion_220-272 into a compact structure which might be the resistant conformation for further oligomerization. Our work will contribute to better understand the interaction and deterioration mechanism of molecular tweezer for prions and similar amyloids, and offer significant insights into therapeutic development for Amyloidosis treatment.

Paxillin family of focal adhesion adaptor proteins and regulation of cancer cell invasion

The paxillin family of proteins, including paxillin, Hic-5, and leupaxin, are focal adhesion adaptor/scaffolding proteins which localize to cell-matrix adhesions and are important in cell adhesion and migration of both normal and cancer cells. Historically, the role of these proteins in regulating the actin cytoskeleton through focal adhesion-mediated signaling has been well documented. However, studies in recent years have revealed additional functions in modulating the microtubule and intermediate filament cytoskeletons to affect diverse processes including cell polarization, vesicle trafficking and mechanosignaling. Expression of paxillin family proteins in stromal cells is also important in regulating tumor cell migration and invasion through non-cell autonomous effects on the extracellular matrix. Both paxillin and Hic-5 can also influence gene expression through a variety of mechanisms, while their own expression is frequently dysregulated in various cancers. Accordingly, these proteins may serve as valuable targets for novel diagnostic and treatment approaches in cancer.

Targeting Intrinsically Disordered Proteins through Dynamic Interactions

Intrinsically disordered proteins (IDPs) are over-represented in major disease pathways and have attracted significant interest in understanding if and how they may be targeted using small molecules for therapeutic purposes. While most existing studies have focused on extending the traditional structure-centric drug design strategies and emphasized exploring pre-existing structure features of IDPs for specific binding, several examples have also emerged to suggest that small molecules could achieve specificity in binding IDPs and affect their function through dynamic and transient interactions. These dynamic interactions can modulate the disordered conformational ensemble and often lead to modest compaction to shield functionally important interaction sites. Much work remains to be done on further elucidation of the molecular basis of the dynamic small molecule-IDP interaction and determining how it can be exploited for targeting IDPs in practice. These efforts will rely critically on an integrated experimental and computational framework for disordered protein ensemble characterization. In particular, exciting advances have been made in recent years in enhanced sampling techniques, Graphic Processing Unit (GPU)-computing, and protein force field optimization, which have now allowed rigorous physics-based atomistic simulations to generate reliable structure ensembles for nontrivial IDPs of modest sizes. Such de novo atomistic simulations will play crucial roles in exploring the exciting opportunity of targeting IDPs through dynamic interactions.

Discovery of drugs that directly target the intrinsically disordered region of the androgen receptor

Introduction: Intrinsically disordered proteins (IDPs) and regions (IDRs) lack stable three-dimensional structure making drug discovery challenging. A validated therapeutic target for diseases such as prostate cancer is the androgen receptor (AR) which has a disordered amino-terminal domain (NTD) that contains all of its transcriptional activity. Drug discovery against the AR-NTD is of intense interest as a potential treatment for disease such as advanced prostate cancer that is driven by truncated constitutively active splice variants of AR that lack the C-terminal ligand-binding domain (LBD).Areas covered: This article presents an overview of the relevance of AR and its intrinsically disordered NTD as a drug target. AR structure and approaches to blocking AR transcriptional activity are discussed. The discovery of small molecules, including the libraries used, proven binders to the AR-NTD, and site of interaction of these small molecules in the AR-NTD are presented along with discussion of the Phase I clinical trial.Expert opinion: The lack of drugs in the clinic that directly bind IDPs/IDRs reflects the difficulty of targeting these proteins and obtaining specificity. However, it may also point to an inappropriateness of too closely borrowing concepts and resources from drug discovery to folded proteins.

Presence and structure-activity relationship of intrinsically disordered regions across mucins

Many members of the mucin family are evolutionarily conserved and are often aberrantly expressed and glycosylated in various benign and malignant pathologies leading to tumor invasion, metastasis, and immune evasion. The large size and extensive glycosylation present challenges to study the mucin structure using traditional methods, including crystallography. We offer the hypothesis that the functional versatility of mucins may be attributed to the presence of intrinsically disordered regions (IDRs) that provide dynamism and flexibility and that the IDRs offer potential therapeutic targets. Herein, we examined the links between the mucin structure and function based on IDRs, posttranslational modifications (PTMs), and potential impact on their interactome. Using sequence-based bioinformatics tools, we observed that mucins are predicted to be moderately (20%-40%) to highly (>40%) disordered and many conserved mucin domains could be disordered. Phosphorylation sites overlap with IDRs throughout the mucin sequences. Additionally, the majority of predicted O- and N- glycosylation sites in the tandem repeat regions occur within IDRs and these IDRs contain a large number of functional motifs, that is, molecular recognition features (MoRFs), which directly influence protein-protein interactions (PPIs). This investigation provides a novel perspective and offers an insight into the complexity and dynamic nature of mucins.

Ameliorating amyloid aggregation through osmolytes as a probable therapeutic molecule against Alzheimer's disease and type 2 diabetes

Large numbers of neurological and metabolic disorders occurring in humans are induced by the aberrant growth of aggregated or misfolded proteins.

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Accuracy of protein-level disorder predictions

Experimental annotations of intrinsic disorder are available for 0.1% of 147 000 000 of currently sequenced proteins. Over 60 sequence-based disorder predictors were developed to help bridge this gap. Current benchmarks of these methods assess predictive performance on datasets of proteins; however, predictions are often interpreted for individual proteins. We demonstrate that the protein-level predictive performance varies substantially from the dataset-level benchmarks. Thus, we perform first-of-its-kind protein-level assessment for 13 popular disorder predictors using 6200 disorder-annotated proteins. We show that the protein-level distributions are substantially skewed toward high predictive quality while having long tails of poor predictions. Consequently, between 57% and 75% proteins secure higher predictive performance than the currently used dataset-level assessment suggests, but as many as 30% of proteins that are located in the long tails suffer low predictive performance. These proteins typically have relatively high amounts of disorder, in contrast to the mostly structured proteins that are predicted accurately by all 13 methods. Interestingly, each predictor provides the most accurate results for some number of proteins, while the best-performing at the dataset-level method is in fact the best for only about 30% of proteins. Moreover, the majority of proteins are predicted more accurately than the dataset-level performance of the most accurate tool by at least four disorder predictors. While these results suggests that disorder predictors outperform their current benchmark performance for the majority of proteins and that they complement each other, novel tools that accurately identify the hard-to-predict proteins and that make accurate predictions for these proteins are needed.